Neurological monitoring

Abstract

Photoplethysmography (PPG) data (12, Fig. 1) of a user are received from a PPG sensor in a ear-worn device. Otoacoustic emission (OAE) data (14, Fig. 1) are received from a microphone in the ear-worn

Description

TECHNOLOGICAL FIELD Examples of the disclosure relate to neurological monitoring. Some relate to neurological monitoring using an ear-worn device. BACKGROUND Various techniques can be used to perform neurological monitoring of a person. Many neurological monitoring techniques are invasive. It would be desirable to improve and / or enhance neurological monitoring. BRIEF SUMMARY According to various, but not necessarily all examples there is provided an apparatus comprising means for: receiving photoplethysmography, PPG, sensor data of a user from a PPG sensor comprised by an ear-worn device; receiving otoacoustic emission, OAE, data of the user from a microphone comprised by the ear-worn device; determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received PPG sensor data and OAE data; and triggering at least one action based, at least in part, on determining that an onset of an acute motor disturbance is occurring. In some examples, the at least one action comprises at least one of the following: outputting an alert to the user to inform the user of the onset of the acute motor disturbance; or outputting at least one sound from a speaker comprised by the ear-worn device to mitigate the occurrence of the acute motor disturbance, or to prevent the occurrence of the acute motor disturbance. In some examples, the means are configured to: receive motion data of the user from a motion sensor comprised by the ear-worn device; and determine that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received motion data. In some examples, determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received motion data comprises determining an occurrence of at least one abnormal movement pattern associated with at least one acute motor disturbance. In some examples, the means are configured to remove motion artifacts from the PPG sensor data based, at least in part, on the received motion data. In some examples, determining that an onset of an acute motor disturbance is occurring for the user comprises determining at least one of the following: stress level of the user; attention level of the user; or cognitive load of the user. In some examples, the means are configured to determine a reference OAE level for the user, wherein determining that an onset of an acute motor disturbance is occurring for the user comprises comparing current OAE data of the user with the reference OAE level for the user. In some examples, the means are configured to: temporally align the received PPG sensor data, and OAE; and determine that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the temporally aligned PPG sensor data, and OAE data. In some examples, the means are configured to: store at least a portion of at least one of the PPG sensor data, and OAE data; and determine that an onset of an acute motor disturbance is occurring based, at least in part, on the stored data. In some examples, an acute motor disturbance comprises at least one of the following: a seizure; tremors; sleep paralysis; or freezing of gait. In some examples, an acute motor disturbance comprises a motor disturbance associated with a neurological disorder. According to various, but not necessarily all, examples there is provided an electronic device comprising an apparatus as described herein In some examples, the electronic device is the ear-worn device or a handheld mobile device such as a tablet, mobile telephone, etc.. According to various, but not necessarily all, examples there is provided a method comprising: receiving photoplethysmography, PPG, sensor data of a user from a PPG sensor comprised by an ear-worn device; receiving otoacoustic emission, OAE, data of the user from a microphone comprised by the ear-worn device; determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received PPG sensor data and OAE data; and triggering at least one action based, at least in part, on determining that an onset of an acute motor disturbance is occurring. In some examples, the at least one action comprises at least one of the following: outputting an alert to the user to inform the user of the onset of the acute motor disturbance; or outputting at least one sound from a speaker comprised by the ear-worn device to mitigate the occurrence of the acute motor disturbance, or to prevent the occurrence of the acute motor disturbance. In some examples, the method comprises: receiving motion data of the user from a motion sensor comprised by the ear-worn device; and determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received motion data. In some examples, determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received motion data comprises determining an occurrence of at least one abnormal movement pattern associated with at least one acute motor disturbance. In some examples, the method comprises removing motion artifacts from the PPG sensor data based, at least in part, on the received motion data. In some examples, determining that an onset of an acute motor disturbance is occurring for the user comprises determining at least one of the following: stress level of the user; attention level of the user; or cognitive load of the user. In some examples, the method comprises determining a reference OAE level for the user, wherein determining that an onset of an acute motor disturbance is occurring for the user comprises comparing current OAE data of the user with the reference OAE level for the user. According to various, but not necessarily all, examples there is provided a computer program comprising instructions for causing an apparatus to perform: receiving photoplethysmography, PPG, sensor data of a user from a PPG sensor comprised by an ear-worn device; receiving otoacoustic emission, OAE, data of the user from a microphone comprised by the ear-worn device; determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received PPG sensor data and OAE data; and triggering at least one action based, at least in part, on determining that an onset of an acute motor disturbance is occurring. In some examples, the at least one action comprises at least one of the following: outputting an alert to the user to inform the user of the onset of the acute motor disturbance; or outputting at least one sound from a speaker comprised by the ear-worn device to mitigate the occurrence of the acute motor disturbance, or to prevent the occurrence of the acute motor disturbance. In some examples, the computer program comprises instructions for causing an apparatus to perform: receiving motion data of the user from a motion sensor comprised by the ear-worn device; and determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received motion data. In some examples, determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received motion data comprises determining an occurrence of at least one abnormal movement pattern associated with at least one acute motor disturbance. In some examples, determining that an onset of an acute motor disturbance is occurring for the user comprises determining at least one of the following: stress level of the user; attention level of the user; or cognitive load of the user. According to various, but not necessarily all, embodiments there is provided an apparatus comprising at least one processor; and at least one memory including computer program code; the at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to perform at least a part of one or more methods described herein. According to various, but not necessarily all, embodiments there is provided an apparatus comprising means for performing at least part of one or more methods described herein. The description of a function and / or action should additionally be considered to also disclose any means suitable for performing that function and / or action. Functions and / or actions described herein can be performed in any suitable way using any suitable method. According to various, but not necessarily all, embodiments there is provided examples as claimed in the appended claims. While the above examples of the disclosure and optional features are described separately, it is to be understood that their provision in all possible combinations and permutations is contained within the disclosure. It is to be understood that various examples of the disclosure can comprise any or all the features described in respect of other examples of the disclosure, and vice versa. Also, it is to be appreciated that any one or more or all the features, in any combination, may be implemented by / comprised in / performable by an apparatus, a method, and / or computer program instructions as desired, and as appropriate. The description of a function should additionally be considered to also disclose any means suitable for performing that function BRIEF DESCRIPTION Some examples will now be described with reference to the accompanying drawings in which: FIG. 1 shows an example of the subject matter described herein; FIG. 2 shows another example of the subject matter described herein; FIG. 3 shows another example of the subject matter described herein; FIG. 4 shows another example of the subject matter described herein; FIG. 5A shows another example of the subject matter described herein; and FIG. 5B shows another example of the subject matter described herein. The figures are not necessarily to scale. Certain features and views of the figures can be shown schematically or exaggerated in scale in the interest of clarity and conciseness. For example, the dimensions of some elements in the figures can be exaggerated relative to other elements to aid explication. Similar reference numerals are used in the figures to designate similar features. For clarity, all reference numerals are not necessarily displayed in all figures. DETAILED DESCRIPTION Examples of the disclosure relate to at least one of the following: apparatuses, methods, or computer programs for and / or involved in neurological monitoring. Additionally, or alternatively, examples of the disclosure relate to at least one of the following: apparatuses, methods, or computer programs for and / or involved in determining an onset of an acute motor disturbance. Additionally, or alternatively, examples of the disclosure relate to at least one of the following: apparatuses, methods, or computer programs, for and / or involved in at least one of the following: alerting a user to an onset of an acute motor disturbance, or mitigating the onset of an acute motor disturbance. In examples, the onset of an acute motor disturbance for a user of an ear-worn device can be determined based, at least in part, on information from at least one sensor comprised by the ear-worn device. The following description and drawings describe various examples of an apparatus comprising means for: receiving photoplethysmography, PPG, sensor data of a user from a PPG sensor comprised by an ear-worn device; receiving otoacoustic emission, OAE, data of the user from a microphone comprised by the ear-worn device; determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received PPG sensor data and OAE data; and triggering at least one action based, at least in part, on determining that an onset of an acute motor disturbance is occurring. The means can comprise at least one processor; and at least one memory including computer program code; the at least one memory storing instructions that, when executed by the at least one processor, cause performance of at least part of at least one method described herein. As used herein, an apparatus and / or device and / or component for / comprising means for performing one or more actions should also be considered to disclose an apparatus and / or device and / or component configured to perform the one or more actions. Similarly, as used herein, an apparatus and / or device and / or component configured to perform one or more actions should also be considered to disclose an apparatus and / or device and / or component for / comprising means for performing the one or more actions. Description of performing an action should also be considered to disclose causing and / or controlling the action. For example, transmitting information should also be considered to disclose causing and / or controlling transmission of information. Similarly, description of causing and / or controlling an action should be considered to also disclose performing the action. FIG. 1 schematically illustrates an example of an apparatus 10. Various features referred to in relation to FIG. 1 can be found in the other FIGs. The apparatus 10 can be comprised, for example integrated, in a device (see, for example, FIG. 2), for example an electronic device. The apparatus 10 can be comprised in any suitable device. For example, the apparatus 10 can be comprised in a user device such as an ear-worn device, mobile telephone, laptop, desktop, tablet, and so on. In some examples the apparatus 10 is comprised by a handheld mobile device such as a mobile telephone, a tablet computer, or similar. In examples, the apparatus 10 is configured to receive signals 18_R comprising information, for example data, such as signals 18_R from at least one sensor. The received signals 18_R / information in the received signals 18_R can originate from at least one entity outside of a device in which the apparatus 10 is comprised. Signals 18_R that originate from at least one entity outside of a device in which the apparatus 10 is comprised can be referred to as external signals. For example, the received signals 18_R / information in the received signals 12_R can originate from at least one sensor, such as from at least one of: at least one photoplethysmography (PPG) sensor, at least one microphone, or at least one motion sensor, located in at least one device that is separate from a device in which the apparatus 10 is comprised. Additionally, or alternatively, the received signals 18_R / information in the received signals 18_R can originate from at least one entity that is also comprised in a device in which the apparatus 10 is comprised. Signals 18_R that originate from at least one entity comprised in a device in which the apparatus 10 is also comprised can be referred to as internal signals. For example, the received signals 18_R / information in the received signals 18_R can originate from at least one sensor, such as from at least one of: at least one PPG sensor, at least one microphone, or at least one motion sensor, located in a device in which the apparatus 10 is comprised. In the illustrated example, the apparatus 10 is configured to receive PPG sensor data 12, otoacoustic emission (OAE) data 14, and motion data 16. However, in some examples, at least one of the PPG sensor data 12, OAE data 14, or the motion data 16 can be omitted. For example, the motion data 16 can be omitted. In some examples, the apparatus 10 is configured to transmit signals 18_T comprising information, such as signals 18_T to control a component or device which can be referred to as control signals. The transmitted signals 18_T can be transmitted towards at least one entity outside of a device in which the apparatus 10 is located. Signals 18_T that are transmitted towards at least one entity outside of a device in which the apparatus 10 is located can be referred to as external signals. For example, the transmitted signals 18_T can be transmitted towards at least one device that is separate from a device in which the apparatus 10 is comprised. For example, the transmitted signals 18_T can be transmitted towards at least one sensor, such as at least one of: at least one PPG, sensor, at least one microphone, or at least one motion sensor, located in a device that is separate from a device in which the apparatus 10 is comprised. For example, the transmitted signals 18_T can be transmitted towards at least one device output, such as at least one speaker, at least one display, at least one haptic output and so on located in a device that is separate from a device in which the apparatus 10 is comprised. Additionally, or alternatively, the transmitted signals 18_T can be transmitted towards at least one entity that is comprised in a device in which the apparatus 10 is comprised. Signals 18_T that are transmitted towards at least one entity comprised in a device in which the apparatus 10 is comprised can be referred to as internal signals. For example, the transmitted signals 18_T can be transmitted towards at least one sensor, such as at least one of: at least one PPG sensor, at least one microphone, or at least one motion sensor, located in a device in which the apparatus 10 is comprised. For example, the transmitted signals 18_T can be transmitted towards at least one device output, such as at least one speaker, at least one display, at least one haptic output and so on located in a device in which the apparatus 10 is comprised. In examples, the transmitted signals 18_T comprise at least one control signal configured to control at least one of the following: at least one component or at least one device and so on. For example, the transmitted signals 18_T can comprise at least one control signal configured to control at least one of: at least one sensor, or at least one device output and so on. The apparatus 10 can be a controller. See, for example, FIGs 5A and 5B. The apparatus 10 can be a device. In some examples, the apparatus 10 is configured to cause / control performance of at least part of at least of at least one method described herein. In some examples, the apparatus 10 is configured to perform at least part of at least one method described herein. FIG. 2 schematically illustrates an example of a device 20. The device 20 can be an electronic device, such as an ear-worn device or a mobile telephone. In examples, an ear-worn device is a device worn on and / or in at least one ear of the user. For example, an ear-worn device can comprise an over-ear headphone, an on-ear headphone, an in-ear headphone (commonly referred to as an earbud), a hearing aid and so on. In examples, different parts of a pair of headphones can be considered different ear-worn devices. For example, a first ear cup of a pair of headphones can be considered a first ear-worn device and a second ear cup of a pair of headphones can be considered a second ear-worn device. For example, a first in-ear headphone of a set of in-ear headphones can be considered a first ear-worn device and a second in-ear headphone of a set of in-ear headphones can be considered a second ear-worn device. An ear-worn device may in some examples be considered a discrete device enclosed within a single housing. However, it may alternatively be a device that is comprised my multiple component parts distributed across more than one apparatus with different housings but in communication with one another. As an example of the latter, a pair of in-hear headphones might be considered to comprise a single ear-worn device. The two sides of a pair of over-ear might also be considered to be a first ear-worn device. The device 20 comprises at least one transceiver 23, at least one PPG sensor 22, at least one microphone 24, at least one motion sensor 26, at least one component 28, and an apparatus 10 as described in relation to FIG. 1. Consequently, FIG. 2 illustrates an example of an electronic device comprising an apparatus 10 as described herein. In examples, the at least one component can comprise the at least one PPG sensor 22, at least one microphone 24, and at least one motion sensor 26. Accordingly, in examples, the at least one PPG sensor 22, at least one microphone 24, and at least one motion sensor 26 can be considered to be components 28 of the device 20. In other words, in examples, the at least one PPG sensor 22, at least one microphone 24, and at least one motion sensor 26 can be comprised by the device 20. Similarly, in examples, the at least one transceiver 23 can be considered to be a component of the device 20. The device 20 can be any suitable device 20. In some, but not necessarily all, examples the device 20 is an ear-worn device. In some examples, the device 20 is a mobile telephone. The at least one transceiver 23 can be any suitable transceiver or transceivers 23. For example, the at least one transceiver 23 can comprise any suitable transceiver(s) 23 for transmitting and / or receiving at least one signal 18. In examples, the at least one transceiver 23 is configured to transmit and / or receive one or more signals 18 using wired or wireless communication. Any suitable wired or wireless communication protocol(s) can be used. In some examples, the at least one transceiver 23 can comprise one or more separate transmitters and receivers. In examples, the at least one transceiver 23 is configured to receive at least one received signal 12_R as discussed in relation to FIG. 1. In examples, the apparatus 10 is configured to transmit one or more signals 12_T to that at least one transceiver 23 for transmission by the at least one transceiver 23. For example, the apparatus 10 can transmit at least one control signal to a device to control at least one of: at least one speaker of the device, at least one sensor of the device, at least one display of the device and so on. The at least one PPG sensor 22 can comprise any suitable PPG sensor(s) 22 configured to provide PPG sensor data 12 of a user. The at least one microphone 24 can comprise any suitable microphone(s) 24 configured to provide OAE data 14 of a user. In some examples, the at least one microphone comprises at least one in-ear microphone. The at least one motion sensor 26 can comprise any suitable motion sensor(s) 26 configured to provide motion data 16 of a user. For example, the at least one motion sensor 26 can comprise at least one inertial measurement unit (IMU). In examples, motion data of a user is data of motion of at least part of a person’s body. The at least one component 28 can comprise any suitable component or components of device 20. In examples, the at least on component 28 comprises at least one device output, such as at least one speaker, at least one display, at least one haptic output and so on. As illustrated in the example of FIG. 2, the at least one transceiver 23, at least one PPG sensor 22, at least one microphone 24, at least one motion sensor 26, and at least one component 28 are operationally coupled to the apparatus 10 and signals 18 can be transmitted between the elements 22, 23, 24, 26, 28 and the apparatus 10. Any number of intervening elements can exist between them (including no intervening elements). Additionally, or alternatively, two or more elements of the device 20 illustrated in the example of FIG. 2 can be integrated or combined. Additionally, or alternatively, one or more elements of the device 20 illustrated in the example of FIG. 2 can be omitted. For example, the at least one transceiver 23 can be omitted. For example, the at least one motion sensor 26 can be omitted. Additionally, or alternatively, the device 20 can comprise any number of additional elements not illustrated in the example of FIG. 2. In examples, the device 20 is configured to perform at least part of at least one method described herein. FIG. 3 illustrates an example of a method 30. One or more features discussed in relation to FIG. 3 can be found in one or more of the other drawings. Method 30 can be performed, for example, by at least one of an apparatus 10 of FIG. 1 or a device 20 of FIG. 2. In examples, method 30, and / or at least part of method 30, is a method of neurological monitoring. In examples, method 30, and / or at least part of method 30, is a method of determining an onset of an acute motor disturbance. In examples, method 30, and / or at least part of method 30, is a method of alerting a user to an onset of an acute motor disturbance. In examples, method 30, and / or at least part of method 30, is a method of mitigating the onset of an acute motor disturbance. At block 32, method 30 comprises receiving PPG sensor data 12 of a user from a PPG sensor 22 comprised by an ear-worn device. PPG sensor data 12 can comprise any suitable data produced by a PPG sensor 22. In examples, PPG sensor data 12 can be considered PPG data. In examples, a user can be considered a person who is using the device 20 in which the PPG sensor 22 (and / or other component such as microphone or motion sensor) is located. For example, a user can be a person who is wearing the ear-worn device in which the PPG sensor is located. In examples, data can be considered to be data of a user because the data has been determined in relation to the user. For example, PPG sensor data 12 of a user can be considered to be PPG sensor data 12 of a user because the PPG sensor data 12 has been determined in relation to the user. In examples, data of a user can be considered data for a user, data determined from a user, data relating to a user and so on. A component can be considered to be comprised by an ear-worn device because the component is located in, for example integrated into / forms part of, the ear-worn device. For example, a PPG sensor 22 can be considered to be comprised by an ear-worn device because the PPG sensor 22 is located in the ear-worn device. In examples, the PPG sensor 22 is configured to perform PPG measurements of a user when the user is using the ear-worn device. At block 304, method 30 comprises receiving OAE data 14 of the user from a microphone 24 comprised by the ear-worn device. OAE data 14 can comprise any suitable data produced by a microphone 24 based, at least in part, on OAEs of a user. For example, OAE data 14 can comprise data produced by a microphone 24 detecting OAEs from a cochlea of a user in response to playing of at least one sound. In examples, the microphone 24 is an in-ear microphone 24. In examples, an in-ear microphone is a microphone 24 that is located at least partially in an ear-canal of the user when the user is using the ear-worn device. In some examples, at least one speaker of an ear-worn device outputs at least one sound, and at least one microphone 24 of the ear-worn device detects the resulting OAE. At block 306, method 30 comprises determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received PPG sensor data 12 and OAE data 14. In examples, determining that an onset of an acute motor disturbance is occurring for the user comprises at least one of determining that an acute motor disturbance is going to occur for the user or determining that an acute motor disturbance is occurring for the user. That is, in examples, determining that an onset of an acute motor disturbance is occurring for a user comprises at least one of determining that the user will experience an acute motor disturbance or determining that the user is experiencing an acute motor disturbance. In some examples, an acute motor disturbance can be referred to as a sporadic neuromotor event. In examples, an acute motor disturbance is an event, for example a body movement, caused by abnormal activities of at least one neuromotor area of the brain. In some examples, an acute motor disturbance comprises a motor disturbance (e.g. involuntary body movement) associated with a neurological disorder, for example Parkinson’s disease. In some examples, an acute motor disturbance comprises at least one of the following: a seizure, a tremor, sleep paralysis, or freezing of gait. In examples, block 36 comprises performing any suitable analysis, for example any suitable processing, based, at least in part, on the received PPG sensor data 12 of the user and the received OAE data 14 of the user. For example, block 36 can comprise performing any suitable analysis based, at least in part, on the received PPG sensor data 12 of the user and the received OAE data 14 to determine that at least one of an acute motor disturbance is going to happen for the user or is happening for the user. In some examples, block 36 comprises using the received PPG sensor data 12 and the received OAE date 14 for the user as an analogue for electroencephalogram (EEG) data for the user. The inventors have determined that there is a relationship between PPG sensor data for a user, OAE sensor data for a user and EEG data for a user. This allows, for example, PPG and OAE data to be used as an analogue of EEG data. In some examples, determining that an onset of an acute motor disturbance is occurring for the user comprises determining at least one of the following: stress level of the user, attention level of the user, or cognitive load of the user. In examples, PPG sensor data 12 is used to measure hemodynamic changes, for example by determining biomarkers such as heart rate, heart rate variability, respiration rate and so on. Such biomarkers correlate with physiological responses to, for example, cognitive and emotional stimuli. In examples, variations in the PPG sensor data 12 are analyzed to determine stress levels of the user. Stress levels, in turn, correlate with potential acute motor disturbances and acute motor disturbances. In examples, the PPG sensor data 12 of the user is analyzed to identify patterns related to symptoms, for example early symptoms, of acute motor disturbances. In examples, a correlation engine is used to identify patterns in the PPG sensor data 12 of the user to identify an onset of an acute motor disturbance is occurring for the user. In examples, the PPG sensor data 12 is analyzed to extract key physiological biomarkers. For example, heart rate, heart rate variability (HRV), and blood flow characteristics. In examples, Fast Fourier Transform (FFT) is employed to derive these biomarkers, providing a comprehensive understanding of the user's physiological state. In examples, the extracted biomarkers are fed into a stress detection engine, which uses, for example, Support Vector Machines (SVM) to assess the user's stress levels. This information can be correlated with motion data 16 from a motion sensor 26 and OAE data 14 from a microphone 24 to determine that an onset of an acute motor disturbance is occurring for the user. In examples, fluctuations in the OAE data 14 of the user are analyzed to identify cognitive feedback and occurrence or potential occurrence of an acute motor disturbance. In examples, the OAE data 14 of the user is analyzed to determine at least one of an attention level of the user, a stress level of the user, or cognitive load of the user. OAE correlate with attention levels and stress, and therefore analysis of the OAE data 14 of the user can enable a determining of an attention level of the user, and a stress level of the user. Furthermore, the inventors have realized that OAE data and EEG data both reflect changes in cognitive load. Through observations, the inventors have determined that when measuring changes in brain wave magnitudes captured by EEG, as well as OAE during various stressor tasks, it was found that higher cognitive load leads to increased EEG activity and noticeable alterations in OAE, indicating a clear link between auditory processing and brain activity. In examples, the correlation properties of PPG data 12 and OAE data 14 with user stress and attention levels, and cognitive load of the user, are leveraged to determine that an onset of an acute motor disturbance is occurring. In examples, PPG sensor data 12 and OAE data 14 of the user are combined to accurately, and continuously, detect, for example, stress levels of the user, which are a known symptom preceding an acute motor disturbance. In some examples, the received data is used to monitor changes in the auditory system of the user that correspond with fluctuations in cognitive load and brain activity and identify early-stage repercussions of these on the motor system of the user. In examples, single-frequency otoacoustic emissions (OAEs) quantify cochlear responses at specific frequencies. In some examples, following the playback of some given test sounds, the cochlea emits feedback at a known frequency or frequency range. In examples, the power of those feedback signals is analyzed to determine performance of the user’s hearing. These single-frequency OAEs can be used to detect variations in response to differing levels of auditory cognitive load the user is undergoing. In examples, an inward-facing microphone 24 in an ear-worn device records the sounds in the ear canal, which not only includes the OAEs, but also the audio event propagating in the ear canal. To extract the OAEs from the recorded audio, in examples, a bandpass filter centered around the expected frequency of the OAEs is implemented. For example, a bandpass filter centered around 3 kHz, with a bandwidth of 200 Hz can be used. This process can effectively remove the test sounds within the audio event, leaving only the OAEs intact. In examples, method 30 comprises determining a reference OAE level for the user, and determining that an onset of an acute motor disturbance is occurring for the user comprises comparing current OAE data of the user with the reference OAE level for the user. In examples, the reference OAE level for the user is determined while the user is in a state of rest. In examples, the reference OAE level is used as a calibration for the user. The magnitude computed for initial calibration part can serve as a reference for the user. In examples, this can be used to account for the variability in individual auditory perception, such as the inherent perception differences, variations in ear canal shape and different earbud positioning. In examples, The OAEs obtained from then onwards can be compared to that of initial reference level for the user. For example, Sound Energy Difference (SED) 6 can be defined, to quantify the acoustic changes with respect to cognitive load variation, a parameter that can, for example, be fed into a correlation engine. 5 = |m2 -M / l Where: M2: is the power of stimuli without cognitive load (user at rest) : is the power of stimuli with cognitive load (current OAE data) The larger the 5, the greater the changes in the auditory system under that cognitive load. In some examples, method 30 comprises temporally aligning the received PPG sensor data 12, and OAE data 14, and determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the temporally aligned PPG sensor data 12, and OAE data. In some examples, motion data 16 from a motion sensor 26 can also be used. Temporally aligning the received PPG sensor data 12, and OAE data 14 can comprise temporally aligning the PPG sensor data 12 after the data has undergone processing and / or the OAE data 14 after the data has undergone processing. For example, after the received data has at least partially moved along a processing pipeline. In examples, data from each sensor is synchronized through timestamp alignment and interpolation. For example, if the microphone 24 captures OAE data 14 every 50 ms and the PPG sensor 22 records heart rate every 100 ms, the data are resampled to match the more frequent interval, ensuring all data streams are aligned. In examples, the received data is processed and analyzed to determine whether an onset of an acute motor disturbance is occurring for the user. Any suitable processing and analysis can be used. In examples, at least one machine learning algorithm can be used to process and / or analyze the received data. For example, a long short-term memory network can be used. In some examples, an LSTM network is used to analyze time-series data from sensors. Data from each sensor is synchronized through timestamp alignment and interpolation. For example, if the in-ear microphone captures OAE data every 50 ms and the PPG sensor records heart rate every 100 ms, the data are resampled to match the more frequent interval, ensuring all data streams are aligned. In examples, motion data 16 from a motion sensor 26 can also be used. In some examples, an LSTM network captures temporal dependencies and relationships between features from different sensors. For instance, if the system detects a drop in OAE amplitude, and a decrease in HRV data simultaneously, the LSTM network integrates these features to assess their combined impact on neuromotor health. In examples, motion data 16 from a motion sensor 26 can also be used. In examples, an LSTM network integrates OAE, PPG, and, in some examples, IMU data. For instance, if OAE amplitude decreases, HRV metrics become irregular, and, in some examples, the IMU detects sudden gait instability, the network correlates these changes to assess stress levels and potential neuromotor issues. In examples, the LSTM network performs continuous processing of incoming data. For example, if the system detects simultaneous spikes in heart rate variability, changes in OAE, and, in some examples, unusual motion patterns, it integrates these signals to provide timely detection and classification of acute motor disturbances, such as seizures or freezing of gait. In some examples, method 30 comprises storing at least a portion of at least one of the PPG sensor data 12, and OAE data 14, and determining that an onset of an acute motor disturbance is occurring based, at least in part, on the stored data. In some examples, motion data 16 from a motion sensor 26 can also be used. The stored data can be used as feedback to calibrate / personalize the determining of acute motor disturbances for a particular user. For example, the stored data can be analyzed to determine data trends and / or features for the user that indicate the onset of an acute motor disturbance for that user. In examples, an LSTM network is trained to recognize patterns indicative of acute motor disturbances. For example, if the model learns that a combination of low OAE amplitude, reduced HRV, and, in some examples, abnormal IMU patterns (such as a sudden stop in gait) precedes a seizure, it uses these patterns to predict potential events in real-time. In some examples, block 34 comprises receiving motion data 16 of the user from a motion sensor 26 comprised by the ear-worn device. motion data 16 can comprise any suitable data produced by a motion sensor 26 based, at least in part, on motion of at least part of a user’s body. Any suitable motion sensor 26 can be used. For example, an IMU can be used. In examples, block 36 comprises determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received motion data 16. Consequently, in examples, method 30 comprises receiving motion data 16 of the user from a motion sensor 26 comprised by the ear-worn device, and determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received motion data 16. In examples, the received motion data 16 can be used in the processing / analysis described above to determine that an onset of an acute motor disturbance is occurring for the user. In examples, the motion data 16 is processed and analyzed in combination with the PPG sensor data 12 and OAE data 14 of the user. In examples, determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received motion data comprises determining an occurrence of at least one abnormal movement pattern associated with at least one acute motor disturbance. For example, the motion data 16 can be analyzed to detect postural instability and abnormal movement patterns indicative of conditions such as early signals preceding, for instance, freezing of gait in Parkinson's disease. In some examples, the motion sensor 26 captures acceleration, gyroscopic movements, and orientation data. From this motion data 16, features such as acceleration magnitude, angular velocity, and orientation changes can be computed. In examples, wavelet analysis for the decomposition of motion data 16 into different frequency components is performed to identify specific biomarkers related to, for example, tremors, rigidity, and other motor symptoms. In examples, at least one machine learning algorithm is used to identify abnormal movement patterns from the extracted features. Any suitable machine learning algorithm(s) can be used. In examples, techniques such as Long Short-Term Memory (LSTM) networks are employed to classify movements. In examples, machine learning algorithm(s) can identify events such as falls, freezing of gait, and other neuromotor anomalies. In examples, by combining real-time motion data with historical patterns, an improvement in the accuracy of event detection and, for example, provision of timely alerts can be achieved. In some examples, the received motion data 16 can be used to improve the received PPG sensor data 12. For example, the received motion data 16 can be used to remove motion-based noise and artifacts from the received PPG sensor data 12. In examples, method 30 comprises removing motion artifacts from the PPG sensor data 12 based, at least in part, on the received motion data 16. In some examples, the PPG sensor data 12 is processed in combination with the received motion data 16 to remove noise and motion artifacts from the PPG sensor data 12. Noise and motion artifacts can significantly affect the accuracy of the readings. In examples, adaptive filtering is used to isolate and remove noise components, ensuring a clean PPG signal for further analysis. At block 38, method 30 comprises triggering at least one action based, at least in part, on determining that an onset of acute motor disturbance is occurring. Consequently, FIG. 3 illustrates a method 30 comprising: receiving photoplethysmography, PPG, sensor data 12 of a user from a PPG sensor 22 comprised by an ear-worn device; receiving otoacoustic emission, OAE, data 14 of the user from a microphone 24 comprised by the ear-worn device; determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received PPG sensor data 12 and OAE data 14; and triggering at least one action based, at least in part, on determining that an onset of an acute motor disturbance is occurring. In examples, triggering at least one action comprises causing performance of the at least one action and / or controlling performance of the at least one action. In examples, at least one action can comprise any suitable action. For example, any suitable action performed by at least one apparatus and / or device. For example, the at least one action can comprise any suitable action to alert the user of the determined onset of an acute motor disturbance for the user. For example, the at least one action can comprise any suitable action to mitigate the onset of an acute motor disturbance for the user. In examples, the at least one action comprises at least one of the following: outputting an alert to the user to inform the user of the onset of the acute motor disturbance, or outputting at least one sound from a speaker comprised by the ear-worn device to mitigate the occurrence of the acute motor disturbance, or prevent occurrence of the acute motor disturbance. Any suitable alert can be used. For example, any suitable alert output by at least one output device such as at least one speaker, at least one display, at least one haptic device and so on can be used. Any suitable sound to mitigate the occurrence of the acute motor disturbance, or prevent occurrence of the acute motor disturbance can be used. For example, any suitable sound or sounds can be used. For example, repeating sounds at a regular frequency can be outputted to mitigate the occurrence of the acute motor disturbance, or prevent occurrence of the acute motor disturbance. Examples of the disclosure are advantageous and provide technical benefits. For example, examples of the disclosure enable determining of an onset of an acute motor disturbance for a user using non-invasive wearable sensors. For example, examples of the disclosure enable an approximation of EEG functionality with a portable and cost-effective solution. For example, examples of the disclosure enable early detection for acute motor disturbances, improving safety of a user and quality of life of a user. For example, examples of the disclosure enable intervention for acute motor disturbances, improving safety and quality of life for a user. For example, examples of the disclosure provide for non-invasive detection of an onset of an acute motor disturbance without requiring surgically implanted sensors. For example, examples of the disclosure provide for detection of an onset of an acute motor disturbance using sensors in an ear-worn device, and also provision of soundbased interventions using the ear-worn device. This provides synergy of non-invasive detection and intervention of the onset of acute motor disturbances, with simplified interaction between the detection and intervention. In exampes, an ear-worn device (also referred as earable) is provided which features a number of sensors. Among these, the earable is equipped with at least a PPG sensor 22, an IMU sensor 26, and in-ear microphone 24. Examples of the disclosure takes as input data readings coming from these sensors to monitor and analyze the user physiology and their movements, to then associate with neuromotor events. FIG. 4 illustrates an example pipeline that can be used examples of the disclosure. FIG. 4 illustrates an example of a method 40. In examples, method 40 can be performed, for example, by at least one of an apparatus 10 of FIG. 1 or a device 20 of FIG. 2. In examples, method 40, and / or at least part of method 40, is a method of neurological monitoring. In examples, method 40, and / or at least part of method 40, is a method of determining an onset of an acute motor disturbance. In examples, method 40, and / or at least part of method 40, is a method of alerting a user to an onset of an acute motor disturbance. In examples, method 40, and / or at least part of method 40, is a method of mitigating the onset of an acute motor disturbance. In examples, processing at the various blocks of FIG. 4 can be as described above in relation to corresponding parts of the example of FIG. 3. In the example of FIG. 4, OAE data 14 is received into a OAE pipeline, motion data 16, which in the example of FIG. 4 comprises IMU data, is received into a motion data signal pipeline, and PPG sensor data 12 is received into a PPG signal processing pipeline. The OAE pipeline comprises OAE processing (block 41) and determining Sound Energy Difference (SED) 5, which is fed into a correlation engine 45. The motion data signal pipeline comprises two components: biomarkers extraction (block 43) and motion detector engine (block 44). The output of the motion detector engine is fed into the correlation engine 45. The PPG signal processing pipeline comprises three main components: motion artifact removal (block 46), biomarkers extraction (block 47), and stress detection engine (block 48). The output of the stress detection engine is fed into the correlation engine. In examples, the OAE analysis analyzes otoacoustic emissions to detect stress. For example, a significant drop in OAE amplitude, compared to baseline levels, indicates increased cognitive load or stress. In examples, the PPG signal processing pipeline assesses stress through heart rate variability (HRV). Reduced HRV and increased heart rate are physiological indicators of stress. For instance, a drop in HRV during a stressful event is a clear sign of elevated stress. In examples, the motion data pipeline detects motion-related biomarkers and postural instability. For example, the motion data may capture irregularities such as sudden changes in gait or abnormal tremor patterns. These anomalies can indicate stress-related motor issues or precursor signs of an acute motor disturbance. FIG. 5A illustrates an example of a controller 50 suitable for use in an apparatus 10, such as apparatus 10 of FIG. 1 and / or device 20 of FIG. 2. In examples, controller 50 can be an apparatus 10. Implementation of a controller 50 may be as controller circuitry. The controller 50 may be implemented in hardware alone, have certain aspects in software including firmware alone or can be a combination of hardware and software (including firmware). As illustrated in Fig 5A the controller 50 may be implemented using instructions that enable hardware functionality, for example, by using executable instructions 56 in a general-purpose or special-purpose processor 52 that may be stored on a machine readable storage medium (disk, memory etc.) to be executed by such a processor 52. The processor 52 is configured to read from and write to the memory 54. The processor 52 may also comprise an output interface via which data and / or commands are output by the processor 52 and an input interface via which data and / or commands are input to the processor 52. The memory 54 stores a computer program 56 comprising computer program instructions (computer program code) that controls the operation of the apparatus when loaded into the processor 52. The computer program instructions, of the computer program 56, provide the logic and routines that enables the apparatus to perform the methods illustrated in one or more of the accompanying Figs. The processor 52 by reading the memory 54 is able to load and execute the computer program 56. The apparatus comprises: at least one processor 52; and at least one memory 54 storing instructions that, when executed by the at least one processor 52, cause the apparatus at least to: receiving photoplethysmography, PPG, sensor data 12 of a user from a PPG sensor 22 comprised by an ear-worn device; receiving otoacoustic emission, OAE, data 14 of the user from a microphone 24 comprised by the ear-worn device; determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received PPG sensor data 12 and OAE data 14; and triggering at least one action based, at least in part, on determining that an onset of an acute motor disturbance is occurring. As illustrated in Fig 5A, the instructions, program, or code 56 may arrive at the apparatus 50 via any suitable delivery mechanism 53. The delivery mechanism 53 may be, for example, a machine readable medium, a computer-readable medium, a non-transitory computer-readable storage medium, a computer program product, a memory device, a record medium such as a Compact Disc Read-Only Memory (CD-ROM) or a Digital Versatile Disc (DVD) or a solid-state memory, an article of manufacture that comprises or tangibly embodies the computer program 56. The delivery mechanism may be a signal configured to reliably transfer the computer program 56. The apparatus 50 may propagate or transmit the computer program 56 as a computer data signal. The term “non-transitory” as used herein, is a limitation of the medium itself (i.e., tangible, not a signal) as opposed to a limitation on data storage persistency (e.g., RAM vs. ROM). Computer program instructions for causing an apparatus to perform at least the following or for performing at least the following: receiving photoplethysmography, PPG, sensor data 12 of a user from a PPG sensor 22 comprised by an ear-worn device; receiving otoacoustic emission, OAE, data 14 of the user from a microphone 24 comprised by the ear-worn device; determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received PPG sensor data 12 and OAE data 14; and triggering at least one action based, at least in part, on determining that an onset of an acute motor disturbance is occurring. The computer program instructions may be comprised in a computer program, a non-transitory computer readable medium, a computer program product, a machine readable medium. In some but not necessarily all examples, the computer program instructions may be distributed over more than one computer program. Although the memory 54 is illustrated as a single component / circuitry it may be implemented as one or more separate components / circuitry some or all of which may be integrated / removable and / or may provide permanent / semi-permanent / dynamic / cached storage. In examples the memory 54 comprises a random-access memory 58 and a read only memory 51. In examples the computer program 56 can be stored in the read only memory 51. See, for example, Fig. 5B. Although the processor 52 is illustrated as a single component / circuitry it may be implemented as one or more separate components / circuitry some or all of which may be integrated / removable. The processor 52 may be a single core or multi-core processor. References to ‘computer-readable storage medium’, ‘computer program product’, ‘tangibly embodied computer program’ etc. or a ‘controller’, ‘computer’, ‘processor’ etc. should be understood to encompass not only computers having different architectures such as single / multi- processor architectures and sequential (Von Neumann) / parallel architectures but also specialized circuits such as field-programmable gate arrays (FPGA), application specific circuits (ASIC), signal processing devices and other processing circuitry. References to computer program, instructions, code etc. should be understood to encompass software for a programmable processor or firmware such as, for example, the programmable 32 content of a hardware device whether instructions for a processor, or configuration settings for a fixed-function device, gate array or programmable logic device etc. As used in this application, the term ‘circuitry’ may refer to one or more or all the following: (a) hardware-only circuitry implementations (such as implementations in only analog and / or digital circuitry) and (b) combinations of hardware circuits and software, such as (as applicable): i. a combination of analog and / or digital hardware circuit(s) with software / firmware and ii. any portions of hardware processor(s) with software (including digital signal processor(s)), software, and memory or memories that work together to cause an apparatus, such as a mobile phone or server, to perform various functions and (c) hardware circuit(s) and or processor(s), such as a microprocessor(s) or a portion of a microprocessor(s), that requires software (for example, firmware) for operation, but the software may not be present when it is not needed for operation. This definition of circuitry applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term circuitry also covers an implementation of merely a hardware circuit or processor and its (or their) accompanying software and / or firmware. The term circuitry also covers, for example and if applicable to the claim element, a baseband integrated circuit for a mobile device or a similar integrated circuit in a server, a cellular network device, or other computing or network device. The blocks illustrated in the accompanying Figs may represent steps in a method and / or sections of code in the computer program 56. The illustration of a particular order to the blocks does not necessarily imply that there is a required or preferred order for the blocks and the order and arrangement of the block may be varied. Furthermore, it may be possible for some blocks to be omitted. Where a structural feature has been described, it may be replaced by means for performing one or more of the functions of the structural feature whether that function or those functions are explicitly or implicitly described. In examples, an apparatus can comprise means for performing one or more methods, or at least part of one or more methods, as disclosed herein. In examples, an apparatus can be configured to perform one or more methods, or at least a part of one or more methods, as disclosed herein. The above-described examples find application as enabling components of: automotive systems; telecommunication systems; electronic systems including consumer electronic products; distributed computing systems; media systems for generating or rendering media content including audio, visual and audio visual content and mixed, mediated, virtual and / or augmented reality; personal systems including personal health systems or personal fitness systems; navigation systems; user interfaces also known as human machine interfaces; networks including cellular, non-cellular, and optical networks; ad-hoc networks; the internet; the internet of things; virtualized networks; and related software and services. The apparatus can be provided in an electronic device, for example, a mobile terminal, according to an example of the present disclosure. It should be understood, however, that a mobile terminal is merely illustrative of an electronic device that would benefit from examples of implementations of the present disclosure and, therefore, should not be taken to limit the scope of the present disclosure to the same. While in certain implementation examples, the apparatus can be provided in a mobile terminal, other types of electronic devices, such as, but not limited to: mobile communication devices, hand portable electronic devices, wearable computing devices, portable digital assistants (PDAs), pagers, mobile computers, desktop computers, televisions, gaming devices, laptop computers, cameras, video recorders, GPS devices and other types of electronic systems, can readily employ examples of the present disclosure. Furthermore, devices can readily employ examples of the present disclosure regardless of their intent to provide mobility. The term ‘comprise’ is used in this document with an inclusive not an exclusive meaning. That is any reference to X comprising Y indicates that X may comprise only one Y or may comprise more than one Y. If it is intended to use ‘comprise’ with an exclusive meaning then it will be made clear in the context by referring to ‘comprising only one...’ or by using ‘consisting.’ In this description, the wording ‘connect’, ‘couple’ and ‘communication’ and their derivatives mean operationally connected / coupled / in communication. It should be appreciated that any number or combination of intervening components can exist (including no intervening components), i.e., to provide direct or indirect connection / coupling / communication. Any such intervening components can include hardware and / or software components. As used herein, the term "determine / determining" (and grammatical variants thereof) can include, not least: calculating, computing, processing, deriving, measuring, investigating, identifying, looking up (for example, looking up in a table, a database, or another data structure), ascertaining and the like. Also, "determining" can include receiving (for example, receiving information), accessing (for example, accessing data in a memory), obtaining and the like. Also," determine / determining" can include resolving, selecting, choosing, establishing, and the like. In this description, reference has been made to various examples. The description of features or functions in relation to an example indicates that those features or functions are present in that example. The use of the term ‘example’ or ‘for example’ or ‘can’ or ‘may’ in the text denotes, whether explicitly stated or not, that such features or functions are present in at least the described example, whether described as an example or not, and that they can be, but are not necessarily, present in some of or all other examples. Thus ‘example’, ‘for example’, ‘can’, or ‘may’ refers to a particular instance in a class of examples. A property of the instance can be a property of only that instance or a property of the class or a property of a sub-class of the class that includes some but not all the instances in the class. It is therefore implicitly disclosed that a feature described with reference to one example but not with reference to another example, can where possible be used in that other example as part of a working combination but does not necessarily have to be used in that other example. As used herein, “at least one of the following: ” and “at least one of ” and similar wording, where the list of two or more elements are joined by “and” or “or” mean at 35 least any one of the elements, or at least any two or more of the elements, or at least all the elements. Although examples have been described in the preceding paragraphs with reference to various examples, it should be appreciated that modifications to the examples given can be made without departing from the scope of the claims. Features described in the preceding description may be used in combinations other than the combinations explicitly described above. Although functions have been described with reference to certain features, those functions may be performable by other features whether described or not. The description of a feature, such as an apparatus or a component of an apparatus, configured to perform a function, or for performing a function, should additionally be considered to also disclose a method of performing that function. For example, description of an apparatus configured to perform one or more actions, or for performing one or more actions, should additionally be considered to disclose a method of performing those one or more actions with or without the apparatus. Although features have been described with reference to certain examples, those features may also be present in other examples whether described or not. The term ‘a’, ‘an’ or ‘the’ is used in this document with an inclusive not an exclusive meaning. That is any reference to X comprising a / an / the Y indicates that X may comprise only one Y or may comprise more than one Y unless the context clearly indicates the contrary. If it is intended to use ‘a’, ‘an’ or ‘the’ with an exclusive meaning then it will be made clear in the context. In some circumstances the use of ‘at least one’ or ‘one or more’ may be used to emphasis an inclusive meaning but the absence of these terms should not be taken to infer any exclusive meaning. The presence of a feature (or combination of features) in a claim is a reference to that feature or (combination of features) itself and to features that achieve substantially the same technical effect (equivalent features). The equivalent features include, for example, features that are variants and achieve substantially the same result in substantially the same way. The equivalent features include, for example, features that perform substantially the same function, in substantially the same way to achieve substantially the same result. In this description, reference has been made to various examples using adjectives or adjectival phrases to describe characteristics of the examples. Such a description of a characteristic in relation to an example indicates that the characteristic is present in some examples exactly as described and is present in other examples substantially as described. The above description describes some examples of the present disclosure however those of ordinary skill in the art will be aware of possible alternative structures and method features which offer equivalent functionality to the specific examples of such structures and features described herein above and which for the sake of brevity and clarity have been omitted from the above description. Nonetheless, the above description should be read as implicitly including reference to such alternative structures and method features which provide equivalent functionality unless such alternative structures or method features are explicitly excluded in the above description of the examples of the present disclosure. Whilst endeavoring in the foregoing specification to draw attention to those features believed to be of importance the Applicant may seek protection via the claims in respect of any patentable feature or combination of features hereinbefore referred to and / or shown in the drawings whether or not emphasis has been placed thereon. l / we claim:

Claims

1. An apparatus comprising means for:receiving photoplethysmography, PPG, sensor data (12) of a user from aPPG sensor (22) comprised by an ear-worn device;receiving otoacoustic emission, OAE, data (14) of the user from a microphone (24) comprised by the ear-worn device;determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received PPG sensor data (12) and OAE data (14); andtriggering at least one action based, at least in part, on determining that an onset of an acute motor disturbance is occurring.

2. An apparatus as claimed in claim 1, wherein the at least one action comprises at least one of the following:outputting an alert to the user to inform the user of the onset of the acute motor disturbance; oroutputting at least one sound from a speaker comprised by the ear-worn device to mitigate the occurrence of the acute motor disturbance, or to prevent the occurrence of the acute motor disturbance.

3. An apparatus as claimed in any preceding claim, wherein the means are configured to:receive motion data (16) of the user from a motion sensor (26) comprised by the ear-worn device; anddetermine that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received motion data (16).

4. An apparatus as claimed in claim 3, wherein determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received motion data (16) comprises determining an occurrence of at least one abnormal movement pattern associated with at least one acute motor disturbance.

5. An apparatus as claimed in claim 3 or claim 4, wherein the means are configured to remove motion artifacts from the PPG sensor data (12) based, at least in part, on the received motion data (16).

6. An apparatus as claimed in any preceding claim, wherein determining that an onset of an acute motor disturbance is occurring for the user comprises determining at least one of the following:stress level of the user;attention level of the user; or cognitive load of the user.

7. An apparatus as claimed in any preceding claim, wherein the means are configured to determine a reference OAE level for the user, wherein determining that an onset of an acute motor disturbance is occurring for the user comprises comparing current OAE data (14) of the user with the reference OAE level for the user.

8. An apparatus as claimed in any preceding claim, wherein the means are configured to:temporally align the received PPG sensor data (12), and OAE; and determine that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the temporally aligned PPG sensor data (12), and OAE data (14).

9. An apparatus as claimed in any preceding claim, wherein the means are configured to:store at least a portion of at least one of the PPG sensor data (12), and OAE data (14); anddetermine that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the stored data.

10. An apparatus as claimed in any preceding claim, wherein an acute motor disturbance comprises at least one of the following:a seizure;tremors;sleep paralysis; orfreezing of gait.

11. An apparatus as claimed in any preceding claim, wherein an acute motor disturbance comprises a motor disturbance associated with a neurological disorder.

12. An electronic device comprising an apparatus as claimed in at least one of claims 1 to 11.

13. An electronic device as claimed in claim 12 wherein the electronic device is the ear-worn device or a handheld mobile device.

14. A method comprising:receiving photoplethysmography, PPG, sensor data (12) of a user from a PPG sensor (22) comprised by an ear-worn device;receiving otoacoustic emission, OAE, data (14) of the user from a microphone (24) comprised by the ear-worn device;determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received PPG sensor data (12) and OAE data (14); andtriggering at least one action based, at least in part, on determining that an onset of an acute motor disturbance is occurring.

15. A method as claimed in claim 14, wherein the at least one action comprises at least one of the following:outputting an alert to the user to inform the user of the onset of the acute motor disturbance; oroutputting at least one sound from a speaker comprised by the ear-worn device to mitigate the occurrence of the acute motor disturbance, or to prevent the occurrence of the acute motor disturbance.

16. A method as claimed in any of claims 14 or 15, comprising:receiving motion data (16) of the user from a motion sensor (26) comprised by the ear-worn device; anddetermining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received motion data (16).

17. A method as claimed in claim 16, wherein determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received motion data (16) comprises determining an occurrence of at least one abnormal movement pattern associated with at least one acute motor disturbance.

18. A method as claimed in claim 16 or claim 17, comprising removing motion artifacts from the PPG sensor data (12) based, at least in part, on the received motion data (16).

19. A method as claimed in any of claims 14 to 18, wherein determining that an onset of an acute motor disturbance is occurring for the user comprises determining at least one of the following:stress level of the user;attention level of the user; or cognitive load of the user.

20. A method as claimed in any of claims 14 to 19, comprising determining a reference OAE level for the user, wherein determining that an onset of an acute motor disturbance is occurring for the user comprises comparing current OAE data (14) of the user with the reference OAE level for the user.

21. A computer program comprising instructions for causing an apparatus to perform:receiving photoplethysmography, PPG, sensor data (12) of a user from a PPG sensor (22) comprised by an ear-worn device;receiving otoacoustic emission, OAE, data (14) of the user from a microphone (24) comprised by the ear-worn device;determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received PPG sensor data (12) and OAE data (14); andtriggering at least one action based, at least in part, on determining that an onset of an acute motor disturbance is occurring.

22. A computer program as claimed in claim 21, wherein the at least one action comprises at least one of the following:outputting an alert to the user to inform the user of the onset of the acute motor disturbance; oroutputting at least one sound from a speaker comprised by the ear-worn device to mitigate the occurrence of the acute motor disturbance, or to prevent the occurrence of the acute motor disturbance.

23. A computer program as claimed in any of claims 21 or 22, comprising instructions for causing an apparatus to perform:receiving motion data (16) of the user from a motion sensor (26) comprised by the ear-worn device; anddetermining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received motion data (16).

24. A computer program as claimed in claim 23, wherein determining that an onset of an acute motor disturbance is occurring for the user based, at least in part, on the received motion data (16) comprises determining an occurrence of at least one abnormal movement pattern associated with at least one acute motor disturbance.

25. A computer program as claimed in any of claims 21 to 24, wherein determining that an onset of an acute motor disturbance is occurring for the user comprises determining at least one of the following:stress level of the user;attention level of the user; or cognitive load of the user.

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