Monitoring mouth activities

Abstract

A wearable device, and method of use, for detecting jaw activity using a sensor which monitors an area of skin whose movement is correlated with a facial muscle associated with movement of the jaw. The sensor captures two-dimensional time dependent data describing movement of the area of skin and a processor 350 detects and processes cyclic movement. A machine learning component may compare this data to threshold levels and / or environmental considerations in order to identify the mouth activity, for example, chewing and mouth opening / closing. Optical flow sensors 201 may be used, and a motion sensor(s) 204, accelerometer, gyroscope, microphone and / or camera 205 may also be incorporated into the device to collect additional information about the user’s activity, such as head tilting, swallowing, bruxism, applied tongue pressure or lip movement. This device could be utilised to use gesture control to operate an external computer 380 or device. The wearable device may be spectacles 200 with sensors on the arms bridge and lower lens frames, or it may be a hat, headband, headphones or earpiece style device.

Description

FIELD OF THE INVENTION This invention relates to monitoring mouth activities such as chewing. BACKGROUND OF THE INVENTION Mouth movements are fundamental to life. From childhood, sucking, eating, drinking, kissing, speaking all involve coordination of facial muscles and more specifically jaw muscles. In addition to consumption of food, drink and drugs, jaw muscles as well as other facial muscles are also involved in expressions of emotional effort, stress or cognitive load. It is known that eating too quickly and eating too frequently can be linked to being overweight or obese. Many people are not aware of the speed at which they consume food, or how often they eat during the day. Keeping track of these activities can help a person to eat more mindfully and lose weight. For example, eating slowly is thought to lead to smaller bites and longer chewing, promoting better satiety through prolonged exposure to food. Rapid eating, on the other hand, is associated with faster gastric emptying and reduced response to satiety-related hormones. It can also be important to monitor food intake for reasons other than weight loss. For example, certain medications may require a person to not eat at certain times. There are various difficulties with accurately monitoring oral intake and the dietary behaviors of a person. Commonly used methods require the person to manually log their meals and snacks in a smartphone app or diary. These methods are inaccurate as they rely on the user remembering to log their food and logging it correctly. It is common for users to under-report what they have consumed. Likewise, monitoring the eating habits of someone experiencing cognitive decline is especially important as malnutrition is commoner in the group affected by neurodegenerative diseases. Monitoring the weight of a person over time is also an ineffective way of preventing unwanted weight changes because weight is a delayed measure of energy intake. In addition, the weight of a person fluctuates daily and can therefore provide seemingly false results to someone trying to achieve long-term weight goals. Monitoring the ingestion of medications or supplements is a similar challenge. People are forgetful, so the common tools of using alarms or diaries are unreliable or inconvenient for those trying to maintain a regular consumption schedule. This is particularly the case with those who are elderly or unwell. Measuring the effects of a drug or supplement that has not been taken is wasteful. Likewise, measuring the consequences of taking a drug or food may not correspond with the peak circulating levels. Knowing the timing and quantity of the ingested material would facilitate metabolic predictions. Emerging technologies such as wearable sensors offer the potential to improve eating assessment methods by passively measuring eating activity with minimal user interaction. Recent advances in wearable sensors for eating detection are disclosed in “Automatic, wearable-based, in-field eating detection approaches for public health research: a scoping review by NPJ Digit Med. 2020; 3: 38. Published online 2020 Mar 13. doi: 10.1038 / S41746-020-0246-2. Currently, sensors use a variety of modalities, such as accelerometers, gyroscopes, magnetometers, and cameras to detect eating-related activities, such as hand-to-mouth gestures, chewing, and swallowing. The accuracy of these sensors in naturalistic settings is uncertain. There are technical, analytical, and multidisciplinary challenges of conducting in-the-wild measurements with wearable sensors. These challenges include sensor noise, sensor occlusion (in the case of cameras), privacy concerns and the need to develop algorithms that can accurately distinguish between eating and other activities. US Patent No. 10,736,566 describes a system for monitoring food intake using an air pressure sensor for detecting ear canal deformation resulting from mandible movement. Using an ear canal sensor can be uncomfortable for users and may pick up on jaw movements that are not as a result of eating. US ‘566 also discloses using a temple sensor to monitor movement of the temple in a direction out of the skin due to chewing. Such a sensor is not able to accurately distinguish between different mouth activities. US Patent No. 10, 809, 796 describes a system for monitoring a user of a head-wearable electronic device with multiple light-sensing assemblies such as proximity sensors. Such a device is not able to accurately distinguish between different mouth activities. SUMMARY OF THE INVENTION According to a first aspect of the present invention there is provided a system for detecting a jaw activity of a user, the system comprising: wearable apparatus comprising a sensor located so as to, in use when the apparatus is worn by a user, monitor an area of skin whose movement is correlated with a facial muscle associated with movement of the jaw, the sensor being configured to capture time-varying data describing movement of the area of skin in two dimensions in a plane defined by the area of skin; and a processor configured to process the time-varying data by detecting a cyclic movement of the area of skin so as to identify a jaw activity of the user. The processor may be configured to process the time-varying data by detecting a cyclic movement of the area of skin in the two dimensions so as to identify a jaw activity of the user. The jaw activity may be one or more of: mouth opening and closing, a jaw clench, chewing, movement of the tongue against a part of the mouth, or bruxism. The area of skin may overly one or more of: a masticator muscle, a zygomaticus major and / or minor muscle, a levator labii superioris muscle, a levator labii superioris alaeque nasi muscle, a levator anguli oris muscle, a risorius muscle, a buccinator muscle, an orbicularis oris muscle, a depressor anguli oris muscle, a depressor labii inferioris muscle, and a mentalis muscle. The facial muscle may be a masticator muscle. The masticator muscle may be one or more of: a temporalis muscle, a masseter muscle, a medial pterygoid, and a lateral pterygoid. The processor may be operable to detect a plurality of different cyclic movements of the area of skin so as to identify a plurality of different jaw activities. The cyclic movement may comprise an oscillation of the area of skin in each of two directions in the plane of the skin. The oscillations may be concurrent. The apparatus may comprise a plurality of sensors. Each sensor of the plurality of sensors may be located so as to, in use when the apparatus is worn by the user, monitor a respective area of skin whose movement is correlated with a respective facial muscle associated with movement of the jaw. Each sensor in the plurality of sensors may be configured to capture time-varying data describing movement of the respective area of skin in two dimensions in a plane defined by that area of skin. The processor may be configured to process the time-varying data from each sensor in the plurality of sensors by detecting a respective cyclic movement of the respective area of skin and comparing the cyclic movements so as to identify the jaw activity. The processor may configured to identify the jaw activity based on a phase difference between the time-varying data of each sensor in the plurality of sensors. The plurality of sensors may comprise a pair of sensors. Each sensor in the pair of sensors may be located so as to, in use when the apparatus is worn by the user, monitor a respective area of skin whose movement is correlated with a corresponding muscle on either side of the face of the user. The processor may be configured to determine a measure of asymmetry in the jaw activity of the user in dependence on the comparison of the cyclic movements. The processor may be configured to identify the jaw activity based on the measure of asymmetry. At least one of the plurality of sensors may be located so as to, in use when the apparatus is worn by the user, monitor an area of skin associated with a masticator muscle of the user and at least one of the plurality of sensors may be located so as to, in use when the apparatus is worn by the user, monitor an area of skin associated with one or more of: a zygomaticus major and / or minor muscle, a levator labii superioris muscle, a levator labii superioris alaeque nasi muscle, a levator anguli oris muscle, a risorius muscle, a buccinator muscle, a depressor anguli oris muscle, a depressor labii inferioris muscle, an orbicularis oris muscle or a mentalis muscle. The apparatus may further comprise a motion sensor and / or microphone. The processor may be configured to use data captured by the motion sensor and / or microphone in combination with the timevarying data from the sensor to identify the jaw activity of the user. The motion sensor may be configured to detect a head tilt of the user when the wearable apparatus is worn on the head of the user. The processor may be configured to infer ingestion of a pill based on a comparison between the time-varying data from the sensor relating to the jaw activity, and data from the motion sensor relating to the head tilt. The sensor may be an optical flow sensor configured to capture samples of the area of skin and compare the samples apart in time so as to detect movement of the area of skin relative to the optical sensor. The processor may be configured to identify the jaw activity based on one or more characteristics of the cyclic movement. The processor may be configured to identify the jaw activity in dependence on one or more characteristics of an immediately preceding cyclic movement and / or an immediately following cyclic movement to the cyclic movement. The processor may be configured to identify the jaw activity in response to detecting a plurality of cyclic movements. The one or more characteristics of the cyclic movement may comprise one or more of: a maximum amplitude of the cyclic movement in either or both of the two dimensions, an average velocity of the skin movement, a maximum velocity of the skin movement, a rate of change of velocity of the skin movement, an orientation of the cyclic movement, and a duration of the cyclic movement. The one or more characteristics may comprise one or more of: an average frequency of the plurality of cyclic movements, a frequency variability of the plurality of cyclic movements, a change in maximum amplitude of the plurality of cyclic movements overtime. The jaw activity may be mouth opening and closing. The processor may be configured to identify mouth opening in response to detecting a cyclic movement having a maximum amplitude that exceeds a predefined threshold. The jaw activity may be chewing. The processor may be configured to identify chewing in response to detecting repeated cyclic movements of the area of skin. The processor may be configured to infer swallowing in response to detecting an interruption in the repeated cyclic movements for a predetermined interval of time. The processor may be configured to infer swallowing in response to detecting a reduction in a maximum amplitude of the repeated cyclic movements preceding the detected interruption. When the jaw activity is pressing the tongue against a part of the mouth, the processor may be configured to infer which part of the mouth is being pressed by the tongue The processor may be configured to identify the jaw activity without using data from a sensor configured to sense motion of the area of skin in a direction substantially out of the plane defined by the area of skin. The processor may be configured to process the time-varying data using a machine learning model trained to identify one or more cyclic movements in the time-varying data and attribute them to a jaw activity. The processor may be configured to process the time-varying data at a neural network configured to operate on time-varying data describing movement of an area of skin so as to detect the cyclic movement and classify the cyclic movement as corresponding to the jaw activity. The apparatus may comprise a lip sensor located so as to, in use when the apparatus is worn by the user, monitor an area of skin associated with a lip dilator and / or lip constrictor, the processor being configured to identify the jaw activity based on a comparison of data from the lip sensor with the timevarying data from the sensor. The processor may be configured to predict when a future jaw activity will occur based on one or more of: time of day, time elapsed since most recent jaw activity, time of a previously identified jaw activity, type of previously identified jaw activities, a history of jaw activities of the user, and a user input. The processor may be configured to control a sampling rate of one or more sensors in the wearable apparatus in dependence on the prediction of the future jaw activity. The future jaw activity may be an eating episode. According to a first aspect of the present invention there is provided a method for detecting a jaw activity of a user, the method comprising: capturing time-varying data describing movement of an area of skin in two dimensions in a plane defined by the area of skin, the movement of the area of skin being correlated with a facial muscle associated with movement of the jaw, said capturing being performed using a sensor located so as to monitor said area of skin; and processing the time-varying data by detecting a cyclic movement of the area of skin so as to identify a jaw activity of the user. The processing may be performed using a machine learning algorithm that takes as an input the timevarying data describing movement of the area of skin and produces as an output a signal identifying the jaw activity. The sensor may be located on a wearable apparatus so as to, in use when the apparatus is worn by the user, monitor the area of skin. There is provided a method comprising any of the functional steps performed by the apparatus described herein. According to another aspect, there is provided a system for monitoring tongue activity of a user, the system comprising: wearable apparatus comprising a sensor located so as to, in use when the apparatus is worn by a user, monitor an area of skin associated with the temporalis muscle of the user, the sensor being configured to capture time-varying data describing movement of the area of skin in two dimensions in a plane defined by the area of skin; and a processor configured to process the time-varying data so as to identify a tongue activity of the user. A tongue activity may involve the user pressing their tongue against a respective part of the mouth. A tongue activity may be one or more of: pressing the tongue against the roof of the mouth, pressing the tongue against the upper or lower left or right side of teeth, pressing the tongue against the lower front teeth, pressing the tongue against the upper front teeth, pressing the tongue against the hard palate, pressing the tongue against the soft palate, and pressing the tongue against the floor of the mouth. The processor may be configured to identify the tongue activity by detecting movement of an area of skin corresponding to activation of the anterior part of the temporalis muscle. The sensor may be one sensor of a pair of sensors at the wearable apparatus which are , in use when the apparatus is worn by the user, located either side of the face of the user so as to monitor respective areas of skin each associated with a respective temporalis muscle on one side of the face of the user. The processor may be configured to compare time-varying data captured by each sensor in the pair of sensors to determine a measure of asymmetry in temporalis muscle activation on either side of the user’s face. The processor may be configured to infer the location of the tongue press within the mouth based on the measure of asymmetry. The measure of asymmetry may be a measure of the relative strength of the muscle activations of the temporalis on each side of the face. The sensor may be configured to capture time-varying data describing movement of each of a plurality of sections of the area of skin. The processor may be configured to process the time-varying data by comparing the time-varying data describing movement of each of the plurality of sections to determine relative movement of each of the plurality of sections with respect to one another, and / or gross 6 movement of each of the plurality of sections with respect to the wearable apparatus. The processor may be configured to identify the tongue activity using a machine learning model trained to attribute relative movement of at least one section of the plurality of sections of the area of skin with a predetermined tongue activity. According to another aspect there is provided a system for detecting a mouth activity of a user, the system comprising: wearable apparatus comprising a sensor located so as to, in use when the apparatus is worn by a user, monitor an area of skin overlying the temporalis muscle, the sensor being configured to capture time-varying data describing movement of the area of skin in two dimensions in a plane defined by the area of skin; and a processor configured to process the time-varying data so as to identify a mouth activity of the user. According to another aspect there is provided a method for monitoring a mouth activity of a user wearing a wearable apparatus, the wearable apparatus comprising a plurality of sensors including a motion sensor and at least one sensor located so as to, in use when the apparatus is worn, monitor an area of skin whose movement is correlated with a facial muscle associated with movement of the jaw, the method comprising: capturing, using the motion sensor, data describing movement of the wearable apparatus, inputting the data from the motion sensor into a machine learning model to identify a potential mouth activity, in response to identifying a potential mouth activity, increasing the sampling rate of the at least one sensor, capturing, using said at least one sensor, data describing movement of the area of skin using the at least one sensor, and processing said data at a processor so as to identify a mouth activity, in response to no mouth activity being identified for a predetermined period, reducing the sampling rate of at least one of the plurality of sensors. BRIEF DESCRIPTION OF THE DRAWINGS The present invention will now be described by way of example with reference to the accompanying drawings. In the drawings: Figures 1a and 1b illustrate possible skin locations sampled by wearable apparatus shown in figure 2 and the locations of some facial muscles of interest. Figure 2 shows a pair of glasses as an example of the wearable apparatus. Figure 3 shows a schematic diagram of a system for detecting mouth activity. Figure 4 shows an example method for identifying a jaw activity. Figure 5 shows an example of steps that may occur to identify an eating episode. Figures 6a to 6d show an example of time-varying data captured by a temple sensor describing movement of an area of skin associated with the temporalis muscle in a plane defined by the area of skin during the beginning of an eating episode. Figures 7a to 7d show an example of time-varying data captured by a cheek sensor describing movement of an area of skin associated with the zygomaticus muscle in a plane defined by the area of skin during the beginning of an eating episode. Figure 8 shows an example of time-varying data captured by a temple sensor when the user is clenching their jaw. Figure 9 shows an example of time-varying data captured by a temple sensor when the user presses their tongue against the roof of the mouth. Figure 10 depicts an example method for monitoring a mouth activity. DETAILED DESCRIPTION OF THE INVENTION The following description is presented by way of example to enable a person skilled in the art to make and use the invention. The present invention is not limited to the embodiments described herein and various modifications to the disclosed embodiments will be apparent to those skilled in the art. It is desirable to detect, identify, and monitor mouth activities of a user accurately and automatically using a minimally invasive wearable device. In particular, it is desirable to detect, identify, and monitor jaw activities of a user. It is further desirable to detect, identify, and monitor eating activities of a user and provide feedback. Furthermore, it is desirable to use additional device data to understand the context of the user behaviour such as location, pose, posture, and physical activity. It is also desirable to detect the relationship between emotional responses and mouth activities of a user such as jaw clenching. A system for detecting a mouth activity of a user is described herein. More specifically, the system may be a system for detecting a jaw activity of a user. In particular, the system may be a system for detecting an eating activity of a user. Whilst the system may advantageously be used to monitor eating activities such as for weight loss applications, the system may also be used to detect bruxism (repeated clenching of the jaw), determine asymmetry in jaw motion, determine limitations in the range of jaw motion, and detect other mouth activities such as pushing the tongue against the teeth. Detection of mouth activities such as these may be important in identifying temporomandibular joint problems or looking for early warning signs of certain conditions. Identification of mouth activities can also be combined with information from other sensors that may be incorporated into a wearable device, for example regarding activation of other facial muscles that allow a contextual analysis of the mouth activity. Sensors worn on the head can be used to infer the location (e.g. using GPS), pose (e.g. sitting, standing, lying down), posture (e.g. the tilt of the head), emotional response and physical activity of the user. In combination with data from these sensors, a wearable device can provide more detailed understanding of eating behaviour, pathological jaw function / pain, or emotional, cognitive or physical strain. For example, some people clench their teeth, particularly when experiencing emotional or cognitive or physical strain. The system described herein can be used to infer the context of jaw clenching which may provide insights into the emotional state of the user. In some cases, mouth activities may also be used to control a computer or Al, e.g. for hands-free control of a device. For example, jaw motions can be used to indicate that the user wants to engage with a particular device. A pre-configured gesture could be used to "handshake" with the device and initiate further interactions. For example, the user may clench their jaw three times consecutively to activate an Al assistant. Head orientation and user location could be used to determine what the user is looking at. When the jaw motion is considered in the context of location data and head orientation, and potentially also a camera incorporated into the wearable apparatus, the Al assistant may be able to infer what actions the user wishes to take with the Al. For example, a user wanting to know the time might look down (in the same way that they would look at a wristwatch) and clench their jaw. This could tell the Al that the user wanted the time which could be given by audio or vision means. In another example, when navigating outdoors, the user might want to request navigation advice. When the user gets to a junction (which may be determined by GPS on the wearable apparatus) and is unsure on where to go, they might look left and right and clench the jaw to summon directions from an Al assistant. Such applications will be described in more detail later. Herein, a “mouth activity” refers to the activation of muscles associated with the mouth. In other words, a mouth activity is the activation of a muscle that can cause movement within the interior of the mouth, and / or movement of an exterior part of the mouth, and / or movement of the skin immediately surrounding the mouth. Mouth activities may include one or more of: opening the mouth, closing the mouth, mastication (chewing), drinking, swallowing, spitting, licking, speaking, singing, breathing, smiling, kissing, whistling, yawning, sneezing, coughing, blowing, movement ofthe tongue within the mouth (e.g. against the teeth), side-to-side jaw movement, front-to-back jaw movement, a jaw clench, and bruxism (repeated clenching ofthe jaw). Facial muscles associated with the mouth may include one or more ofthe muscles of mastication (the masseter, the temporalis, the medial pterygoid, and the lateral pterygoid), the tongue, the cheek muscles (the zygomaticus major and minor, the levator labii superioris, the levator labii superioris alaeque nasi, the levator anguli oris, buccinator), the risorius, the depressor anguli oris, the depressor labii inferioris, the orbicularis oris, and the mentalis. A “jaw activity” is a type of mouth activity that involves the activation of muscles associated with the jaw (i.e. jaw muscles). Jaw activity may include one or more of opening the mouth, closing the mouth, chewing, side-to-side jaw movement, front-to-back jaw movement, movement of the tongue against a part of the mouth, a jaw clench, and bruxism. Herein, references to mouth activities may include jaw activities. The muscles of mastication are muscles associated with the jaw. They control movement of the lower jaw (the mandible). The muscles of mastication may include the masseter, the temporalis, the medial pterygoid, and the lateral pterygoid. Each of these muscles may be called a masticator muscle herein. The activation of the jaw using any of the mastication muscles is a mouth activity, and more specifically, a jaw activity. Mastication (chewing) is a jaw activity because it involves the activation of the masticator muscles. Bruxism is a jaw activity because it involves activation of the temporalis muscle. Pressing the tongue against different parts of the mouth such as the front teeth or anterior palate may be considered a jaw activity because it involves (at least) the activation of one or more jaw muscles such as the masseter and / or temporalis. A “consuming” activity is a type of mouth activity. A consuming activity occurs when the user is consuming a substance such as food and / or drink and / or medication. Consuming activities may include one or more of opening the mouth, closing the mouth, chewing, sucking, swallowing, and more generally eating and drinking. An “eating activity” is a type of mouth activity, and, more specifically, a type of “consuming activity”. Eating activities may include one or more of opening the mouth, closing the mouth, chewing, and swallowing. Whilst the system described herein is predominantly described in the context of detecting jaw activities, it may be used for detecting mouth activities more generally such as those listed above. The inventors have recognized that mouth activities such as chewing can be accurately detected by measuring lateral movement of an area of skin that moves when the mouth moves. By measuring lateral movement of the skin (e.g. movement in the plane of the skin), rather than movement out of the plane of the skin, it can be possible to differentiate between different kinds of mouth activities, as well as determine metrics relating to these activities. In particular, the inventors have recognized that jaw activity can be identified by detecting one or more cyclic movements of the area of the skin in the plane of the skin. Features of the cyclic movement(s) can be used to identify the jaw activity. Figure 1a illustrates some of the muscles associated with the jaw and some example areas of skin that may be monitored by the system described herein. Not all the mouth muscles listed above have been labelled on figure 1a for clarity. Figure 1b shows the masticator muscles during a side-to-side grinding motion whilst chewing. One of the muscles associated with the jaw is the temporalis muscle 1. The temporalis muscle is near the temple and extends downwards in a direction towards the mouth. There is a temporalis muscle on each side of the face. The temporalis muscle controls movement of the lower jaw that can result in the opening and closing of the mouth. The cheek, muscles such as zygomaticus major and minor are generally indicated at 2. Herein, references to the zygomaticus muscle mean either or both the zygomaticus major and minor. Also labelled at the risorius 7 and the buccinator 8. Figure 1 b depicts the other muscles of mastication muscles: the masseter 3, medial pterygoid 4 and the lateral pterygoid 5. Also shown in figure 1b is the temporomandibular joint 6. To detect a jaw activity, an area of skin whose movement can be correlated with the activity of a facial muscle associated with jaw movement is monitored. In other words, there is a relationship between movement of the area of skin being monitored and the activation of a facial muscle associated with movement of the jaw. For example, the area of skin being monitored may be correlated with (e.g. associated with) the masticator muscles. The area of skin being monitored need not overly a facial muscle directly associated with movement of the jaw. For example, the area of skin may overly a different facial muscle to one that is associated with movement of the jaw, provided that the movement of the area of skin can still be correlated with a facial muscle associated with movement of the jaw. For example, in figure 1a, the area of skin 22 may be monitored to detect jaw activity, even though it overlies the zygomaticus muscle 2 and not a masticator muscle. This is because the activation of the muscles associated with the jaw can cause movement of the area of skin 22 overlying the zygomaticus muscle. As another example, the area of skin being monitored 33 may be by the hinge of the jaw below the ear, as depicted in figure 1a. Whilst this area of skin is not directly overlying any facial muscle, activation of the muscles associated with the jaw can cause movement of this area of skin. In other examples, the area of skin may overly a facial muscle directly associated with movement of the jaw. For example, the area of skin 11 overlying the temporalis muscle 1 may be monitored. Figure 2 shows an example wearable apparatus 200 that is a part of a system 300 for determining a mouth activity of a user. In the example shown in figure 2, the wearable apparatus is a pair of glasses. The apparatus may alternatively be a headset, headband, hat, earphone, earpiece or any other wearable device or article that can act as a platform from which a sensor can monitor the relevant area of skin of the user. At least one sensor (e.g. a sensor 201) is positioned on the wearable apparatus. The sensor is located on the wearable apparatus so that when the wearable apparatus is worn by a user, the sensor can monitor an area of skin whose movement is correlated with a facial muscle associated with movement of the jaw, as described above. For example, one of the sensors 201 may monitor the area of skin 11 associated with temporalis muscle 1 when the glasses are worn by the user. As another example, one of the sensors 202 may monitor an area of skin 22 associated with the zygomaticus muscle 2. As described above, this is because movement of the area of skin 22 can be correlated with a facial muscle associated with jaw movement such as the temporalis muscle 11. In some examples, the area of skin may preferably overlie an insertion point of a facial muscle associated with movement of the jaw. By monitoring an area of skin overlying an insertion point of a facial muscle, a greater range of skin movement can be seen when that facial muscle is activated when compared with monitoring an area of skin positioned over the middle of the muscle. The area of skin being monitored may be in the range 0.1mm2 to 30mm2. The size of the area of skin monitored by the sensor may depend on the muscle or muscles associated with that area of skin, and / or the surface texture and shape of the skin in the area. The area of skin monitored by the sensor need not be circular or uniform in shape. For example, a sensor located so as to monitor an area of skin associated with the temporalis muscle may monitor a relatively large area of skin such as in the range 5mm2 to 20mm2. As the temporalis muscle acts over a relatively wide area, the sensor can monitor a relatively large area of skin without being in danger of picking up skin movement caused by a different muscle to the temporalis. In another example, a sensor located so as to monitor an area of skin associated with the levator anguli oris (near the edge of the lip) may monitor a relatively small area of skin in the range 0.1mm2 to 5mm2. This may avoid the sensor detecting movement of the area of skin that is caused by a different muscle to the levator anguli oris. By monitoring areas of skin that are associated with predominantly one muscle, data collected by the sensor can be more accurately attributed to a particular mouth activity or muscle activation. Alternatively, a sensor may monitor an area of skin on the face of the user that is associated with more than one muscle. For example, sensor 202 may monitor one area of skin that is associated with both the zygomaticus muscle and the orbicularis oris muscle (the muscle surrounding the lips). A sensor may monitor more than one area of skin associated with a respective muscle. For example, sensor 202 may monitor an area of skin associated with the zygomaticus muscle and separately an area of skin associated with the orbicularis oris muscle. As facial muscles tend to work together, it may be the case that one mouth activity will be caused by the movement of multiple related facial muscles. The mouth activity may be determined by monitoring one area of skin that is associated with multiple related facial muscles. It may not always be necessary to determine which muscle caused the movement of the area of skin as the ultimate goal may be only to determine the mouth activity, rather than specific muscle activity. The sensor (e.g. sensor 201) is configured to capture time-varying data describing movement of the area of skin in two dimensions in a plane defined by the area of skin. In other words, the sensor is configured to capture time-varying data describing lateral movement of the area of skin. That is, the direction of movement of the area of skin being measured by the sensor is along the surface of the skin. In some examples, this may be a direction transverse to the direction of data capture by the sensor. The time-varying data captured by the sensor may be one or more of: vector information (e.g. a coordinate or a pair of coordinates expressing a direction and magnitude of movement in the plane defined by the skin); a time series of images or other optical representations of the area of skin; or data in any form which captures time-varying contrast at the sampled area of skin (e.g. due to features on the skin, hairs, or its texture). For example, in figure 1a the area of skin 11 defines a plane in which a sensor (e.g. sensor 201) measures movement. The x-y axes in figure 1a depict two example directions in which movement may be measured in the plane 11. In this example, the area of skin 11 is shown as an x-y plane. In examples in which the sensor captures data along the z-axis, the x-y plane is transverse to the direction of data capture by the sensor. As the temple area is relatively flat, the area of skin monitored by the sensor may be relatively large so as to define a relatively large plane (e.g. 10mm2) in which movement may be measured. In examples in which the skin being monitored is particularly curved or bumpy, the area of skin may be smaller (e.g. 0.1mm2). In some examples, the data captured by the sensor may be substantially in the plane defined by the area the skin. In an example, the sensor is configured to capture time-varying data describing movement of the area of skin in two directions in the surface of the area of skin. It can be particularly advantageous to capture data describing movement of the area of skin in two directions (e.g. a first direction and a second direction) in the plane of the skin. The first and second directions do not need to be orthogonal, although they may be. In an example, the first and second directions may be labelled x and y directions. The x and y directions may be orthogonal. By capturing data in both the first and second directions in the plane of the skin, a richer source of information can be obtained and used to determine the user’s mouth activities, as will be explained below. In some examples, the sensor may be configured not to measure movement of the area of skin in a direction transverse to the plane defined by the area of skin. In other words, the sensor may be configured not to capture time-varying data describing movement of the area of skin in a direction out of the surface of the area of skin. In an example, the sensor is not a singular proximity sensor. The sensor may be any type of sensor that is adapted to capture time-varying data describing movement of the area of skin in two dimensions in a plane defined by the area of skin. In an example, the sensor may be an optical sensor. An optical sensor may be any sensor configured to use light (e.g. visible or infra-red light) to detect movement. The optical sensor may image the area of skin associated with a facial muscle of a user. In an example, the optical sensor is an optical flow sensor. An optical flow sensor is a sensor configured to capture (e.g. visually) samples of the area of skin and compare the captured samples apart in time so as to detect movement of the area of skin relative to the sensor. A surface sample captured by an optical flow sensor may be any kind of representation of the surface. This may be a representation of light modified by the surface such as a matrix, an interference pattern or speckle pattern created by the surface interacting with electromagnetic radiation such as a laser directed at the surface. Suitable optical flow algorithms may be used to track changes between samples which represent movement of the sampled surface. A suitable optical flow algorithm may be of the type used in computer mice to track relative movement of the mouse over a surface. The optical flow sensor outputs information representative of movement of the area of skin being monitored relative to the optical flow sensor - i.e., the “optical flow” of the skin movement. This may be in the form of a vector describing the magnitude and direction of the skin movement. For example, the sensor 201 shown in figure 3 is an optical flow sensor having a light sensor 206 and an optical flow processor 207 for processing the output of the light sensor so as to determine relative movement between the sensor 201 and the area of skin. The optical flow processor 207 provides an output representative of movement of the skin relative to the sensor. In an example, the optical flow processor 207 may be configured to capture lateral skin movement (i.e. skin movement in the plane of the skin) using suitable optical flow algorithms configured to track movement of a surface. The light sensor 206 may be configured to capture an array of pixeis, e.g. a 16x16 array, 18x18 or 30x30 array of pixels, to alternative examples, the light sensor 206 may comprise a single pixel and the optica! flow sensor may comprise a Sens or other optical element configured to randomise the light passing through it and onto the single pixel sensor (e.g. by means of a suitable translucent material or a randomising array of mirrors). The light sensor may be, for example, a Charge Coupled Device (CCD) or a Complementary Metal-Oxide-Semiconductor (CMOS) sensor. The optical flow processor 207 and the light sensor 206 may be provided as a stogie integrated circuit. Alternatively, the optical flow processor 207 may be separate to the light sensor 206 and may be external to the sensor 201 at the wearable apparatus. For example, the optical flow sensor may transmit data obtained from the light sensor 206 to the external processor 207 for determining relative movement. As another example of an optical sensor, the sensor may be an event-based detector. For example, the sensor may be a Metavision ® sensor produced by Prophesee ®. When the event-based detector monitors an area of skin, rather than capturing frames at a predefined frequency, the sensor only registers changes (e.g. “events”) in the surface of the skin as they occur. In this way, less redundant data is collected and / or transmitted because the sensor is only taking a measurement when it detects motion or a change in the area being monitored. Sampling can also occur at a much higher frequency than when using conventional frame-based optical sensors. As another example, the optical sensor may be a camera combined with a motion detection algorithm. As another example, the optical sensor may be a millimeter wave detector. In some examples, the sensor may be configured to make use of expected directions of movement of the area of skin being monitored and ignore motion in other unexpected directions. Due to the underlying anatomy of the human face, areas of skin on the face can be expected to move in certain directions, depending on the muscle(s) being activated. For example, when a person smiles, there are defined flow directions (e.g. directions of movement) forparts of the face such as the skin nearthe lips orthe cheeks. The sensor may ignore motion of the area of skin in directions that are not associated with the activation of the muscle(s) underling the area of skin being monitored. This processing may be performed at a processor external to the sensor, and / or it may be performed within the sensor. For example, the eventbased sensor described above may incorporate a filter that filters out or ignores motion in directions that are not expected for the area of skin being monitored by that sensor. Preferably, the wearable apparatus may comprise a plurality of sensors. In particular, the apparatus may comprise a plurality of sensors of the type described above. That is, the apparatus may comprise a plurality of sensors each located so as to, in use when the apparatus is worn by a user, monitor a respective area of skin whose movement is correlated with a facial muscle associated with movement of the jaw of the user. Each sensor of the plurality of sensors may be configured to capture time-varying data describing movement of the respective area of skin in two dimensions in a plane defined by that area of skin. For example, the apparatus in figure 2 comprises four sensors 201 each located so as to monitor an area of skin associated with a temporalis muscle of the user, and three sensors 202 each located so as to monitor an area of skin associated with other facial muscles of the user such as the zygomaticus muscle. Sensors 201 and 202 are of the type described above. Sensors 203, 204 and 205 are a different type of sensor that are not part of said plurality of sensors and will be discussed later. Of said plurality of sensors, two or more may monitor respective areas of skin that are each associated with the same facial muscle of the user. For example, figure 2 shows two sensors 201 on the right-hand side of the glasses 200 from the point of view of the wearer (i.e. the left-hand side in the view in figure 2). Each of the sensors 201 is located on the glasses so as to monitor an area of skin associated with the right temporalis muscle of the user when worn (from the point of view of the user). As the sensors 201 measure activity of the temporalis muscle in this example, they will be referred to herein as “temple sensors” to distinguish them from other sensors described later. Preferably, the wearable apparatus comprises two or more sensors located on the wearable apparatus so as to, in use when the apparatus is worn by the user, monitor respective corresponding areas of skin on opposite sides of the face of the user. Suitably, the two or more sensors may be located so as to monitor respective areas of skin that are each associated with a muscle of a muscle pair on either side of the user’s face. Most facial muscles exist on both sides (i.e. the left and right side) of the human face as part of a pair of muscles. For example, there is a respective temporalis muscle on each side of a human face, one at each temple. Of the plurality of sensors, two or more may be positioned so as to monitor the same muscle pair on either side of the user’s face. In figure 2, for example, there are two sets of temple sensors 201, one on each arm 210 of the glasses. The two sets of sensors 201 each measure the same muscle pair (in this case, the left and right temporalis muscles). Of the plurality of sensors, two or more may monitor respective areas of skin that are each associated with different facial muscles. For example, in figure 2, the wearable apparatus comprises temple sensors 201 for monitoring respective areas of skin associated with the temporalis muscles, and cheek sensors 202 for monitoring respective areas of skin associated with cheek muscles such as the zygomaticus muscles. More generally, the apparatus may comprise a first sensor for measuring movement of an area of skin associated with a masticator muscle (e.g. the temporalis muscle), and a second sensor for measuring movement of an area of skin associated with a different facial muscle (e.g. the zygomaticus muscle). In an example, a third sensor may be located on the wearable apparatus so as to monitor an area of skin associated with a lip dilator and / or lip constrictor. A lip dilator may be one or more of: the zygomaticus major and minor, levator labii, levator labii, superioris alaeque nasi, risorius, depressor anguli oris, depressor labii inferioris, and platysma. A lip constrictor may be one or more of: the orbicularis oris and the levator anguli oris. The sensors may detect a range of movement from 1pm to 10cm. In an example, a cheek sensor may measure skin movement of 5mm in a vertical direction (e.g. in the y-direction) relative to the user when the user opens their mouth wide. In an example, a temple sensor may measure skin movement of 0.7mm in the y-direction, and 1.5mm of skin movement in the x-direction in the plane of the skin when the user opens their mouth wide. The distance moved by the area of skin during a particular jaw activity will vary depending on the skin quality, age, and local anatomy. The apparatus may comprise othertypes of sensors which are not configured to capture data describing movement of the area of skin in a plane defined by the area of skin. For example, the apparatus may comprise ECG sensors. Figure 2 shows ECG sensors 203 located on the nosepiece of the glasses and on the temple tips of the arms of the glasses, where the glasses contact the skull. ECG sensors measure the electrical activity of the user’s heart using electrodes that contact the skin. In examples in which the wearable apparatus is not a pair of glasses, the ECG sensors may be located at any suitable position where contact with the user’s skin is likely. In an example in which the apparatus is an earpiece, the ECG sensors may be located so as to contact the outer ear of the user. As another example, the apparatus may comprise one or more motion sensors 204 such as an accelerometer or inertial measurement unit (IMU). An IMU may comprise an accelerometer and / or gyroscope and / or magnetometer, for example. The motion sensor measures movement of the wearable apparatus. Suitably, when the wearable apparatus is a pair of glasses, the motion sensor may be positioned towards the front of the glasses such as at the hinges or in the casing around the lenses. This may reduce the amount of damping of the movement of the glasses that may occur closer to the temple tips, where the glasses meet the ears of the user. Alternatively, the motion sensor may be positioned near the ear of the device to capture vibrations related to motion at the temporomandibular joints. Two or more motion sensors may be located at corresponding positions on either side of the apparatus so as to pick, up on any asymmetry in the motion of the apparatus. The motion sensor(s) may be configured to act as a microphone if sampled at a high enough frequency. For example, the motion sensor may be sampled at 4000Hz in order to pick up vibrations in the audio range. Otherwise, the motion sensor may sample at 50Hz or adaptively change sampling frequency based on context such as location, or detected activity inferred by motion analysis algorithms. The motion sensor(s) may comprise an accelerometer. The motion sensor(s) may be an accelerometer. An IMU may comprise one or more of said motions sensors. The motion sensor(s) may be an IMU. The apparatus may comprise a motion sensor and / or an IMU which may comprise a (separate) motion sensor. Sensor 204 may alternatively or additionally comprise a microphone. The apparatus may further comprise a camera 205. Figure 3 shows a system 300 for detecting mouth activities. The system 300 comprises the wearable apparatus 200 and a processor 350. As described above, the apparatus 200 comprises at least one sensor 201 configured to capture time-varying data describing movement of an area of skin in two dimensions in a plane defined by that area of skin. Data captured by the at least one sensor is output to the processor 350. The processor processes the time-varying data received from the sensor so as to identify a mouth activity of the user. The at least one sensor may comprise the plurality of sensors described above having the same data capturing capability as the at least one sensor 201. As described above, there may be other sensors within the apparatus that do not have the same data capturing capability as the at least one sensor 201, such as the ECG sensors 203 and motion sensor 204. The processor 350 also receives data captured by these sensors. The outputs from the sensor(s) may be combined at the processor 350. Generally, processor 350 may be configured to perform various types of processing to the outputs from the sensors such as multiplexing, encoding, encrypting and / or compressing the outputs from the sensors. The processor 350 may be a part of the wearable apparatus. For example, the processor 350 may be incorporated into an arm 210 of the glasses 200. In an example, processor 350 may be configured to transmit processed data to an external computer system 380 such as a smartphone for further analysis. For example, after identifying a mouth activity of the user, the processor may provide feedback to a user via a smartphone app. Dashed line 312 indicates a possible boundary between system 300 and an external computer system 380. Alternatively, the processor 350 may be external to the wearable apparatus, such as in the computer system 380. The computer system could be any kind of system having suitable processing capabilities, such as a personal computer, server, smartphone, tablet, smartwatch, or dedicated processing unit. An application running at the smartphone may provide suitable processing capabilities in order to identify a mouth activity from the sensor data. In the example in which the processor is external to the apparatus, the wearable apparatus may comprise a data transmitter 310 for transmitting data from the sensor(s) to the external processor, e.g. wirelessly according to a Bluetooth protocol. In the case where the processor is incorporated onto the apparatus, the data transmitter 310 may be a part of the processor. Figure 4 shows an example method for identifying a jaw activity of a user using the system 300 described herein. At step S100, data describing movement of the area of skin in a plane defined by the area of skin is captured by at least one sensor. Preferably, the data describing movement of the area of skin is in two directions in the plane of the area of skin. This allows more detailed information to be determined regarding the jaw activities of the user when compared with sampling data only in one direction. Figures 6a to 6d show an example of the data that may be captured at this step using a temple sensor 201 configured to capture skin movement in two dimensions in a plane defined by the area of skin being monitored. The graphs shown in figures 6a to 6d depict the same data but from different perspectives along the same axes. The data is outputted by the sensor and received by the processor 350. At step S110, the time-varying data is processed (e.g. at processor 350) in order to detect at least one cyclic movement of the area of skin in the plane of the skin - e.g. in the two directions (e.g. axes). Figures 6a to 6d show example time-varying data from a temple sensor 201 in which the data is exhibiting multiple cyclic movements. This is most noticeable when the time-varying data is plotted on a 3-D graph of position in each of the two directions against time. Each of the directions may be an axis defined, for example, for the purpose of processing the time-varying data captured by the sensor at the processor, and / or for the purpose of representing the time-varying data captured by the sensor monitoring the skin movement, and / or by the direction(s) of measurement of one or more of the sensors (e.g. the sensor may capture movement data along each of the axes). A cyclic movement may comprise the combination of an oscillating pattern over time in each of the two directions individually. For example, as can be seen in figure 6c, the x-direction data exhibits an oscillating pattern over time. Similarly, as can be seen in figure 6d, the y-direction data exhibits an oscillating pattern overtime. Figures 6a and 6b show the cyclic movements in the x and y direction that are formed from the combination of an oscillation in the x direction overtime and an oscillation in the y direction overtime. As can be seen in figures 6a to 6d, the rate of change of the skin movement in each of the x and y directions overtime is not constant. The oscillations in each of the two directions may occur concurrently. The oscillations in each of the two directions may be out of phase with one another. The oscillations in each of the two directions may be sinusoidal or approximately sinusoidal. A cyclic movement does not necessarily require the data to exhibit a circular or oval pattern. A cyclic movement, or cyclic pattern, may comprise oscillating movements in the two directions over time. When plotted in three dimensions that comprise the x and y directions (as labelled herein for convenience) and time, a cyclic movement may represent a spiral or corkscrew shape - e.g. depending on the nature of the oscillations and their relative phases in each of the two directions. This shape may become apparent when the skin movement in two directions is plotted on a 3-D graph of each direction over time - for example, as shown in Figures 6a to 6d. A cyclic movement need not be a perfectly closed loop in the x-y plane such that movement of the skin returns to exactly the same point as each cycle is completed. In some examples, a cyclic movement may be identified by detecting, in the time-varying data captured by the sensor, a combination of movements which represent the skin periodically returning to approximately the same point each cycle as detected by the sensor. In some examples, a cyclic movement may be identified by detecting a repetition of the gross movement of the skin. A cyclic movement may not be strictly periodic according to a fixed or varying period (although it could be). A cyclic movement may comprise movement of the area of skin in the plane which comprises components of movement in the two directions such that, after a single cycle, the area of skin returns approximately to its starting point. A plurality of cyclic movements may comprise movement of the area of skin in the plane which comprises approximate repetition of the components of movement in the two directions such that, on each cycle, the area of skin passes approximately through its starting point. The starting point of the area of skin may be any position of the area of skin relative to the sensor capturing the movement of that area of skin - the starting point may be position of the area of skin relative to the sensor when the jaw muscles (or more generally the facial muscles) are not activated such that face is at rest. The area of skin may be considered to have returned approximately to its starting point when the position of the area of skin along the two directions is within 25%, 20%, 15%, 10%, 5% or 2% of its starting point as determined by the sensor arranged to monitor the movement of the area of skin in the plane of the skin. To detect a cyclic movement, feature extraction methods or other data analysis techniques or machinelearning models may be used (e.g. by the processor 350) that look for a sequence of one or more cyclic movements. For example, information such as the size, cross-sectional area of the cyclic loop in the x-y plane, direction of movement, and turning points (e.g. where the gradient of the curve changes sign) of a potential cyclic movement may be extracted from the sensor data and analyzed to confirm the presence of a cyclic movement. In another example, the sensor data may be transformed into new data representations utilizing the 2D information from the sensors (e.g. movement measured in the x and y axis) at the same point in time. In an example, a simple 2D representation could be an image of the x-y plot, on top of which deep learning could be utilized to identify a cyclic movement. For example, a machine learning model could be trained to identify a cyclic movement in an image of the x-y plot based on images of sensor data exhibiting different cyclic movements having varying features such as their size, orientation and turning point. Multiple cyclic movements may be detected in the same way as individual cyclic movements. In many cases, a single jaw activity may be immediately preceded or followed by other jaw activities that are responsible for multiple cyclic movements, or a single jaw activity can be responsible for multiple cyclic movements. The processor may be configured to detect a plurality of cyclic movements at step S110. This may have the advantage that anomalies in individual cyclic movements within the plurality of cyclic movements can be ignored, whilst still picking up the majority of the plurality of cyclic movements. At step S120, the processor may determine one or more characteristics of the cyclic movement(s). The characteristics may be used to identify the jaw activity. Step S120 may be omitted, for example if the processor processes the time-varying data using machine learning techniques in which characteristics of the cyclic movement may not be explicitly identified, as described further below. Steps S120 and S110 may be performed simultaneously. The determination of characteristics of the cyclic movement(s) may be a part of detecting whether or not a cyclic movement or a plurality of cyclic movements are occurring. A characteristic of an individual cyclic movement may include any of the following: A maximum amplitude of the cyclic movement. The maximum amplitude may be in either or both of the two dimensions in which the sensor is configured to capture data. For example, the maximum amplitude may be in either or both of the two directions in which the skin movement is captured. An average velocity of the skin movement. The average velocity of the skin movement may be determined by taking two points in time and determining the distance travelled by the area of skin between those two points. A maximum velocity of the skin movement. The velocity of the skin movement at a particular point in time can be found from the gradient of the distance-time curve at that point in time. The maximum velocity of the skin movement is the gradient of the cyclic movement at its steepest point. An acceleration of the skin movement (e.g. the rate of change of the velocity of the skin movement). The acceleration of the skin movement can be found from the rate of change of the gradient of the distance-time curve. A duration of the cyclic movement (e.g. the time over which the cyclic movement is made). The orientation of the cyclic movement. The orientation may be determined by the angle made by the major axis of the cyclic movement with respect to a fixed axis. In examples in which the processor is configured to detect a plurality of cyclic movements, the characteristics of the plurality of cyclic movements may further include any of: The variance of the maximum amplitude of each cyclic movement in the plurality of cyclic movements (e.g. a change in maximum amplitude of each cyclic movement of the plurality of cyclic movements overtime). The average frequency of cyclic movements in the plurality (i.e. number of cyclic movements per unit time). The frequency variability of cyclic movements in the plurality (e.g. whether the frequency of the repeated cyclic movements is constant). In examples in which the wearable apparatus comprises a pair of sensors each located so as to monitor a respective area of skin associated with a corresponding muscle on either side of the face of the user, the processor may determine a measure of asymmetry between the cyclic movement or movements detected in the data from each sensor in the pair of sensors, and identify the jaw movement in dependence on the measure of asymmetry. Each of the above characteristics may be defined separately for the two directions in which the skin movement is captured (e.g. a first and a second direction). For example, the cyclic movement may have maximum amplitude in the first direction and a maximum amplitude in the second direction. The processor may determine some of the above characteristics for part (e.g. half) of a cyclic movement where applicable. For example, the processor may determine some of the above characteristics for an opening-phase of a cyclic movement and separately determine some of the above characteristics fora closing-phase of the cyclic movement. For example, an average velocity of the skin movement may be determined for the opening-phase of the mouth by determining the time taken to reach a maximum amplitude of the cyclic movement from a neutral position. The maximum amplitude of the cyclic movement is indicative of the mouth being at its most open point. This characteristic can be used to determine a jaw metric relating to the speed that the jaw opens. Similarly, the average velocity of the skin movement may be determined for the closing-phase by determining the time taken for the area of skin to return to the neutral position from the maximum amplitude. This provides information regarding how fast the jaw closes. When the user is chewing, the lower jaw partially opens and closes to chew the food. The processor may learn to distinguish the partial opening and closing of the jaw that occurs during chewing from a mouth-opening and mouth-closing event when the mouth is empty (e.g. during singing, talking, or when about to take a bite) based on characteristics of the cyclic moment such as its amplitude, and the context in which it occurs. Based on characteristics of the cyclic movement, the processor may optionally determine one or more jaw metrics of the user. A jaw metric quantifies the movement of the jaw that caused the cyclic movement. The following are examples of a jaw metric: how fast the jaw moved during the cyclic movement (jaw velocity), how wide the mouth opened (bite amplitude), the acceleration of the jaw during the cyclic movement (jaw acceleration), or how long it took for the jaw to open or close (mouth-opening duration and mouth-closing duration). Table 1 shows a list of example jaw metrics and the characteristics of the cyclic movement that may be used to determine them. Jaw metric Characteristic of the cyclic movement used to determine jaw metric Bite amplitude Maximum amplitude of the cyclic movement Opening jaw velocity Average velocity of the skin movement during the opening-phase of the cyclic movement. Closing jaw velocity Average velocity of the skin movement during the closing-phase of the cyclic movement. Opening jaw acceleration Rate of change of the velocity of the skin movement during the opening-phase of the cyclic movement. Closing jaw acceleration Rate of change of the velocity of the skin movement during the closing-phase of the cyclic movement. Maximum jaw velocity The maximum velocity of the skin movement during the cyclic movement. Maximum jaw acceleration The maximum value of the rate of change of the velocity of the skin movement during the cyclic movement. Mouth-opening duration Time taken to reach a maximum amplitude of the cyclic movement from a neutral position. Mouth-closing duration Time taken for the area of skin to return to the neutral position from the maximum amplitude. Table 1 In examples in which the wearable apparatus comprises a pair of sensors each located so as to monitor a respective area of skin associated with a respective muscle of a pair of facial muscles, the processor may determine a jaw metric relating to the symmetry of the user’s jaw activity. A measure of jaw symmetry or asymmetry can be determined from a comparison between the data from the pair of sensors during a jaw activity. For example, each sensor in the pair of sensors may be configured to capture time-varying data describing movement of the respective area of skin in at least one dimension in a plane defined by that area of skin. The processor may process the data from the pair of sensors to identify a first cyclic movement of the area of skin monitored by a first one of the pair of sensors, and a 22 second cyclic movement of the area of skin monitored by a second one of the pair of sensors. The first and second cyclic movements may then be compared so as to produce a measure of asymmetry relating to the jaw activity causing the first and second cyclic movements. In one example, base values of each jaw metric may be derived from images or videos captures of the individual’s mouth to calibrate the system. In another embodiment, the co-activation of facial muscles associated with lip dilation may further characterise the bite behaviour. At step S130, the processor identifies a jaw activity. The processor may identify a jaw activity based on the characteristics of the cyclic movement(s). Over time the values of certain characteristics can be associated with particular jaw activities. In some examples, the system may be trained for an individual by getting the user to log different jaw activities so that the processor can learn to associate a range of values for each characteristic with a jaw activity. In an example, the processor may be configured to identify a mouth-opening event in response to detecting a cyclic movement having a maximum amplitude in the dimensions in which the skin movement is captured that exceeds a predefined threshold. For example, the processor may be configured to determine that a mouth-opening event has occurred if the amplitude in the y-direction exceeds a predefined threshold, and / or if the amplitude in the x-direction exceeds a (different) predefined threshold. The predefined threshold may be updated over time as the system learns the maximum dimensions of the user’s mouth such as their maximum bite amplitude. Based on the characteristics of the cyclic movement(s), the processor may be able to rule out certain jaw activities as not having occurred and may determine that other jaw activities are more likely to have occurred. For example, when talking, the jaw tends to move much more quickly and erratically than when eating. A short duration of a cyclic movement combined with a high average speed of the skin movement may suggest that the user is talking rather than eating. A high maximum amplitude suggests that the user may be taking a bite of food, singing or yawning. As another example, if the processor determines that the duration of the cyclic movement, and more specifically the duration of the opening and closing phase of the cyclic movement is unusually long, it may determine that the user is yawning or singing, rather than eating, for example. As will be described later, combining the data from mouth opening with data from other sensors such as a motion sensor and / or microphone may be used to identify and determine the jaw activity. An example of the latter is to distinguish pathological changes in jaw function such as trismus, or bruxism. Figure 8 shows an example of time-varying data captured by the temple sensor obtained whilst the user clenches their jaw repeatedly, exhibiting bruxism. As can be seen in figure 8, the area of skin being monitored exhibits a cyclic pattern in both directions in the plane of the area of skin. Although bruxism produces similar looking cyclic movements to that during chewing in this example, the maximum amplitude of the cyclic movements during bruxism is significantly (almost 10 times) smaller than the maximum amplitude of a cyclic movement during chewing. In addition, the maximum amplitude of the cyclic movements during bruxism does not reduce overtime, unlike during chewing. Thus, the processor may be able to distinguish repeated jaw clenching from chewing based on the maximum amplitude of the cyclic movement(s). As explained further below, the processor may also be configured to use the context in which the cyclic movements occur to help identify the jaw activity. Whilst the system may determine a jaw activity from the detection of one individual cyclic movement of the area of skin, preferably, each cyclic movement is considered in the context of the data preceding and following that cyclic movement. For example, the processor may identify a jaw activity based on characteristics of the cyclic movement in combination with the position of the cyclic movement in a series of cyclic movements. For example, the processor may be configured to only identify a chewing activity if immediately preceding the chewing activity it detected an initial mouth-opening event and a mouthclosing event. This avoids the processor incorrectly identifying a chewing activity when instead the user is repeatedly clenching their jaw, for example. Thus, the processor may attribute a jaw activity to a cyclic movement in dependence on a position of the cyclic movement in a sequence of jaw activities. In addition to using multiple cyclic movements to determine the context in which a particular cyclic movement occurs, the processor may determine a jaw activity based on characteristics of a plurality of cyclic movements, as mentioned above. That is, instead of identifying a jaw activity based on one individual cyclic movement (even when considered contextually), one jaw activity may be responsible for a plurality of cyclic movements. Chewing is an example of this. The processor may be configured to determine chewing based on repeated cyclic movements. The repeated cyclic movements may at least initially have similar characteristics, such as similar duration per cycle, similar amplitudes, and similar maximum speeds. The repeated cyclic movements occur sequentially. Machine Learning The process of identifying a jaw activity from the time-varying data captured by the sensor(s) of the system may be performed using a machine learning algorithm or performed at a machine learning system. For example, a machine learning algorithm may be used in place of steps S110 and S120, or steps S110 and S120 may be performed implicitly (or explicitly) by a machine learning system. A machine learning system for learning jaw activities may be provided at a computer system to which wearable apparatus is connected, such as the external computer system 380. The machine learning system may provide a dataset for use by the processor so as to enable the processor to identify a jaw activity according to the scheme learned by the machine learning system. Alternatively, the machine learning algorithm and / or machine learning system may be provided at the processor 350. The wearable apparatus may be provided with a learning mode in which the output of the sensor is passed through to the machine learning system at the computer system, processor or system on chip or chiplet architecture. In one example, the time-varying data captured by the sensor is used as an input to a machine learning system such as a classifier. By causing a user to perform different jaw activities and informing the machine learning system which activities are being performed, the machine learning system can (using suitable algorithms) learn to identify which cyclic movements are associated with which jaw activities. In other words, the machine learning model may be provided with training data that includes pre-classified jaw activities (e.g. already known jaw activities) and the labels associated with each known jaw activity. In this way, the machine learning model can be trained to label certain cyclic movements as corresponding to certain jaw activities. Preferably, the training data includes cyclic movements that correspond to a series of known jaw activities (e.g. each cyclic movement corresponding to a respective jaw activity) and cyclic movements that correspond to singular known jaw activity (e.g. a known jaw activity that corresponds to a plurality of cyclic movements). In this way, the machine learning model may be trained to distinguish between a jaw activity that causes multiple cyclic movements (e.g. chewing) and a jaw activity that causes one cyclic movement (e.g. mouth opening) but which may also be identified based on the context of the cyclic movement. The machine learning system for identifying a jaw activity using time-varying data from the sensor(s) may be configured to perform some or all of the following steps: Preprocessing - the data from the sensor(s) may be segmented into time windows or frames. This can help in capturing the temporal dynamics of the activities occurring. Filtering - filtering techniques may be applied to remove noise or outliers from the data. Data augmentation - the data may be augmented with variations of the existing data to improve the generalization of the model. Techniques that may be employed in this step include time warping, jittering, or introducing random noise. Feature extraction - the relevant features from each segmented time window may be extracted. The relevant features can be used to represent the underlying patterns in the data. Relevant features may include: statistical features (e.g. mean, standard deviation, skewness, kurtosis); frequency domain features (e.g., Fast Fourier Transform (FFT) coefficients); time-domain features; autoregressive coefficients, or domain-specific features such as features that describe the cyclic patterns visible in the x-y data. Feature Selection - the most relevant features may be chosen to reduce dimensionality and computational complexity. Techniques like Recursive Feature Elimination (RFE) or feature importance from tree-based models can be used here. Model Training - In this step, a feature-based machine learning model is trained (e.g., Random Forest or any other algorithm based on feature input) using a training data set comprising examples of cyclic movement and associated data indicating the mouth activity represented by each cyclic movement, along with - typically - data which does not represent cyclic movement. The training data set may be formed at least in part using features extracted from each segmented time window in the manner described above. The training data may be processed using one or more of the steps described above prior to being input to the machine-learning model. In a more complex machine learning example, the processor may identify a jaw activity based on the cyclic movement of the area of skin using a neural network. For example, data from the sensor that is captured during different jaw activities in which a cyclic movement or a plurality of cyclic movements can be detected in the skin movement may be provided to the neural network as training data. The data from the sensors may be processed using some of the same steps described above, such as the preprocessing and filtering steps and the data augmentation. The neural network may be trained using the training data from the sensors (e.g. as discussed above) by optimizing the network for a specific classification task. As would be known by the skilled person, hyperparameters such as learning rate, batch size, and network architecture, can all be experimented with to achieve optimal performance. Thus, the neural network may be trained using the training data to classify a cyclic movement or a plurality of cyclic movements as corresponding to a jaw activity. The neural network may perform the classification based on the one or more characteristics of the cyclic movement as described above, but this step may not be explicitly performed. The neural network architecture used can be flexible, provided it is suitable for time-series data. Suitable neural network architectures include recurrent neural networks (RNNs), Long Short-Term Memory networks (LSTM), Convolutional Neural Networks (CNNs), Transformer, Informer, Residual networks, Inception networks, and similar. Any combination of layers based on existing deep-learning architectures may be used. In one example, the Spectro Temporal Residual Network (STResNet) can be used. STResNet extracts channel-specific spectro-temporal information. As spectral information, the networks calculate the spectrogram in decibels, i.e., Iog10 of the amplitude spectrogram, for each input window. For the temporal information, the network uses residual blocks that contain CNN layers with 1-dimensional filters. The output from all channel-specific layers is then merged by a fully connected layer. The fully connected layer is followed by a softmax layer, that outputs class probability estimates for each input segment. Once trained, the neural network can take as an input time-varying data from the sensor describing movement of an area of skin in a plane defined by the area of skin that exhibits a cyclic movement and classify the cyclic movement as corresponding to a jaw activity. The output of the neural network is the identified jaw activity. The neural network may be trained and / or implemented on a hardware accelerator in the processor or run as software on the processor. At step S140, optionally, feedback may be provided to a user about the jaw activity that has occurred. In an example in which the jaw activity is chewing, the processor may log the activity as an eating episode and inform the user of the time and duration of the eating episode, once it is over. The user may correct the logged activity if it has been incorrectly classified. This may be fed back to the machine learning algorithm or neural network so as to improve its accuracy. The sensor used to capture the data in the above-described method is not limited to a temple sensor. The sensor can be located so as to monitor any area of skin whose movement is correlated with a facial muscle associated with movement of the jaw. Figures 7a to 7d show a graph of time-varying data captured by a cheek sensor (e.g. sensor 202) configured to monitor an area of skin associated with the zygomaticus muscle. In this example, the cheek sensor is configured to capture data in two dimensions in a plane defined by the area of the skin, as described above in relation to figures 6a to 6d. It can be seen that similar cyclic movements exist in the data captured by the cheek sensor compared to the data captured by the temple sensor. Multiple sensors The above method is described with reference to the data captured by one sensor. Preferably, the processor is configured to identify a jaw activity based on data from a plurality of sensors of the type described above. The time-varying data captured by the plurality of sensors can be compared to identify a mouth activity. For example, steps S100 to S120 may be independently performed as above for data captured by each appropriate sensor in the wearable apparatus. Step S130 may be performed based on a combination of the data captured by the plurality of sensors. For example, the processor may compare the cyclic movement(s) detected from the data from each sensor so as to identify the jaw activity. By looking at data captured from multiple sensors, the accuracy and reliability of the system at identifying a jaw activity can be improved. Suitably, the apparatus comprises at least a first sensor configured to monitor an area of skin associated with the temporalis muscle, and a second sensor configured to monitor an area of skin associated with the cheek muscle such as the zygomaticus muscle. By monitoring an area of skin associated with the temporalis muscle and an area of skin associated with the zygomaticus muscle, the system may be more likely to correctly identify an eating activity because eating activities tend to cause movement of areas of skin around both the zygomaticus muscle and the temporalis muscle. By comparing data captured from both a temple sensor and a cheek sensor, the system is less likely to incorrectly classify a jaw activity such as bruxism (which may only be picked up from the temple sensor data) as an eating activity. In an example, the phase difference between the time-varying data from each sensor may be used to identify the mouth or jaw activity. In an example in which each sensor outputs data in the form of a time- varying vector describing movement of the respective area of skin, the phase difference between the two vectors may be used to help identify the mouth activity. This may be particularly effective when the apparatus comprises a pair of sensors each located on the apparatus so as to monitor respective corresponding areas of skin on either side of the user’s face. When chewing, for example, a user may have a dominant side of the mouth on which they preferentially chew their food, giving rise to a distinctive side-to-side grinding movement during chewing. This would be apparent when comparing the phase difference between the data of the two sensors (e.g. temple sensors 201) located on either side of the user’s face. The data may be compared to determine a measure of asymmetry in the mouth activity, as described above. The measure of asymmetry may be indicative of pathology of the joint, bone, muscles, teeth, gum, tongue or oral lining. When performing certain jaw activities, there is often a coordinated sequence of muscle activations that occur. For example, contraction of the muscle that facilitates mouth opening is usually coordinated with relaxation of the orbicularis oris muscle and differential contraction of the cheek muscles to retract the lips and expose the teeth. Similarly, during mastication the coordinated contraction and relaxation of the cheek muscles on both sides of the face facilitates the manipulation of the food bolus in collaboration with the tongue. This sequence of muscle activations can be detected with the system described herein comprising multiple sensors and used to identify or confirm the jaw activity occurring. For example, combining information from a lip dilator and / or lip constrictor sensor (e.g. a sensor located on the wearable apparatus so as to monitor an area of skin associated with a lip dilator and / or lip constrictor), with data obtained from the sensor(s) may improve the accuracy and reliability of the system in identifying mouth or jaw activities. A sensor or sensors for monitoring a lip dilator and / or lip constrictor may be particularly advantageous when determining a mouth-opening event, as the lip constrictors and lip dilators are most active during this event. As mentioned above, the wearable apparatus may comprise a motion sensor, which may be an inertial measurement unit (IMU). The motion sensor may be configured to measure tilting of the head of the user when the wearable apparatus is worn on the head of the user. This may allow the system to infer that a jaw activity (e.g. mouth-opening and mouth-closing) corresponds to consumption of a pill such as a food supplement or medication. When a user ingests a pill, they typically tilt their head backwards at the same time as swallowing the pill. The motion sensor can be used to detect the head tilting, which can be compared (e.g. at the processor) with information regarding the identified jaw activity (e.g. the opening and closing of the mouth) prior to the head tilt. The processor may infer that a pill has been ingested based on the comparison. In some examples, the wearable apparatus may comprise a detector for detecting a signal emitted from the pill to confirm ingestion of the pill. For example, the pill may emit acoustic or radio waves that may be detected at the detector in the wearable apparatus. This may allow for more accurate tracking of a user’s medication or supplements. In other examples, the wearable apparatus may comprise a 28 detector for detecting a signal emitted from a medication container. For example, a unique acoustic signal from the medication container may be sensed by one or more microphones on the wearable devices to provide context that the subsequent head and jaw motions indicate medication ingestion from the container. Facial expression detection The system may further comprise sensors configured to monitor areas of skin that are not associated with or correlated with mouth muscles. For example, the apparatus may comprise sensors located so as to monitor from the upper facial muscles such as the contraction or passive stretching of corrugator supercillii, depressor supercillii, procerus, and orbicularis oculi. In the example in which the wearable apparatus comprises a pair of glasses, sensors for monitoring upper facial muscles such as the corrugator supercilli can easily be incorporated into the frame of the glasses. By monitoring the activation of upper facial muscles, the system may be configured to infer a facial expression of the user during the jaw activity. A method for inferring a facial expression of a user from sensor data that monitors movement of the skin around the upper facial muscles can be found in UK Patent No. GB2561537, which is incorporated by reference herein. When combined with information from the other sensors, the system may be able to determine an emotional reaction or preference of the user during a particular mouth activity. This may be provided as feedback to the user. For example, bruxism and / or individual jaw clenching are often triggered by emotional and / or physical stress, anxiety, pain, anger, or intense concentration. Jaw clenching may by itself indicate an emotional response such as the negative valence responses of anger, nervousness, tension or stress. Inferring the facial expression of the user whilst they are clenching their jaw and / or exhibiting bruxism provides the user with additional information regarding how they are responding to external factors. Generally, analysing the facial expression of the user whilst certain mouth activities are occurring may have a variety of applications, including psychological assessment of stress and anxiety, medical uses such as for detecting subtle signs of pain, monitoring progress in treatments such as for TMJ dysfunction or chronic neck pain, personalised pain management, research purposes such as understanding pain and muscle movements / contractions, and biofeedback therapy. The device may also be used for understanding workplace stress levels and employee well-being or assessing physical strain by assessment of motor overflow. In some examples, the processor may be configured to identify a mouth activity more generally, without necessarily requiring a cyclic movement to be determined. Whilst the sensors preferably capture data in two directions in the plane of the skin, in some cases the sensor may be configured to measure movement of the area of skin in only one direction in the plane defined by the area of skin. When measuring movement of the area of skin in only one direction, preferably that direction is the substantially vertical direction with respect to the user - i.e., in a direction 29 substantially parallel to the sagittal plane of the user. This is because most areas of skin are likely to move the most in the vertical direction during jaw activities such as opening and closing the mouth than in other directions. If the sensor is configured to measure movement of the area of skin in only in one direction in the plane of the skin, an oscillating movement in that direction overtime (e.g. as shown in figures 6c and 6d) can be detected which may be used to identify a mouth or jaw activity. Tongue-pressing activity In an example, the apparatus may be configured to identify the user pressing their tongue against a part of their mouth. Pressing the tongue against a location in the mouth causes activation of at least one jaw muscle so as to stabilize the jaw in reaction to movement of the tongue. For example, the anterior part of the temporalis muscle may primarily be activated in response to the tongue being pressed against a part of the mouth. Thus, skin movement correlated to the activation of the anterior part of the temporalis muscle may be used to identify a tongue-pressing activity. When the user presses their tongue against a part of their mouth and then releases that pressure, an area of skin whose movement is correlated with activation of a jaw muscle may exhibit a cyclic movement which can be detected, e.g. by the sensors described herein. If the user repeatedly presses their tongue against a part of the mouth, multiple cyclic movements may be identified. Figure 9 shows data captured by a temple sensor on the apparatus described herein when the user repeatedly presses their tongue against their front teeth. In this example, when pushing the tongue against the front teeth, the anterior part of the temporalis muscle contracts. The maximum amplitude of the skin movement during this activity is, similarly to during bruxism, significantly smaller (e.g. 10 times smaller) than the maximum amplitude of the skin movement during other jaw activities such as chewing or opening the mouth. In an example, the amplitude of the skin movement can be used to identify a tongue-press, additionally or alternatively to detecting that the anterior part of the temporalis muscle is activated. Generally, the machine learning system can be trained to identify skin movement resulting from the user pressing their tongue against the mouth based on training data exhibiting this activity. Being able to identify the jaw activity of pressing the tongue against a part of the mouth can improve the robustness of the system in correctly identifying other jaw activities. The system described herein may further be used to indirectly infer the location of the tongue-press. Depending on what part of the mouth the tongue is pressing against, different jaw muscles may be activated and to differing degrees. In an example, when pushing the tongue against the front teeth or anterior palate, the anterior part of the temporalis muscle contracts. In an example, when pushing the tongue against the front teeth or anterior palate, the strength of activation of the temporalis muscle on the two sides of the face is approximately equal. In an example, when the tongue presses against the left teeth, the strength of activation of the jaw muscles (e.g. temporalis) on the right side of the face is generally greater than the strength of activation of the corresponding jaw muscles on the left side of the face. In an example, when the tongue presses against the right teeth, the strength of activation of the jaw muscles (e.g. temporalis) on the left side of the face is generally greater than the strength of activation of the corresponding jaw muscles on the right side of the face. The processor may therefore be configured to distinguish between a tongue press being performed by the user on the left and right teeth inside the mouth by comparing the strength of activation of one or more jaw muscles (e.g. degree of movement of the skin correlated with the respective muscle(s)) on the two sides of the face. The processor may be configured to identify a tongue press on the front teeth or anterior palate by detecting tongue activity in which the strength of activation of corresponding jaw muscles on the two sides of the face is substantially the same. The system can be trained to recognize different patterns of skin movement that correlate with different jaw muscle contractions each associated with the tongue pressing against a particular location within the mouth. This may be improved with calibration for a particular user. In particular, the system may infer the location of the tongue-press based on the balance of jaw muscle activation on either side of a user’s face. By monitoring the activity of the jaw muscles in the manner described herein it is therefore possible to determine where the tongue is being pressed inside the mouth - e.g. by comparing jaw muscle activity on the left and right side of the user’s face, and / or by looking at which section of the temporalis muscle is activated. This enables, for example, the tongue to be used as an input to a computer system without requiring an invasive sensors inside the mouth. Taking the temporalis muscle as an example, using data from a sensor located so as to monitor an area of skin associated with the temporalis muscle and configured to capture skin movement in two dimensions in a plane defined by the area of skin being monitored, the processor may determine pressing locations of the tongue within the mouth. The temporalis muscle is activated by several different nerves that cause different sections of the temporalis muscle to move. Generally, the anterior part of the temporalis muscle primarily activates in response to a tongue-press. When the user presses their tongue against different parts of the mouth, for example against the front teeth or the roof of the mouth, different sections of the temporalis muscle may be activated. The anterior part of the temporalis muscle may activate most strongly when the tongue is pressed against the front teeth or anterior palate. Depending on the section of the temporalis muscle that is activated, a different movement of the skin associated with (e.g. overlying) the temporalis muscle may be observed, enabling the movement of that skin to be used to determine which part of the temporalis has activated. Whilst movement of individual sections of the area of skin in the temple region may be difficult to discern using the naked eye, by monitoring the area of skin associated with the temporalis muscle with the sensor type described herein, the sensor may differentiate between movement of different parts of the skin within the area of skin being monitored. The processor may correlate particular movements of the area of skin being monitored with particular sections of the temporalis muscle being activated that are associated with certain tongue activities (e.g. using suitable training such as a machine learning algorithm or neural network as described above). The system may thus infer that particular skin movements of an area of skin associated with the temporalis correspond to particular tongue pressing activities. In a simple example, the area of skin being monitored by the sensor may be divided into quadrants. Each quadrant may be monitored independently by the same sensor. Movement of the skin within each quadrant may be associated with a particular tongue activity (e.g. once the system has been trained to recognise which quadrants correspond to which tongue activities). When movement is detected entirely or predominantly in a particular quadrant, it may be inferred that the tongue is performing the activity associated with movement of that quadrant. In more complex examples, the area of skin may be divided into a greater number of sections. Movement within each section or within a combination of sections may be attributed to a different tongue activity. The area of skin need not be divided into sections explicitly. The system may provide sensor data describing movement of different parts of the skin within the area of skin to the processor which maybe configured to identify the tongue activity using a machinelearning algorithm or neural network. In another example, a comparison of the contraction of the jaw muscles on either side of a user’s face can be used to identify the tongue-pressing location within the mouth. For example, when pressing the tongue into the teeth on one side of the mouth, the activation of the masseter on that side of the face may be more pronounced than the activation of the masseter on the opposite side of the face. The same may apply to the activation of other jaw muscles on either side of the face, such as the temporalis muscle. When pressing the tongue at a central location in the mouth (e.g. in the middle of the roof of the mouth), the activation of the jaw muscles on either side of the face may be the same or similar. The activation of the jaw muscles is reflected in the amount of movement of an area of skin associated with those jaw muscles. The tongue-pressing location may be inferred from the balance of muscle activation on either side of the user’s face. This can be determined using sensors located on corresponding sides of the apparatus. The data from the sensors located so as to monitor an area of skin associated with a jaw muscle on either side of the user’s face may be used in combination with the technique described above in relation to monitoring sections of the temporalis muscle to infer the tongue-pressing location. Inferring the position of a tongue press within the mouth using the system herein may enable an alternative use of the system as an input device, e.g. for use by people with full body paralysis, or as input device for an augmented reality or virtual reality headset where it is advantageous for the user’s hands to be free to perform other tasks. The tongue pressing location may be used to control a device, for example as an alternative to a computer mouse or joystick. For example, four distinct tongue pressing locations may be used to operate a left-right-up-down controller or the like. Other mouth activities may also be used to remotely control a device, such as jaw clenching. A mouth activity can be used as an input method for certain types of questions or prompts to an Al assistant. For example, if a user is navigating a location or challenging task, rather than writing or speaking a message, explicit or implicit input can be used to facilitate communication. Implicit communications may include emotional responses such as facial, mouth, or jaw movements. Jaw movements indicative of stress can be used, such as jaw clenching. The intensity of the jaw clenching movements can be used to communicate the degree of stress. Explicit communication could be distinguishable from implicit by the duration or sequence of clenching. For example, a double clench, or triple clench may be pre-configured as an explicit communication. Integrating a jaw monitoring device that can read facial expressions into a human-computer interaction system could enhance each stage of the human-computer interaction process in several ways. These are described below with reference to an Al assistant. 1. Initiation: The Al system can detect when the user is puzzled or in need of information through their facial and / or mouth or jaw movements, potentially prompting the Al to offer assistance proactively. 2. Formulation: By reading the user’s facial and / or mouth or jaw movements, the Al might infer the user’s emotional state or level of understanding, allowing it to guide the formulation of their query more effectively. 3. Transmission: The Al could use visual cues to confirm whether the user’s request has been communicated successfully or if further clarification is needed. 4. Processing: Emotional cues such as stress indicators can help the Al understand the urgency or the emotional context of the request, influencing how it processes the information. 5. Response Formulation: There are ways that the system can provide value to the user. The Al can adjust its response based on the user’s emotional state - for instance, providing more detailed explanations if the user seems confused. The Al can modulate the delivery of its response, such as using a more empathetic tone if the user seems stressed. Based on past interactions the Al can make suggestions inferred from their past emotional responses e.g. playing relaxing music. 6. User Interpretation: By continuously reading the user’s reactions, the Al can gauge whether the information is being understood and can offer further clarification as needed. 7. Feedback and Iteration: Real-time emotional feedback allows the Al to adjust its responses on the fly, improving the quality of the interaction and reducing the need for repeated queries. Eating episode example In a more specific example, the system described herein is used to monitor a user’s eating episodes. Figure 5 shows a series of mouth activities that occur during an eating episode. An eating episode occurs when the user consumes some food. For example, if the user eats a snack this is classified as an eating episode, even if it only involves one mouthful. As another example, when the user consumes a meal such as breakfast, lunch or dinner, the duration over which the meal is consumed is an eating episode. An eating episode starts when the user initiates consumption such as an initial bite of food and ends when the user stops consumption fora predetermined amount of time. The predetermined amount of time after which the eating episode is deemed to have finished may be updated by the system as it learns the user’s habits. For example, if the user is a particularly slow eater, the system may wait longer before it determines that the user has finished their eating episode. An eating episode can only start if a previous eating episode is not already in progress. Steps S510 to S540 are described below with reference to the graphs shown in figures 6a to 6d. An eating episode starts with an initial mouth-opening event at step S510. This can be identified using the techniques described above. In figures 6a to 6d, the initial mouth-opening event occurs within the first few seconds (e.g. at 0 <t <2 seconds). An example cyclic movement that may be associated with a mouth-opening event is marked out with a dashed box in figures 6b, 6c and 6d. In this case, the first cyclic movement has a relatively large maximum amplitude in both the x and y direction compared to the other cyclic movements, indicating that a mouth-opening event may be occurring. At step S520, subsequently to the mouth opening event, the user may close their mouth over some food that they are taking a bite from, e.g. an apple. The processor may determine that a mouth-closing event has occurred if, following the mouth-opening event, it detects a reduction in amplitude of the cyclic movement from the maximum amplitude to a value that is near the user’s closed-mouth position. The mouth-opening and mouth-closing event may be identified based on the same cyclic movement. In some examples, the processor may increase a sampling rate of the sensor after steps S510 and S520 occur. As the mouth opening and mouth closing event may indicate that an eating episode is starting, the sensor may be awakened from operating in a previous low-power mode in order to sample more frequently to detect the eating episode. This may save power by not operating the sensor at a high sampling rate when an eating episode is unlikely to be occurring. After the mouth has closed at least partially, the user starts to chew the food. At step S540, in response to detecting repeated cyclic movements, a chewing activity is determined. As the repeated cyclic movements were immediately preceded by a mouth-opening and a mouth-closing event, it is expected that a chewing event may subsequently occur. Chewing may be confirmed by analysing the data for further features such as a reduction in amplitude of the cyclic movements overtime. For example, the slope of the dotted trapezium in figure 6d indicates the gradual reduction in maximum amplitude of the cyclic movements as the user continues to chew their food and break it down into smaller pieces. Had the graph in figure 6d continued for longer than 14 seconds, the amplitude would have decreased further. Assuming that the user is eating food, rather than chewing gum for example, there will eventually be a pause in the repeated cyclic movements whilst the user stops chewing to swallow their food (not shown in figures 6a to 6d). At step S550, a swallowing activity is inferred. The processor may be configured to infer a swallowing event in response to detecting an interruption in the repeated cyclic movements for predetermined interval of time. A reduction in the maximum amplitude of the cyclic movements immediately preceding the interruption may additionally be used to infer a swallowing event. If the apparatus comprises a motion sensor and / or microphone, a swallowing event can be confirmed based on a peak in the data from the motion sensor and / or microphone during the interruption in the repeated cyclic movements. The user may then either continue to chew the food or open their mouth to take another bite, and the process described above continues. Alternatively, after the swallowing activity, the eating episode ends. When the processor detects an eating episode (as evidenced by the sequence of mouth activities shown in figure 5), it may determine an eating metric of the user. An eating metric may include: The number of eating episodes per 24hrs The duration of each eating episode Eating episode chewing count (the number of chews per eating episode) Bolus duration (how long each mouthful is chewed for) Bolus chewing count (the number of chews per mouthful) Chewing rate (the number of chews per minute) Inter-bolus interval (gap between mouthfuls in the same eating episode) The eating metrics may be provided as feedback to the user, e.g. at step S140. The system may be pre-configured by the user or to learn using the neural network about the eating context. The eating context comprises the commonest eating locations, times or patterns of preceding sensor data (such as associated with food preparation). Sensor data may include the location of the user based on GPS, Wifi, Bluetooth or other local area network data. Sensor data may include motion data indicative of the pose, posture or the activity of the user. Sensor data may also include the time windows since the eating sensors last detected an eating episode. This may be used by the processor to predict the time of the next eating episode, as will be explained in the power-saving section later. Power saving features It is desirable to reduce the amount of time that the sensor or sensors in the wearable apparatus are using power where possible. The sensor(s) of the wearable apparatus are likely to be powered by a respective battery or a centralized single battery. It is advantageous to reduce the frequency with which the battery or batteries in the wearable apparatus need to be charged or replaced. The system may be configured to adapt the sampling rate (e.g. how often the sensor captures data) of each sensor in the wearable apparatus. Each sensor may have a variety of operating modes with varying sampling rates. For example, each sensor may have a stand-by mode in which the sensor does not capture any data, a power-saving mode in which the sensor captures a minimal number of samples, and a full-power mode in which the sensor takes a maximum number of samples. There may be more or fewer than three operating modes. The system may control each sensor to operate in one of the operating modes at a time. There may not be discrete operating modes for each sensor. For example, the sampling rate of each sensor may be varied by the system to any desired value, rather than controlling the sensor to operate in a particular mode. Some sensors in the system may be more power intensive than others. For example, if the wearable apparatus comprises a camera 205, the camera is likely to require more power to run than the temple sensor 201, which may be an optical flow sensor, for example. To save power, the system may only operate the high power sensor(s) during periods in which eating has been detected or is likely to be occurring. To detect this, the system may use low power sensor(s) to detect activity that indicates eating may be about to occur. The low power sensors may be controlled to sample data at a relatively low sampling rate until an eating activity is detected. For example, in response to detecting a mouth-opening event using a low power sensor, the system may increase the sampling rate of the low power sensor and / or the sampling rate of a high power sensor. If it is determined that the mouth-opening event did not lead to a chewing activity, and so an eating episode is not occurring, the sampling rate of the high power sensor and / or the low power sensor may be reduced. The low power sensor may continue to monitor at a relatively low sampling rate in case further jaw activities occur. The sampling rate of the motion sensor may be increased from 50Hz to 4000Hz when an eating episode is detected (e.g. using data captured by another sensor or from the motion sensor when sampling at a lower rate). When sampling at 4000Hz, the motion sensor can act like a microphone to pick up sound wave vibrations which may be used to confirm swallowing or the chewing of hard food. As sampling at 4000Hz will use more power than sampling at 50Hz, the sampling rate of the motion sensor may only be increased to this level when the system detects an eating episode. In a preferred example, the low-power sensor is an accelerometer. An example power-saving method 600 for activating the sensors in the wearable apparatus will now be described, with reference to figure 10. In this example, the wearable apparatus is a head-worn device comprising a motion sensor (e.g. an accelerometer), in addition to one or more of the sensors described above (e.g. the temple sensor 201). The power-saving method may be used whilst monitoring any of mouth, jaw, consuming, and / or eating activities. Suitably, the power-saving method is used when the apparatus is being used to monitor one or more eating episodes. The inventors have recognized that mouth / jaw / consuming activities cause motion in the wearable apparatus that can be detected in a relatively low-power way using a motion sensor. Upon detection of movement of the wearable apparatus that is consistent with movement that might occur during a mouth, jaw, or consuming activity, the sampling rate of other more power-hungry sensors can be increased. If, following an increase in sampling rate of the other sensors, the potential mouth, jaw, or consuming activity is determined not to be occurring, or to have finished, the sampling rate of the power-hungry sensors may be decreased. At step S610, data is captured describing movement of the wearable apparatus using a motion sensor. Said data may be captured over a predetermined period of time. The motion sensor is configured to detect movement of the wearable apparatus relative to the user, when worn. The motion sensor may also be configured to detect gross movement of the wearable apparatus. Preferably, the motion sensor comprises an accelerometer. The sampling rate of the motion sensor during step S610 may be relatively low, e.g. 50Hz. The remaining sensors in the apparatus may be set to operate in a stand-by mode, e.g. where the sampling rate of the sensors is zero or very low. At step S620, the motion sensor data may be analyzed to determine whether the movement of the wearable apparatus meets certain criteria. For example, one criterion may be whether the relative movement of the wearable apparatus exceeds a predefined threshold, e.g. during the predetermined period. If the movement exceeds the predefined threshold, the method may proceed to step S630. Otherwise, the method returns to step S610. Other criteria may be whether the relative movement is below a different predetermined threshold. For example, if the apparatus moves too quickly with respect to the user, it may be determined that the user is not performing a mouth, jaw, or consuming activity. The filtering performed at step S620 may prevent unnecessary further analysis of the motion data in the case where the movement of the apparatus is too small or too big to be suggestive of a mouth activity occurring. Step S620 may be omitted. Preferably, step S620 is implemented directly on the motion sensor, e.g. as an in-built filtering operation. This avoids the need to communicate with the processor in the system. At step S630, the motion data is input to a machine learning model, e.g. at the processor 350. The machine learning model is trained to identify patterns in movement of the wearable apparatus as corresponding to a potential mouth activity. The model may be trained in the same way as the machine learning models described above, but where the training data is motion data obtained whilst the user is wearing the wearable apparatus and is performing known activities that include different mouth activities, non-eating activities and eating activities. The machine learning model does not need to identify the mouth activity itself, it only needs to identify whether it is likely that an mouth activity is occurring based on the motion data. If at step S635 the machine learning model identifies the motion data as corresponding to a potential eating activity (e.g. the answer to step S635 is yes), the method proceeds to step S640. Because steps S630 and S635 are preliminary steps that occur before one or more of the remaining sensors are activated to confirm and analyse the mouth activity, preferably the machine learning model is trained with a large degree of tolerance in what is considered to be a potential mouth activity. That is, the machine learning model is trained to be sensitive enough to detect all mouth activities and potential mouth activities (e.g. eating activities and potential eating activities), even if this includes some non-mouth activities (e.g. non-eating activities). To prevent frequent activations of the remaining sensors caused by false positives, one or more of the remaining sensors may only be triggered (e.g. awoken from stand-by mode) if the machine learning model identifies a potential mouth activity more than a predefined number of times within a predetermined period. For example, if the machine learning model outputs a flag when a potential mouth activity is detected, the method may only proceed to step S640 if the model outputs more than a threshold number of flags within a certain time frame (e.g. 10 seconds). This selective approach may ensure that the remaining sensors are activated only when substantial and consistent indications of a potential mouth activity are detected, thus enhancing accuracy while minimizing unnecessary activations. At step S640, the sampling rate of one or more sensors in the wearable apparatus is increased. The one or more sensors may include the motion sensor. Preferably, at least one of the one or more sensors is a sensor located so as to, in use when the apparatus is worn by a user, monitor an area of skin whose movement is correlated with a facial muscle associated with movement of the jaw. Said sensor is preferably configured to capture time-varying data describing movement of the area of skin in two dimensions in a plane defined by the area of skin (e.g. temple sensor 201 or cheek sensor 202). The increase in sampling rate may be an increase from zero, e.g. activating the sensor(s) from stand-by mode, or from a low-power mode sampling rate. The increase in sampling rate may be different for different sensors. Some sensors (e.g. very high-power sensors) may remain in standby mode until further checks are performed to confirm an eating activity. Once the sampling rate for at least one of the one or more sensors has been increased, the method may proceed to step S100 to determine more information regarding the potential mouth activity. The data capturing method and analysis of the data to determine the mouth activity may be as described above with reference to figure 4 and 5, although it may be performed in an alternative way. At step S650, a check is performed periodically to determine whether a mouth activity is detected. It may be the case that despite a potential mouth activity being identified in step S635, this was a false alarm and no such mouth activity was detected by the one or more sensors. In another case, a mouth activity may have initially been detected by the one or more sensors but it may have since ended. Alternatively, the mouth activity may still be ongoing, for example because the user is having a meal. When it is determined that no mouth activity has been detected for a predefined period, at step S660, the sampling rate of at least one of the one or more remaining sensors is reduced. Not all the sensors that were activated in step S640 need to have their sampling rate reduced at the same time, or reduced by the same amount. For example, some of the sensors may continue to sample at the same rate as I step S640, whilst some of the other sensors may be switched off or returned to standby mode. Each sensor may have its sampling rate decreased individually depending on its respective power requirements. During tests of the above method on eating episodes, on average, eating activities were detected within 15 seconds of the eating activity starting. In addition to or alternatively to the method described above, the system may be configured to control the sampling rate of one or more of the sensors in the wearable apparatus in dependence on a likelihood of a mouth activity occurring, or more specifically, an eating episode occurring. The system may determine the likelihood of a mouth activity occurring as a stand-alone feature, separately to controlling the sampling rate. The likelihood of a mouth activity occurring may be based on one or more of: the time of day, the times at which historic mouth activities occurred, the time elapsed since the most recent mouth activity, a user input, and learned user habits. The processor may be configured to predict the time of the next mouth activity (e.g. based on the likelihood of it occurring as described above) and increase the sampling rate of one or more sensors around the predicted time of the next mouth activity. For example, the system may set all sensors in the wearable apparatus to stand-by mode during certain times of the day in which the mouth activity being monitored (e.g. eating) is unlikely to occur, such as during the night. As another example, the user may provide the system with typical times during the day in which the mouth activity is likely to occur. In another example, the system may learn the times of day that the user is likely to perform that mouth activity. The system may increase the sampling rate of at least some of the sensors around those times. In a more specific example, the processor may be configured to predict when the next consumption episode will occur (e.g. drinking, eating, taking medication). The prediction may be based on the time since the last (e.g. most recent) consumption episode, for example as detected by the processor or as logged by the user. For example, the probability of an eating episode occurring increases non-linearly beyond a certain minimum threshold since the previous meal. The system may learn (e.g. using a neural network) patterns in the consumption habits of the user and base the prediction of the next consumption episode on the learned habits. The system may allow for the user to inputtheir usual mealtimes into the system. Based on the predicted time of the next consumption episode, the system may increase the sampling rate of one or more sensors in the wearable apparatus around the times that the consumption episode is expected. This may preserve the battery life of the sensors by not sampling data when a consumption episode is unlikely to occur. For example, the system may increase the sampling rate of the sensors an hour or 30minutes before the expected time of the next consumption episode. In other words, the system may increase the sampling rate of one or more sensors during a time window that includes the predicted time of the next consumption episode. The duration of the window (e.g. how much earlier it begins before the expected time, and how much longer it continues after the expected time) may depend on the accuracy of previously predicted consumption episodes. For example, the user may tend to eat lunch at the same time every day, in which case the window may only begin 15 minutes before the expected time of the consumption episode. In another example, if the user tends to snack throughout the day such that the predicted time of the next consumption episode is relatively soon afterthe previous consumption episode, the window may span most of the time between successive consumption episodes. When predicting the time of the next consumption episode, the system may use data from one or more sensors arranged to detect information regarding the user’s external environment. Examples of external environmental information may be the location of the user (e.g. as determined by GPS) and what the user may be looking at (e.g. using the camera in combination with image recognition). For example, if the user is in a restaurant (e.g. as determined by the GPS) and is looking at a menu (e.g. as determined by image recognition from the camera data), the system may adjust the predicted time of the next consumption episode so that it falls within the next hour. To further save power, during an eating episode, the sensor may sample data periodically and for a predetermined amount of time. For example, the sensor may sample data for a period of 2 seconds, every 10 seconds. The gap between sampling periods may be determined by a minimum time for chewing a mouthful. In an extreme power saving mode, the system may prioritize determining the number of mouthfuls during the eating episode rather than dynamics of the jaw during chewing. So, the system may decide not to sample data multiple times during one mouthful. The gap between sampling periods may be personalized on the individual. For example, fora user who tends not to chew their food many times before swallowing, the gap between sampling periods may be relatively short so that the sensor can detect a mouth-opening event. In some cases, the gap between sampling periods may be determined based on a predicted time to chew a mouthful based on the bite amplitude during a mouthopening event. A large bite amplitude during an eating episode may suggest the user is taking a large bite of food which will take longer to chew than the average mouthful, which may be used to increase the gap between sampling periods for the sensor. General comments The apparatus and system in figures 2 and 3 are shown as comprising a number of functional blocks. This is schematic only and is not intended to define a strict division between different logic elements of such entities. Each functional block may be provided in any suitable manner. It is to be understood that intermediate values described herein as being formed by sensors or processors need not be physically generated by the sensor / processor at any point and may merely represent logical values which conveniently describe the processing performed by the sensor / processor between its input and output. The use of the term “image capture” need not imply that an image is formed or output by an optical flow processor and may refer generally to capturing contrast, texture or other information by means of an optical sensor which does not lead to an image being formed. Generally, any of the functions, methods, techniques or components described above can be implemented in software, firmware, hardware (e.g., fixed logic circuitry), or any combination thereof. The terms “module,” “functionality,” “component”, “element”, “unit", “block” and “logic” may be used herein to generally represent software, firmware, hardware, or any combination thereof. In the case of a software implementation, the module, functionality, component, element, unit, block or logic represents program code that performs the specified tasks when executed on a processor. The algorithms and methods described herein could be performed by one or more processors executing code that causes the processor(s) to perform the algorithms / methods. Examples of a computer-readable storage medium include a random-access memory (RAM), read-only memory (ROM), an optical disc, flash memory, hard disk memory, and other memory devices that may use magnetic, optical, and other techniques to store instructions or other data and that can be accessed by a machine. The terms computer program code and computer readable instructions as used herein refer to any kind of executable code for processors, including code expressed in a machine language, an interpreted language or a scripting language. Executable code includes binary code, machine code, bytecode, and code expressed in a programming language code such as C, Java or OpenCL. Executable code may be, for example, any kind of software, firmware, script, module or library which, when suitably executed, processed, interpreted, compiled, executed at a virtual machine or other software environment, cause a processor of the computer system at which the executable code is supported to perform the tasks specified by the code. A processor, computer, or computer system may be any kind of device, machine or dedicated circuit, or collection or portion thereof, with processing capability such that it can execute instructions. A processor may be any kind of general purpose or dedicated processor, such as a CPU, GPU, System-on-chip, state machine, media processor, an application-specific integrated circuit (ASIC), a programmable logic array, a field-programmable gate array (FPGA), or the like. A computer or computer system may comprise one or more processors. The applicant hereby discloses in isolation each individual feature described herein and any combination of two or more such features, to the extent that such features or combinations are capable of being carried out based on the present specification as a whole in the light of the common general knowledge of a person skilled in the art, irrespective of whether such features or combinations of features solve any problems disclosed herein. In view of the foregoing description it will be evident to a person skilled in the art that various modifications may be made within the scope of the invention.

Claims

1. A system for detecting a jaw activity of a user, the system comprising:wearable apparatus comprising a sensor located so as to, in use when the apparatus is worn by a user, monitor an area of skin whose movement is correlated with a facial muscle associated with movement of the jaw, the sensor being configured to capture time-varying data describing movement of the area of skin in two dimensions in a plane defined by the area of skin; anda processor configured to process the time-varying data by detecting a cyclic movement of the area of skin so as to identify a jaw activity of the user.

2. The system as claimed in claim 1, wherein the jaw activity is one or more of: mouth opening and closing, a jaw clench, chewing, movement of the tongue against a part of the mouth, or bruxism.

3. The system as claimed in any preceding claim, wherein the area of skin overlies one or more of: a masticator muscle, a zygomaticus major and / or minor muscle, a levator labii superioris muscle, a levator labii superioris alaeque nasi muscle, a levator anguli oris muscle, a risorius muscle, a buccinator muscle, an orbicularis oris muscle.

4. The system as claimed in any preceding claim, wherein the facial muscle is a masticator muscle and the masticator muscle is one or more of: a temporalis muscle, a masseter muscle, a medial pterygoid, and a lateral pterygoid.

5. The system as claimed in any preceding claim, wherein the cyclic movement comprises an oscillation of the area of skin in each of two directions in the plane concurrently.

6. The system as claimed in any preceding claim, wherein the apparatus comprises a plurality of sensors, each located so as to, in use when the apparatus is worn by the user, monitor a respective area of skin whose movement is correlated with a respective facial muscle associated with movement of the jaw, each sensor in the plurality of sensors being configured to capture time-varying data describing movement of the respective area of skin in two dimensions in a plane defined by that area of skin, wherein the processor is configured to process the time-varying data from each sensor in the plurality of sensors by detecting a respective cyclic movement of the respective area of skin and comparing the cyclic movements so as to identify the jaw activity.

7. The system as claimed in claim 6, wherein the processor is configured to identify the jaw activity based on a phase difference between the time-varying data of each sensor in the plurality of sensors.

8. The system as claimed in claim 6 or 7, wherein the plurality of sensors comprises a pair of sensors, each sensor in the pair being located so as to, in use when the apparatus is worn by the user, monitor a respective area of skin whose movement is correlated with a corresponding muscle on either side of the face of the user.

9. The system as claimed in any of claims 6 to 8, wherein the processor is configured to determine a measure of asymmetry in the jaw activity of the user in dependence on the comparison of the cyclic movements.

10. The system as claimed in claim 9, wherein the processor is configured to identify the jaw activity based on the measure of asymmetry.

11. The system as claimed in any of claims 6 to 10, wherein at least one of the plurality of sensors is located so as to, in use when the apparatus is worn by the user, monitor an area of skin associated with a masticator muscle of the user and at least one of the plurality of sensors is located so as to, in use when the apparatus is worn by the user, monitor an area of skin associated with one or more of: a zygomaticus major and / or minor muscle, a levator labii superioris muscle, a levator labii superioris alaeque nasi muscle, a levator anguli oris muscle, a risorius muscle, a buccinator muscle, a depressor anguli oris muscle, a depressor labii inferioris muscle, an orbicularis oris muscle or a mentalis muscle.

12. The system as claimed in any preceding claim, the apparatus further comprising a motion sensor and / or microphone, the processor being configured to use data captured by the motion sensor and / or microphone in combination with the time-varying data from the sensor to identify the jaw activity of the user.

13. The system as claimed in claim 12, wherein the motion sensor is configured to detect a head tilt of the user when the wearable apparatus is worn on the head of the user, wherein the processor is configured to infer ingestion of a pill based on a comparison between the time-varying data from the sensor relating to the jaw activity, and data from the motion sensor relating to the head tilt.

14. The system as claimed in any preceding claim, wherein the sensor is an optical flow sensor configured to capture samples of the area of skin and compare the samples apart in time so as to detect movement of the area of skin relative to the optical sensor.

15. The system as claimed in any preceding claim, wherein the processor is configured to identify the jaw activity based on one or more characteristics of the cyclic movement.

16. The system as claimed in any preceding claim, wherein the processor is configured to identify the jaw activity in dependence on one or more characteristics of an immediately preceding cyclic movement and / or an immediately following cyclic movement to the cyclic movement.

17. The system as claimed in any preceding claim, wherein the processor is configured to identify the jaw activity in response to detecting a plurality of cyclic movements.

18. The system as claimed in any of claims 15 to 17, wherein the one or more characteristics of the cyclic movement comprise one or more of: a maximum amplitude of the cyclic movement in either or both of the two dimensions, an average velocity of the skin movement, a maximum velocity of the skinmovement, a rate of change of velocity of the skin movement, an orientation of the cyclic movement, and a duration of the cyclic movement.

19. The system as claimed in claim 16, wherein the one or more characteristics comprises one or more of: an average frequency of the plurality of cyclic movements, a frequency variability of the plurality of cyclic movements, a change in maximum amplitude of the plurality of cyclic movements overtime.

20. The system as claimed in any preceding claim, wherein the jaw activity is mouth opening and closing, the processor being configured to identify mouth opening in response to detecting a cyclic movement having a maximum amplitude that exceeds a predefined threshold.

21. The system as claimed in any preceding claim, wherein the jaw activity is chewing, the processor being configured to identify chewing in response to detecting repeated cyclic movements of the area of skin.

22. The system as claimed in claim 21, wherein the processor is configured to infer swallowing in response to detecting an interruption in the repeated cyclic movements for a predetermined interval of time.

23. The system as claimed in claim 22, wherein the processor is configured to infer swallowing in response to detecting a reduction in a maximum amplitude of the repeated cyclic movements preceding the detected interruption.

24. The system as claimed in any preceding claim, wherein the jaw activity is pressing the tongue against a part of the mouth, and the processor is configured to infer which part of the mouth is being pressed by the tongue.

25. The system as claimed in any preceding claim, wherein the processor is configured to identify the jaw activity without using data from a sensor configured to sense motion of the area of skin in a direction substantially out of the plane defined by the area of skin.

26. The system as claimed in any preceding claim, wherein the processor is configured to process the time-varying data using a machine learning model trained to identify one or more cyclic movements in the time-varying data and attribute them to a jaw activity.

27. The system of any preceding claim, wherein the processor is configured to process the timevarying data at a neural network configured to operate on time-varying data describing movement of an area of skin so as to detect the cyclic movement and classify the cyclic movement as corresponding to the jaw activity.

28. The system as claimed in any preceding claim, the apparatus further comprising a lip sensor located so as to, in use when the apparatus is worn by the user, monitor an area of skin associatedwith a lip dilator and / or lip constrictor, the processor being configured to identify the jaw activity based on a comparison of data from the lip sensor with the time-varying data from the sensor.

29. The system as claimed in any preceding claim, wherein the processor is configured to predict when a future jaw activity will occur based on one or more of: time of day, time elapsed since most recent jaw activity, time of a previously identified jaw activity, type of previously identified jaw activities, a history of jaw activities of the user, and a user input.

30. The system as claimed in claim 29, wherein the processor is configured to control a sampling rate of one or more sensors in the wearable apparatus in dependence on the prediction of the future jaw activity.

31. The system as claimed in claim 29 or 30, wherein the future jaw activity is an eating episode.

32. A method for detecting a jaw activity of a user, the method comprising: capturing time-varying data describing movement of an area of skin in two dimensions in a plane defined by the area of skin, the movement of the area of skin being correlated with a facial muscle associated with movement of the jaw, said capturing being performed using a sensor located so as to monitor said area of skin; andprocessing the time-varying data by detecting a cyclic movement of the area of skin so as to identify a jaw activity of the user.

33. The method of claim 32, wherein the processing is performed using a machine learning algorithm that takes as an input the time-varying data describing movement of the area of skin and produces as an output a signal identifying the jaw activity.

34. The method of claim 32 or 33, wherein the sensor is located on a wearable apparatus so as to, in use when the apparatus is worn by the user, monitor the area of skin.