Monitoring system and monitoring method

Abstract

Systems and methods for monitoring a user during a physical activity are disclosed. In one arrangement, a monitoring system comprises a wearable sensor worn by a user and a data processing system. The wearable sensor performs measurements to obtain measurement data containing information about respiration. The data processing system processes the measurement data to derive information about a level of exertion of the user during a physical activity and output during the physical activity the derived information about the level of exertion.

Description

[0001] MONITORING SYSTEM AND MONITORING METHOD

[0002] The present disclosure relates to systems and methods for monitoring a person during physical activity.

[0003] Wearable devices for monitoring physiological parameters such as heart rate during physical activity are known. It is also known to use such parameters to generate metrics that represent a level of exertion during exercise. An example of such a metric is the Rate of Perceived Exertion (RPE) in Borg’s exertion scale, which defines a scale of levels from 6-20, 6 representing no exertion and 20 representing maximal exertion. The levels of the Borg scale may be set based on a percentage of maximum heart rate for a user.

[0004] Although heart rate is easily accessible with widespread wearable devices, it is not always directly associated with RPE. For example, the decline in maximum heart rate with age is associated with a higher RPE at any given heart rate. It has also been shown that pharmacological intervention to either speed up or slow down heart rate does not alter RPE. Furthermore, in hot environments, the heart rate increases, whereas RPE may not necessarily increase. These external and internal factors affect the accuracy and reliability with which the RPE is represented.

[0005] It is an object of the invention to provide systems and methods that support improved monitoring of physical activity.

[0006] According to an aspect of the invention, there is provided a monitoring system, comprising: a wearable sensor configured to be worn by a user; and a data processing system, wherein: the wearable sensor is configured to perform measurements to obtain measurement data containing information about respiration; and the data processing system is configured to process the measurement data to derive information about a level of exertion of the user during a physical activity and output during the physical activity the derived information about the level of exertion. In contrast to typical health monitors used during exercise, which rely on measuring heart rate, the monitoring system uses information about respiration in order to derive and output information about a level of exertion of a user during a physical activity. The monitoring system exploits a strong correlation between a perceived level of exertion (which may be represented as an RPE value) and variation of respiration such as respiration rate over a wide range of conditions, including high-strain activities such as exercise, thereby providing information about a level of a user during a physical activity in an improved manner.

[0007] There is also provided a monitoring system for monitoring respiration, comprising: a wearable sensor configured to be worn by a user; and a data processing system, wherein: the wearable sensor is configured to perform measurements to obtain measurement data containing information about respiration; and the data processing system is configured to filter the measurement data to suppress contributions to the measurement data from movements of the user other than movements associated with respiration. The monitoring system extracts relevant information about respiration (e.g., respiration rate) over a wide range of activity types, even those involving high levels of movement, in a highly reliable and robust manner.

[0008] There is also provided a method of monitoring, comprising: performing measurements to obtain measurement data containing information about respiration at a wearable sensor worn by a user; and processing the measurement data to derive information about a level of exertion of the user during a physical activity at a data processing system and outputting during the physical activity the derived information about the level of exertion.

[0009] Embodiments of the disclosure will be further described by way of example only with reference to the accompanying drawings.

[0010] Figure 1 schematically depicts an example monitoring system;

[0011] Figure 2 schematically depicts an example implementation of a wearable electronics unit and wearable sensor of the monitoring system of Figure 1;

[0012] Figures 3 to 5 are schematic plan views showing example implementations of the sensor device; and

[0013] Figures 6 and 7 are schematic cross sectional views showing example ways of attaching wearable sensors to a user’s skin.

[0014] The present disclosure relates to a monitoring system and associated method. The system and method use a wearable sensor to perform measurements to obtain measurement data.

[0015] Figure 1 depicts an example of the monitoring system 2. The system 2 comprises a wearable sensor 4. The wearable sensor 4 is configured to be worn by a user. The system 2 further comprises a data processing system 6. The wearable sensor 4 may be configurated to exchange data with (i.e., be in communication with) the data processing system 6. The exchange of data may be performed wirelessly, via a wired connection, or both.

[0016] The wearable sensor 4 is configured to perform measurements to obtain measurement data. The measurement data contains information about respiration. In principle, any measurement technique that produces measurement data containing information about respiration may be used. A preferred technique that uses a piezoelectric or piezoresistive device is described in further detail below.

[0017] The data processing system 6 is configured to process the measurement data to derive information about a level of exertion of the user during a physical activity. The data processing system 6 outputs, during the physical activity, the derived information about the level of exertion.

[0018] In contrast to typical health monitors used during exercise, which rely on measuring heart rate, the present system 2 uses information about respiration. The inventors have found a strong correlation between a perceived level of exertion (which may be represented as an RPE value) and variation of respiration such as respiration rate over a wide range of conditions, including high-strain activities such as exercise. Furthermore, the inventors have found that it is possible to extract relevant information about respiration (e.g., respiration rate) over a wide range of activity types, even those involving high levels of movement, in a highly reliable and robust manner.

[0019] The measurements may comprise regularly determining a state of the wearable sensor 4 (e.g., via sampling at a selected rate). The measurements may be performed in response to, and / or under the control of, one or more signals from the data processing system 6. A sampling rate of the measurements may be constant and / or dynamically adjusted. The data processing system 6 may be configured to set a sampling rate based on a preset value and / or user input. Alternatively or additionally, the data processing system 6 may adaptively set a sampling frequency based on the derived information about the level of exertion of the user (e.g., to have a higher sampling rate for higher levels of exertion, to account for higher frequency respiration, and vice versa). In some implementations, the data processing system 6 is configured to repeatedly update the output of the derived information about the level of exertion during the physical activity, optionally substantially in real time or quasi-real time (e.g., with a small delay between performing a measurement and outputting corresponding information about a level of exertion, for example of less than about 10 seconds).

[0020] The monitoring system 2 is particularly suitable for use with physical activities that involve a significant amount of effort, such as physical activities undertaken with an aim to improve or maintain fitness. However, the monitoring system 2 may be used in conjunction with any physical activity where outputting information derived from respiration rate and volume, such as a level of exertion, would be useful. Non-limiting examples include aerobics, basketball, handball, volleyball, tennis, gymnastics, ballet, pilates and yoga.

[0021] The wearable sensor 4 may be configured to be worn at an attachment location on the chest or abdomen. For example, the wearable sensor 4 may be provided in the form of a low-profile patch that is configured to be worn on the chest or abdomen. Positioning the wearable sensor 4 on the chest or abdomen allows the wearable sensor 4 to measure motion due to respiration (e.g., chest motion caused by respiration) particularly effectively. In such cases, the wearable sensor 4 may be configured to generate a real time signal that represents (e.g., is proportional to) chest expansion and contraction, thereby providing the information about respiration. In some embodiments, the wearable sensor 4 may be used to obtain measurement data based on the position of the surface of the chest relative to an initial position of the surface of the chest, in order to measure chest motion caused by respiration.

[0022] It is not essential for the wearable sensor 4 to be worn on the chest or abdomen. The wearable sensor 4 can be configured to be worn by a user at any position on the user that results in the measurement data containing information about respiration. Any position on the user that moves during respiration in a way that allows respiration to be measured with sufficient accuracy and / or reliability may be used.

[0023] In some implementations, the wearable sensor 4 is configured to be attached at the attachment location by adhesion to the skin. This approach has been found to promote high levels of accuracy in the measurement data. In one example implementation, the wearable sensor 4 is attached to a user’s skin with medical grade adhesive tape, such as medical grade double-sided adhesive tape. Alternatively or additionally, the wearable sensor 4 may be held in contact with (e.g., attached to) the user’s skin using other techniques and / or materials.

[0024] The data processing system 6 comprises one or more devices that are configured to receive and process measurement data received from the wearable sensor 4 and to provide one or more outputs. The data processing system 6 may in general comprise any combination of data processing hardware, firmware, and software that is capable of providing the required functionality (i.e., including processing of the measurement data to derive information about the level of exertion). The data processing hardware may be provided in a single device or distributed between any number of distinct computing devices (e.g., computing devices provided at different locations). The data processing device 6 may be configured to perform the processing of the measurement data locally (e.g., using one or more device elements worn by the user and / or carried by the user), remotely (e.g., at a remote server accessed via the internet), or via a combination of the two.

[0025] In the example of Figure 1, the data processing system 6 comprises a wearable electronics unit 10. The wearable electronics unit 10 is configured to be worn by a user. In some implementations, the wearable electronic unit 10 is configured to be worn on (e.g., supported by) an item of clothing such as a belt. The system 2 comprises a data connection (e.g., wireless, wired, or a combination of the two) between the wearable sensor 4 and the wearable electronics unit 10. The data connection allows transmission of data at least from the wearable sensor 4 to the wearable electronics unit 10.

[0026] The wearable electronics unit 10 may be configured to perform all of the processing to derive the information about the level of exertion. Alternatively, the wearable electronics unit 10 may be configured to perform only part of the processing and the rest of the processing may be performed elsewhere, such as on a mobile device 8 where this is provided (e.g., as in the example of Figure 1 and discussed below) or on a computing system, such as a remote server, at a different location. Distributing the processing such that at least part of the processing is performed elsewhere than at the wearable electronics unit 10 and wearable sensor 4 reduces data processing demands at elements that are worn by the user, thereby improving comfort by reducing the weight and / or local heating associated with such elements. Any computing device that contributes to the processing of the measurement data to derive the information about the level of exertion may be considered as being part of the data processing system 6.

[0027] In some implementations, the wearable electronics unit 10 does not form part of the data processing system 6. In such implementations, the wearable electronics unit 10 does not contribute directly to processing the measurement data to derive information about the level of exertion. The wearable electronics unit 10 may in this case perform relatively minimal processing of a signal from the wearable sensor, such as amplification and / or analog-to-digital conversion. The wearable electronics unit 10 may be configured in this case to communicate (e.g., wirelessly) with the data processing system 6, for example to send measurement data to the data processing system 6.

[0028] The monitoring system 2 may comprise a flexible connection arrangement mechanically connecting the wearable sensor 4 and the wearable electronics unit 10. The flexible connection arrangement may be configured to allow a distance between the wearable sensor 4 and the wearable electronics unit 10 to be varied freely within a predetermined range prior to attachment of the wearable sensor 4 and the wearable electronics unit 10 to the user. In some implementations, the data connection between the wearable sensor 4 and the wearable electronics unit 10 comprises one or more wires (e.g., cables) that not only provide electrical connection between the wearable sensor 4 and the wearable electronics unit 10, but also provide, at least partly, the flexible mechanical connection arrangement. In other examples, the wearable sensor 4 and the wearable electronics unit 10 are connected to each other by a flexible mechanical connection without the mechanical connection forming part of the data connection. In some implementations, the wearable sensor 4 and the wearable electronics unit 10 are mechanically separated from each other and data is transferred from the wearable sensor 4 to the wearable electronics unit 10 wirelessly.

[0029] The wearable sensor 4 and the wearable electronics unit 10 may be configured to be attached at different locations on the user’s body. The different locations may be separated from each other. Attaching the wearable sensor 4 and the wearable electronics unit 10 at separated locations reduces demands for processing of measurement data at the location of the wearable sensor 4. The reduction of processing at the location of the wearable sensor 4 makes it possible for the wearable sensor 4 to be made lighter, more compact and / or less prone to heating, as well as reducing potential interference between operation of the sensor and operations associated with data processing. Comfort and / or performance may therefore be improved.

[0030] In other implementations, the wearable sensor 4 and the wearable electronics unit 10 are integrated to form a monolithic device. For example, the wearable sensor 4 and the wearable electronics unit 10 may be joined in a small wearable patch the size of a band aid. In such arrangements, the wearable sensor 4 and the wearable electronics unit 10 may be joined in a wearable patch having a surface area of less than 100 cm2, preferably less than 50 cm2, more preferably less than 25 cm2and yet more preferably less than 10 cm2. The wearable patch may comprise one or more adhesive portions enabling adhesion of the wearable patch to a user’s skin.

[0031] In the example of Figure 1, the data processing system 6 further comprises a mobile device 8. The mobile device 8 is separate from the wearable electronics unit 10. The mobile device 8 may be worn or not worn. The mobile device 8 may be any mobile electronics device capable of performing computing operations, such as a smartphone, smartwatch, or laptop. The mobile device 8 communicates, wirelessly or via a wired connection, with the wearable electronics unit 10. The wearable sensor 4 communicates, wirelessly or via a wired connection, with the wearable electronics unit 10. Distributing processing between the wearable electronics unit 10 and the mobile device 8 reduces demands on the wearable electronics unit 10, which may allow the wearable electronics unit 10 to be more compact and / or less prone to heating, thereby improving comfort.

[0032] In an example, the wearable sensor 4 is configured to be adhered to the user’s skin and the wearable electronics unit 10 is configured to be mounted on an item of clothing. Adhering the wearable sensor 4 directly to skin has been found to provide high performance. The wearable electronics unit 10 is typically heavier than the wearable sensor 4 and / or may produce heat during operation. Mounting the electronics unit 10 on an item of clothing provides improved comfort relative to incorporating corresponding functional components into the wearable sensor 4 that is adhered to skin. Alternatively, the wearable electronics unit 10 may be configured to be adhered to the user’s skin, for example through the use of adhesive medical grade tape, or through the use of any one or more appropriate alternative material and / or technique.

[0033] A wearable sensor 4 is thus provided that performs measurements to obtain measurement data containing information about respiration. The measurement data is then processed by the data processing system 6, which can take any of the forms described above. In the example of Figure 1, the measurement data is received by a wearable electronics unit 10, which performs amplification and / or analog-to-digital conversion, before being transmitted to a mobile device 8. In this example, the mobile device 8 processes the measurement data to derive information about a level of exertion of the user during physical activity. The derived information can be output by the mobile device 8 directly to a user and / or indirectly to one or more further devices and / or users.

[0034] As described above, the data processing system 6 may be configured to derive information about a level of exertion by processing the measurement data from the measurement sensor. In some implementations, the data processing system 6 is configured to obtain a respiration rate from the measurement data and use the obtained respiration rate to derive the information about the level of exertion. The respiration rate has been found to be reliably correlated with level of exertion and can be extracted with high efficiency from the measurement data.

[0035] The data processing system 6 may be configured to use the obtained respiration rate and a user’s maximum respiration rate (fRmax) to derive the information about the level of exertion. The user’s fRmax may be used for example to calibrate a relationship between the obtained respiration rate and the level of exertion. For example, a given obtained respiration rate may correspond to a higher level of exertion for a user with a relatively low fRmax in comparison to a user with a higher fRmax (who may be younger or fitter for example). The fRmax may thus be used to convert an obtained respiration rate to a level of exertion, which may be represented for example as a Rate of Perceived Exertion (RPE) value.

[0036] The data processing system 6 may be configured to output (e.g., via mobile device 8 or via an exercise machine such as a treadmill) instructions for a sequence of physical activities configured to progressively increase physical demands on the user and to derive therefrom an estimate of the user’s fRmax. Various algorithms may be used to obtain a value representing fRmax. In one implementation, fRmax is defined as the highest 60- second average during strenuous exercise. The sequence of physical activities for obtaining fRmax may thus be designed to include suitable strenuous exercise and to allow one or more 60-second averages of respiration rate to be obtained.

[0037] The derived information about the level of exertion is output during the physical activity of a user. The derived information may be output via any suitable mechanism, for example through display via an application (app) running on the mobile device 8 or via an app running on an exercise machine such as a treadmill. The information about the level of exertion may comprise an obtained respiration rate and / or a measure of a rate of perceived exertion, RPE.

[0038] In an example implementation, the data processing system 6 is configured to generate exercise guidance based on the derived information about the level of exertion (e.g., RPE) and output the exercise guidance to the user during the physical activity. The exercise guidance may comprise instructions for a user to follow. Additionally or alternatively, the exercise guidance may comprise automatic control of one or more devices based on the derived information about the level of exertion, such as control of one or more exercise machines (e.g., to control a level of power input from the user that is demanded by the machine, such as by controlling a speed and / or incline of a treadmill, and / or to control a resistance applied by the machine to resist motion by a user, such as to resist turning of pedals of an exercise bike).

[0039] In an example implementation, the exercise guidance comprises one or more indications to adapt a level of exertion issued at one or more respective times during the physical activity, each indication comprising an indication to increase or decrease a level of exertion towards a target level of exertion.

[0040] In an example implementation, an app, running for example on a mobile device 8 of the data processing system 6 or on another computing device, allows a user to select an exercise regimen, such as weight loss, muscle building, or cardio, and continuously directs the user towards optimal exertion during exercise based on the derived information about the level of exertion (e.g., RPE). Alternatively or additionally, the derived information about the level of exertion (e.g., RPE) may be output to other users and / or devices, such as to a coach. Although the derived information about the level of exertion is output during the physical activity, the information may also be stored and subjected to more detailed analysis at a later time, such as for advanced exercise strain analysis.

[0041] Alternatively or additionally, the app may be configured to derive a performance metric of a user while the user is participating in the physical activity. The performance metric may numerically represent any aspect of performance of the user, such as a speed of movement, for example corresponding to a speed of running, cycling, rowing, and / or performance of gym or exercise class activities. The performance metric may be used, optionally in combination with the derived information about the level of exertion, to provide a combined output that includes a measure of a rate of perceived exertion and information about performance (such as the performance metric). The output may optionally provide guidance to a user to adjust (e.g., reduce or increase a speed of) a movement, such as of a body part, to reduce or avoid fatigue or an injury risk or improve performance of the activity.

[0042] The monitoring system 2 may be adapted such that an app, running for example on a mobile device 8 forming part of the data processing system 6, implements an algorithm to evaluate and show the derived level of exertion (e.g., as an RPE value). The app may monitor fR in real time, for example by applying an “advanced counting method” and evaluating fR, averaging over 10-second time intervals. The app may continuously show the value of RPE, calculated based on a measured fR and fRmax, for example based on the percentage of fRmax that the measured fR represents. RPE values may be defined and / or displayed in various ways. In one implementation, RPE values in a range 8-11, corresponding to light strain, are shown on a green background, values in a range 12-16, corresponding to moderate to heavy strain, are shown on an orange background, and values in a range of 17-20, represented very hard to extremely hard exertion, are shown on a red background. The RPE scale may be based on Borg’s exertion scale.

[0043] Figure 2 depicts an example implementation of a wearable electronics unit 10 and wearable sensor 4 comprising a sensor device 16, which may be a piezoelectric device or a piezoresistive device. The wearable electronics unit 10 of Figure 2 may be configured to provide any of the functions associated with the wearable electronics unit 10 described above with reference to Figure 1. The wearable sensor 4 may provide any of the functions associated with the wearable sensor 4 described above with reference to Figure 1. The sensor device 16 may take any of the forms described below with reference to Figures 3 to 7.

[0044] A signal from the sensor device 16 is communicated to the wearable electronics unit 10. The sensor device 16 produces a signal in response to mechanical distortion of the sensor device 16 via the piezoelectric effect or the piezoresistive effect. The signal produced by the sensor device 16 is communicated via one or more electrical connections to one or more electrical circuits in the wearable sensor 4 and / or wearable electronics unit 10 for further processing. The wearable sensor 4 is configured and positioned on the user’s body in a way that results in movements of the user’s body due to respiration causing a distortion of the sensor device 16 that is detectable via the piezoelectric effect or the piezoresistive effect.

[0045] The sensor device 16 may comprise a piezofilm sensor. The piezofilm sensor may be configured to deform when attached to the attachment location and thereby conform to a local curvature of the user’s body at the attachment location. The piezofilm sensor is a thin-film component. Electrical properties of the component change in response to bending of the component via the piezoelectric or piezoresistive effect. The changing electrical properties may be measured as changes in capacitance or resistance. The electrical properties of the component are measured via one or more electrical connections from the piezoelectric or piezoresistive element. In an implementation, the sensor device 16 is a piezoelectric device comprising a piezofilm sensor having electrical contacts formed on top and bottom opposing sides of the piezofilm sensor forming two plates of a capacitor. The electrical contacts are connected to an electrical circuit, which may be in the wearable sensor 4 and / or wearable electronics unit 10, for further processing via electrical connections made to each of the top and bottom electrodes of the piezofilm sensor. The electrical connections may be provided in any appropriate manner, such as in the form of wires soldered to each of the top and bottom electrodes of the piezofilm sensor. This enables changes in the capacitance of the piezofilm sensor to be measured in response to bending the piezofilm sensor. In another implementation, the sensor device 16 comprises a piezoresistive element, such as a graphene film, which may also be considered a piezofilm. Electrical contacts are formed on a surface of the graphene film and are electrically connected to an electrical circuit, which may be in the wearable sensor 4 and / or wearable electronics unit 10, for further processing, for example by wires soldered to the electrical contacts. Such an arrangement allows changes in resistance to be measured as the piezoresistive element deforms. In further implementations, the electrical properties of the component are measured using any suitable additional or alternative electrical connection. The piezofilm allows accurate measurements to be made of local changes in shape of the surface to which the piezofilm is attached (e.g., adhered). The piezofilm may comprise any suitable piezoelectric material, such as a ceramic, a polymer, or a composite material. Additionally, or alternatively, the piezofilm may comprise any suitable piezoresistive material, such as graphene.

[0046] The use of a piezoelectric or piezoresistive sensor device 16 in the wearable sensor 4 has been found to provide a particularly effective implementation of the monitoring system 2. Piezoelectric and piezoresistive devices provide a direct response signal in contrast to other devices that provide indirect signals that require complex processing. Further, piezoelectric and piezoresistive devices provide fast responses that enable reliable real-time detection of signal changes due to respiration-induced motion (e.g., chest motion). Piezoelectric and piezoresistive devices are furthermore implementable in a highly unobtrusive manner, for example in a highly compact manner (e.g., in the form of a flat film), in a flexible manner (e.g., to conform with the contours of the body and flex easily with movements), and in a lightweight manner. The inventors have found that piezoelectric and piezoresistive devices 16 can provide highly accurate and robust measurements of respiration over a wide range of physical activities, including those involving large amounts of movement where it might have been expected that such movements would interfere with the measurements of respiration rate.

[0047] In one example implementation, the piezofilm sensor is configured to have a capacitance between about 0.3 nF and about 4.0 nF, optionally between about 0.5 nF and about 3.5 nF, but piezofilm sensors having other capacitances can be used as long as they can provide the required functionality (e.g., including performing measurements to obtain measurement data containing information about respiration). The piezofilm sensor may comprise two leads for connectivity with the wearable electronics unit 10. In one implementation, the monitoring system 2 comprises a wearable electronics unit 10 in the form of a small 3D-printed electronics package that is connected to the wearable sensor 4 with cables.

[0048] In the example of Figure 2, the wearable electronics unit 10 comprises an amplifier 11 that receives and amplifies the signal from the sensor device 16. In the example shown, the sensor device 16 provides an output directly to the amplifier 11. However, in other configurations, additional electronic components may be provided, either separately, or as part of the sensor device 16, wearable electronics unit 10 and / or amplifier 11, in order to process the signal from the sensor device 16 for the amplifier 11. In the example shown, the amplifier 11 comprises an operational amplifier (op-amp), but any other configuration that provides suitable amplification can be used. The wearable electronics unit 10 further comprises an analog-to-digital converter 14 that converts the amplified analog signal to a digital signal. The wearable electronics unit 10 further comprises a microcontroller 12 and an energy source (not shown). In one example implementation, the microcontroller 12 supports Bluetooth 5.0 connectivity and operates at up to 64 MHz. The microcontroller 12 may be configured to allow measurement data to be transmitted in real time from the wearable sensor 4 to the wearable electronics unit 10 via a Bluetooth Low Energy (BLE) connection of the microcontroller 12. Any other suitable wireless transmission protocol may be used. In this implementation, the microcontroller 12 consumes 5 microamperes of current in deep sleep mode. The microcontroller 12 has 1 MB of flash and 256 kB RAM in this particular example but other configurations may be used. The energy source may be a rechargeable lithium-ion battery for example. Other configurations are possible as long as the functionality required of the wearable electronics unit 10 is provided (e.g., to enable a signal from a wearable sensor 4, such as a signal from a piezoelectric or piezoresistive sensor device 16 of a wearable sensor 4, to be processed and communicated as necessary within a data processing system 6). Such other configurations may include configurations comprising only a subset of an amplifier, an analog-to-digital converter, a microcontroller, and an energy source.

[0049] The microcontroller 12 may use wireless connectivity to wirelessly transmit the digital signal from the analog-to-digital converter 14 to an external device, such as the mobile device 8 depicted in Figure 1 and discussed above. A custom app may be built for providing the required functionality. The app may be built on the cross-platform technology Xamarin, which can easily be applied to Android and iOS. The app may connect to the wearable electronics unit 10, read data in real time, and display the data, for example on a graph, at a display of a mobile device 8.

[0050] Figures 3 to 7 depict different example implementations of wearable sensors 4 comprising sensor devices 16, which may be piezoelectric or piezoresistive sensor devices.

[0051] In some implementations, as exemplified in Figure 3, the wearable sensor 4 has a piezoelectric or piezoresistive sensor device 16 with a piezosensor film having a simple polygonal shape, such as a rectangle, in plan view. Other polygonal shapes can be used, including regular polygons such as squares or triangles, or circular or oval shapes. High aspect ratio shapes may provide higher sensitivity to bending than lower aspect ratio shapes and therefore be particularly effective for a given surface area of contact with the skin. Figure 3 schematically provides an example in the form of a rectangle. Optionally, shapes having an aspect ratio (e.g., a ratio of a largest dimension, such as a length of a rectangle, to a smallest dimension, such as a width of a rectangle) of at least 2, optionally at least 4, optionally at least 6, optionally at least 10.

[0052] In some implementations, as exemplified in Figure 4, the wearable sensor 4 may comprise a piezoelectric or piezoresistive sensor device 16 having a more complex shape in plan view. In the example of Figure 4, the sensor device 16 has a serpentine form in plan view. Configuring the sensor device 16 to have such a form may increase the aspect ratio of the sensor device 16 for a given device width and length, and may improve a piezoelectric or piezoresistive response and / or conforming of the device to the user’s skin, which may also contribute to improved measurements and / or reliability and / or improved comfort for the user. The piezoelectric or piezoresistive sensor may have a “dog bone” or “hierarchical dog bone” form in plan view.

[0053] The area of the sensor device 16 is not generally limited. The area needs to be large enough to provide measurement data that contains the required information about respiration and not to be so large that it becomes uncomfortable to wear and / or damages the skin. The inventors have found that a good balance of properties can be found for piezoelectric or piezoresistive sensor devices 16 having an area in plan view in the range of about 10 mm2to about 10000 mm2, optionally in a square, rectangular, or serpentine form.

[0054] In some implementations, as exemplified in Figures 5 to 7, the sensor device 16 comprises a plurality of through holes 18. The through holes 18 are configured to provide respective fluidic connections from the user’s skin to the environment outside of the wearable sensor 4 while the wearable sensor 4 is worn by the user. The fluidic connections promote evaporation of sweat from the user’s skin during the physical activity. Allowing sweat to evaporate during exercise reduces sweat accumulation and associated skin discomfort, which promotes long-term wearability and / or enhances user comfort during intense physical exercise.

[0055] Figure 5 is a plan view showing an example distribution of through holes 18. The sizes, shapes, density and positioning of the through holes are purely exemplary. In other implementations, any one or more of the sizes, shapes, density and positioning of the through holes 18 may be different. For example, instead of all of the through holes 18 having the same shape and size, as in Figure 5, two or more sets of through holes having different shapes and / or different sizes may be provided.

[0056] In some implementations, the sensor device 16 is a piezoresistive sensor device comprising a laser-induced graphene (LIG) element. Piezoresistive devices 16 comprising LIG elements may be manufactured by a direct laser writing process on flexible substrates, such as biocompatible material substrates, thereby enabling customizable patterns and designs. Advantageously, such fabrication is low-cost, environmentally friendly and scalable for mass production, making it suitable for widespread adoption in consumer fitness wearables. The LIG may be configured to provide a degree of porosity in the wearable sensor 4 and thereby provide advantages similar and / or complementary to those described above in relation to the through holes 8. Long term wearability and / or comfort may be particularly enhanced in implementations in which the sensor device 16 comprises LIG and through holes 18. Like piezoelectric and piezoresistive sensors 16 more generally, as discussed above, the LIG element may take various sizes and shapes. The LIG element may, for example, have an area in plan view in the range of about 10 mm2to about 10000 mm2, which has been found to provide a good balance of properties. Optionally, the LIG element may be provided in a square, rectangular (e.g., as depicted in Figure 3), or serpentine (e.g., as depicted in Figure 4) form.

[0057] Implementing the sensor element 16 with a LIG element may provide a particularly lightweight device, which improves comfort and reduces interference with the user’s natural movements, contributing to a more seamless integration into daily fitness routines.

[0058] Figures 6 and 7 depict cross sectional views of example wearable sensors 4 attached to a user’s skin 22. In each case, the sensor device 16 may take any of the forms described above, for example with reference to Figures 2-5. In the examples shown, the piezoelectric or piezoresistive sensor devices 16 comprise through holes 18.

[0059] Figure 6 exemplifies one approach for attaching the sensor device 16 to skin using an adhesive layer 20. The adhesive layer 20 may be a tape that simultaneously adheres to the sensor device 16 and a user’s skin 22 whilst holding the sensor device 16 against the user’s skin 22. As mentioned above, a medical grade adhesive tape may be used. In the example shown, the attachment is achieved by arranging for an adhesive surface (facing downwards in the orientation of Figure 6) of the adhesive layer 20 to overlap across at least a portion of a peripheral edge of the sensor device 16, thereby simultaneously contacting both a portion (or all) of one side of the sensor device 16 and a portion of skin directly adjacent to the sensor device 16. In the example shown, the adhesive layer 20 extends across the entirety of the sensor device 16 and a closed loop of skin surrounding the sensor device 16 to provide a secure connection. In this example, the adhesive layer 20 encapsulates the sensor device 16 between the adhesive layer 20 and the skin 22.

[0060] Figure 7 exemplifies another approach for attaching the sensor device 16 to skin using an adhesive layer. In this example, in contrast to the arrangement of Figure 6, an adhesive layer 20 is positioned between the sensor device 16 and the user’s skin 22. The adhesive layer 20 may thus comprise double-sided adhesive tape. As mentioned above, a medical grade double-sided adhesive tape may be used. In a similar manner to the wearable sensor 4 described with reference to Figure 6, whilst the adhesive layer 20 is shown to extend across the entirety of one side of the sensor 16, this is not essential. In other implementations the sensor device 16 is attached to the skin 22 using one or more adhesive layers 20 that do not extend across the entirety of one side of the sensor device 16.

[0061] Regardless of how the adhesive layer 20 is used to attach the sensor device 16 to the skin (e.g., single-sided as in Figure 6 or double-sided as in Figure 7), the adhesive layer 20 may further be configured to enable air flow to and from the user’s skin 22 through the adhesive layer 20. The adhesive layer 20 may thus provide fluidic connections in a path between the user’s skin and an environment outside of the wearable sensor 4 to promote evaporation of sweat from the user’s skin during the physical activity. The adhesive layer 20 may thus be breathable (e.g., porous).

[0062] Whilst the approaches exemplified at Figures 6 and 7 show a single layer sensor device 16, in other implementations, the sensor device 16 may comprise any number of suitable layers. In an implementation, the sensor device 16 is a piezoresistive sensor device 16 comprising a LIG element and a thin film substrate, such as a polymer film. The polymer film may be a biocompatible polymer film. In such implementations, the sensor device 16 may comprise through holes 18 passing through the LIG element and thin film substrate to promote evaporation of sweat from the user’s skin during physical activity.

[0063] The wearable sensor 4 described above with reference to Figures 1 to 7 is configured to obtain measurement data containing information about respiration. In some implementations of this functionality, the measurement data comprises first motion data and second motion data. In such cases, the wearable sensor 4 comprises a first measuring device configured to obtain the first motion data using a first measurement modality and a second measuring device configured to obtain the second motion data using a second measurement modality different from the first measurement modality. The data processing system 6 is configured to use a combination of the first motion data and the second motion data to derive the information about the level of exertion.

[0064] Using motion data obtained from different measurement modalities provides more detailed information about respiration, thereby enabling more accurate derivation of information about the level of exertion of a user. In an example implementation, the data processing system 6 is configured to use the second motion data to suppress contributions to the derived information about the level of exertion from components of the first motion data that correspond to movements of the user other than movements associated with respiration. The suppression of contributions from movements other than those associated with respiration, such as movements due to running or cycling etc., reduces noise and thereby improves accuracy.

[0065] In the above example, the second motion data is used to suppress contributions to the derived information about the level of exertion from components of the first motion data that correspond to movements of the user other than movements associated with respiration. However, the data processing system 6 may use the combination of the first motion data and the second motion data in any appropriate manner that improves derivation of the information about the level of exertion. For example, the first motion data and the second motion data may be configured to provide measurements of respiration rate that rely on independent measurement modalities, thereby providing redundancy and thus robustness to variations in operational circumstances, such as weather conditions or particular types of physical activity, for which the different measurement modalities may have different vulnerabilities. In an example implementation, the first measuring device comprises a piezoelectric or a piezoresistive device and the second measuring device comprises an accelerometer.

[0066] In some implementations, additionally or alternatively to the functionality described above using first and second motion data, the data processing system 6 is configured to filter the measurement data to suppress contributions to the measurement data from movements of the user other than movements associated with respiration.

[0067] In one implementation, the filtering of the respiration data comprises suppressing contributions above an upper threshold frequency relative to contributions below the upper threshold frequency. Filtering measurements based on such an upper threshold frequency has been found to reliably and efficiently suppress unwanted contributions to the measurement data and thereby promote more accurate and / or efficient derivation of the information about the level of exertion.

[0068] The upper threshold frequency may desirably be in the range of between 30 and 80 breaths per minute. This range has been found to provide good performance.

[0069] In an example implementation of this type, the data processing system 8 is configured to derive a respiration rate from the respiration data and use the respiration rate to adapt the upper threshold frequency dependent on the derived respiration rate. Dynamically adapting the upper threshold frequency has been found to provide improved filtering by allowing the filtering to respond to different stages of the user’s activity and thereby provide more optimal filtering for each stage. For example, when a respiration rate is relatively low due to a relatively low level of exertion, it is possible to safely lower the upper threshold frequency, thereby supressing a larger proportion of data that is not indicative of respiration, without risking excessive (or any) suppression of data that is relevant to respiration (because data relevant to respiration will be at a lower frequency when the underlying respiration rate is lower). Conversely, when the respiration rate is relatively high, due to a higher level of exertion, raising the upper threshold frequency will reduce a risk of suppression of data relevant to respiration rate, which may exist at higher frequencies that when the underlying respiration rate is lower.

[0070] Alternatively or additionally, a lower threshold frequency may be used in a similar manner. Thus, in one implementation, the filtering of the respiration data comprises suppressing contributions below a lower threshold frequency relative to contributions above the lower threshold frequency. Filtering measurements in such a way based on lower threshold frequency has also been found to reliably and efficiently suppress unwanted contributions to the measurement data and thereby promote more accurate and / or efficient derivation of the information about the level of exertion.

[0071] The lower threshold frequency may desirably be in the range of between 8 and 30 breaths per minute.

[0072] In an example implementation of this type, the data processing system 6 is configured to derive a respiration rate from the respiration data and use the respiration rate to adapt the lower threshold frequency dependent on the derived respiration rate. Dynamically adapting the lower threshold frequency has been found to provide improved filtering for similar reasons to the improved filtering obtained by dynamically adapting the higher threshold frequency discussed above. For example, improved filtering may be obtained by allowing the filtering to respond to different stages of the user’s activity and thereby provide more optimal filtering for each stage. For example, when a respiration rate is relatively high due to a relatively high level of exertion, it is possible to safely increase the lower threshold frequency, thereby suppressing a larger proportion of data that is not indicative of respiration, without risking excessive (or any) suppression of data that is relevant to respiration (because data relevant to respiration will be at a higher frequency when the underlying respiration rate is higher). Conversely, when the respiration rate is relatively low, due to a lower level of exertion, lowering the upper threshold frequency will reduce a risk of suppression of data relevant to respiration rate, which may exist at lower frequencies than when the underlying respiration rate is higher.

[0073] The above-described filtering may be applied using any of a wide range of known hardware, firmware and / or software-based filters. The filtering may be performed with a filtering circuit and / or via software implementations such as Fourier filtering. The filtering may be performed by one or more components of the data processing system 6, such as a mobile device 8 and / or a wearable electronics unit 10 where these are part of the data processing system 6. In an example implementation, an order of an applied filter is 5, 6, 7, 8, 9, or 10. A sampling rate may be between 12 samples per second and 80 samples per second for example.

[0074] Arrangements have been discussed above in which a sensor device 16, such as a piezoelectric or piezoresistive device, is used to obtain measurement data for monitoring respiration. However, the piezoelectric or piezoresistive sensor device 16 and / or other sensors may be used to additionally monitor other aspects of user’s physiology and / or activity. For example, the piezoelectric or piezoresistive sensor device 16 or other sensors may be used to simultaneously monitor respiratory patterns, heart rate, and motion metrics (such as the number of steps during fitness training or identifying patterns or anomalies in the user’s movement that can be useful for gait analysis and / or detecting irregular movements during a specific exercise) to provide comprehensive fitness and health data in real-time.

[0075] Whilst examples of the wearable sensor 4 discussed above use a piezoelectric or piezoresistive sensor device 16 to measure respiration in real time, alternative and / or additional devices may be used in the wearable sensor 4 to measure respiration in real time. For example, one or more of the following may be used: microphones; capacitive, resistive (including graphene) and fiber-optic chest motion sensors; pulse oximeters; transthoracic impedance sensors; accelerometers; gyroscopes; magnetometers; air humidity detection sensors; and airflow sensors, such as flowmeters and anemometers.

Claims

CLAIMS1. A monitoring system, comprising: a wearable sensor configured to be worn by a user; and a data processing system, wherein: the wearable sensor is configured to perform measurements to obtain measurement data containing information about respiration; and the data processing system is configured to process the measurement data to derive information about a level of exertion of the user during a physical activity and output during the physical activity the derived information about the level of exertion.

2. The monitoring system of claim 1, wherein the measurements measure chest motion.

3. The monitoring system of claim 2, wherein the wearable sensor comprises a piezoelectric or piezoresistive device configured to perform the measurements.

4. The monitoring system of claim 3, wherein the piezoelectric or piezoresistive device comprises a piezofilm sensor.

5. The monitoring system of claim 4, wherein the piezofilm sensor is configured to deform when attached to the attachment location and thereby conform to a local curvature of the user’s body at the attachment location.

6. The monitoring system of claim 4 or 5, wherein the piezofilm sensor has a capacitance in the range of about 0.3nF to 4.0nF.

7. The monitoring system of any of claim 3 to 5, wherein the wearable device comprises a piezoresistive device comprising a laser-induced graphene element.

8. The monitoring system of claim 7, wherein the laser-induced graphene element hasan area in the range of 10 mm2to 10000 mm2, optionally in a square, rectangular, or serpentine form.

9. The monitoring system of any of claims 3-8, wherein the piezoelectric or piezoresistive device comprises a plurality of through holes configured to provide respective fluidic connections from the user’s skin to the environment outside of the wearable sensor while the wearable sensor is worn by the user, thereby promoting evaporation of sweat from the user’s skin during the physical activity.

10. The monitoring system of any preceding claim, wherein the data processing system is configured to obtain a respiration rate from the measurement data and use the obtained respiration rate to derive the information about the level of exertion.

11. The monitoring system of claim 10, wherein the data processing system is configured to use the obtained respiration rate and a user’s maximum respiration rate to derive the information about the level of exertion.

12. The monitoring system of claim 11, wherein the data processing system is configured to output instructions for a sequence of physical activities configured to progressively increase physical demands on the user and to derive therefrom an estimate of the user’s maximum respiration rate.

13. The monitoring system of any of claims 10-12, wherein the information about the level of exertion comprises the obtained respiration rate and / or a measure of a rate of perceived exertion, RPE.

14. The monitoring system of any preceding claim, wherein the data processing system is configured to generate exercise guidance based on the derived information about the level of exertion and output the exercise guidance to the user during the physical activity.

15. The monitoring system of claim 14, wherein the exercise guidance comprises oneor more indications to adapt a level of exertion issued at one or more respective times during the physical activity, each indication comprising an indication to increase or decrease a level of exertion towards a target level of exertion.

16. The monitoring system of any preceding claim, wherein the wearable sensor is configured to be worn at an attachment location on the chest or abdomen.

17. The monitoring system of claim 16, wherein the wearable sensor is configured to be attached at the attachment location by adhesion to the skin.

18. The monitoring system of any preceding claim, wherein the monitoring system comprises a wearable electronics unit.

19. The monitoring system of claim 18, further comprising a data connection between the wearable sensor and the wearable electronics unit, the data connection being configured to allow transmission of data at least from the wearable sensor to the wearable electronics unit.

20. The monitoring system of claim 19, comprising a flexible connection arrangement mechanically connecting the wearable sensor and the wearable electronics unit, the flexible connection arrangement being configured to allow a distance between the wearable sensor and the wearable electronics unit to be varied freely within a predetermined range prior to attachment of the wearable sensor and the wearable electronics unit to the user.

21. The monitoring system of claim 20, wherein the data connection comprises one or more wires that provide the flexible connection arrangement.

22. The monitoring system of any of claims 18-21, wherein the wearable sensor and the wearable electronics unit are configured to be attached at different locations on the user’s body, the different locations being separated from each other.

23. The monitoring system of any of claims 18-22, wherein the wearable sensor is configured to be adhered to the user’s skin and the wearable electronics unit is configured to be mounted on an item of clothing, optionally wherein the wearable sensor and wearable electronics unit are joined in a wearable patch.

24. The monitoring system of any of claims 18-23, wherein the wearable electronics unit comprises one or more of the following: an amplifier, an analog-to-digital converter, a microcontroller, and an energy source.

25. The monitoring system of any of claims 18-24, wherein the wearable electronics unit is configured to transmit data wirelessly.

26. The monitoring system of any preceding claim, wherein the data processing system is configured to repeatedly update the output of the derived information about the level of exertion during the physical activity, in real time or quasi-real time.

27. The monitoring system of any of claims 1-26, wherein the wearable electronics unit is configured to communicate wirelessly with the data processing system.

28. The monitoring system of any of claims 1-26, wherein the wearable electronics unit contains at least a portion of the data processing system.

29. The monitoring system of any preceding claim, wherein: the measurement data comprises first motion data and second motion data; the wearable sensor comprises a first measuring device configured to obtain the first motion data using a first measurement modality; the wearable sensor comprises a second measuring device configured to obtain the second motion data using a second measurement modality different from the first measurement modality; and the data processing system is configured to use a combination of the first motion data and the second motion data to derive the information about the level of exertion.

30. The monitoring system of claim 29, wherein the data processing system is configured to use the second motion data to suppress contributions to the derived information about the level of exertion from components of the first motion data that correspond to movements of the user other than movements associated with respiration.

31. The monitoring system of claim 29 or 30, wherein the first measuring device comprises a piezoelectric device or a piezoresistive device.

32. The monitoring system of claim 31, wherein the second measuring device comprises an accelerometer.

33. The monitoring system of any preceding claim, wherein the data processing system is configured to filter the measurement data to suppress contributions to the measurement data from movements of the user other than movements associated with respiration.

34. A monitoring system for monitoring respiration, comprising: a wearable sensor configured to be worn by a user; and a data processing system, wherein: the wearable sensor is configured to perform measurements to obtain measurement data containing information about respiration; and the data processing system is configured to filter the measurement data to suppress contributions to the measurement data from movements of the user other than movements associated with respiration.

35. The monitoring system of claim 33 or 34, wherein the filtering of the respiration data comprises suppressing contributions above an upper threshold frequency relative to contributions below the upper threshold frequency.

36. The monitoring system of claim 35, wherein the upper threshold frequency is in the range of between 30 and 80 breaths per minute.

37. The monitoring system of claim 35 or 36, wherein the data processing system is configured to derive a respiration rate from the respiration data and use the respiration rate to adapt the upper threshold frequency dependent on the derived respiration rate.

38. The monitoring system of any of claims 34-37, wherein the filtering of the respiration data comprises suppressing contributions below a lower threshold frequency relative to contributions above the lower threshold frequency.

39. The monitoring system of claim 38, wherein the lower threshold frequency is in the range of between 8 and 30 breaths per minute.

40. The monitoring system of any of claims 38-39, wherein the data processing system is configured to derive a respiration rate from the respiration data and use the respiration rate to adapt the lower threshold frequency dependent on the derived respiration rate.

41. A method of monitoring, comprising: performing measurements to obtain measurement data containing information about respiration at a wearable sensor worn by a user; and processing the measurement data to derive information about a level of exertion of the user during a physical activity at a data processing system and outputting during the physical activity the derived information about the level of exertion.

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