Method and system for detecting peaks in a signal indicative of a heartrate

Abstract

A method of detecting peaks such as R-peaks and removing spurious peaks in a heartrate signal such as an ECG signal. Subject specific information indicative of the signal-to-noise ratio of the signal

Description

The present invention is directed towards a method and system for detecting peaks such as R-peaks in a signal indicative of a heartrate such as an electrocardiogram signal. In particular, the present invention is directed towards methods and systems for improving the process for removing spurious detected peaks. Background Wearable articles, such as garments, incorporating sensors are wearable electronics used to measure and collect information from a wearer. Such wearable articles are commonly referred to as ‘smart clothing’. It is advantageous to measure biosignals of the wearer during exercise, or other scenarios. It is known to provide a garment, or other wearable article, to which an electronic device (i.e. an electronics module, and / or related components) is attached in a prominent position, such as on the chest or between the shoulder blades. Advantageously, the electronic device is a detachable device. The electronic device is configured to process the incoming signals, and the output from the processing is stored and / or displayed to a user in a suitable way A sensor senses a biosignal such as electrocardiogram (ECG) signals and the biosignals are coupled to the electronic device, via an interface. The sensors may be coupled to the interface by means of conductors which are connected to terminals provided on the interface to enable coupling of the signals from the sensor to the interface. Electronics modules for wearable articles such as garments are known to communicate with user electronic devices over wireless communication protocols such as Bluetooth ® and Bluetooth ® Low Energy. These electronics modules are typically removably attached to the wearable article, interface with internal electronics of the wearable article, and comprise a Bluetooth ® antenna for communicating with the user electronic device. The electronic device includes drive and sensing electronics comprising components and associated circuitry, to provide the required functionality. The drive and sensing electronics include a power source to power the electronic device and the associated components of the drive and sensing circuitry. ECG sensing is used to provide a plethora of information about a person’s heart. It is one of the simplest and oldest techniques used to perform cardiac investigations. In its most basic form, it provides an insight into the electrical activity generated within heart muscles that changes over time. By detecting and amplifying these differential biopotential signals, a lot of information can be gathered quickly, including the heart rate. Among professional medical staff, individual signals have names such as “the QRS complex,” which is the largest part of an ECG signal and is a collection of Q, R, and S signals, including the P and T waves. Whilst lay persons may not be aware of the clinical aspects and significance of an ECG signal trace, lay persons would usually recognise the general form of such a signal trace, if only as a measure of heart rate. Typically, the detected ECG signals can be displayed as a trace to a user for information. The user may be a clinician who is looking to assess cardiac health or may be a lay user using the electronics module as a fitness or health and wellness assessment device. A typical ECG waveform or trace is illustrated in Figure 1 showing the QRS complex. Figure 2 shows an ECG waveform of two successive heartbeats. The time difference between the two R-peaks in the ECG waveform is the inter-beat interval (IBI) also known as the R-R interval. IBI values can also be calculated based on the difference between other peaks such as between two successive S peaks. This time is usually expressed in milliseconds. IBI values represent the time between successive heartbeats. Calculating the IBI value requires that peaks are detected in the ECG waveform. Peak detection algorithms are known in the art. Example peak algorithms include the Pan Tomkins algorithm as described in Pan, Jiapu; Tompkins, Willis J. (March 1985). "A Real-Time QRS Detection Algorithm". IEEE Transactions on Biomedical Engineering. BME-32 (3): 230-236. Generally, peak detection algorithms involve comparing candidate peaks against a threshold level. Candidate peaks that are below the threshold level are used to calculate the IBI values. US Patent No. 8,032,206 B1 discloses a method of detecting a heart rate of a patient. The method comprises: sensing via a first sensor a first signal indicative of heartrate; sensing via a second sensor a second signal indicative of motion of the patient’s chest due to respiration; adjusting a threshold value of the amplitude of the first signal based on the second signal; and determining the heart rate as a function of the adjusted threshold value and the first signal. The threshold value is increased in response to a determination of inflation of a patient’s chest due to respiration. The threshold value is decreased in response to a determination of deflation of the patient’s chest due to respiration. The threshold value is kept constant in response to a determination of no substantial movement of the patient’s chest. An object of the present invention is to provide an improved process for detecting peaks from a signal indicative of the heartrate such as an ECG signal. Summary According to the present disclosure there is provided a method and electronics module as set forth in the appended claims. Other features of the invention will be apparent from the dependent claims, and the description which follows. According to a first aspect of the disclosure, there is provided a method of detecting peaks in a signal indicative of a heartrate for a subject. The method comprises obtaining subject specific information indicative of the signal-to-noise ratio of the signal. The method comprises obtaining signal values for the signal. The method comprises filtering the signal values. The method comprises detecting one or more candidate peaks in the filtered signal values. The method comprises determining if a detected candidate peak has an amplitude less than a threshold level, and if the detected candidate peak has an amplitude less than the threshold level, removing the detected candidate peaks. The threshold level is determined at least in part according to the subject specific information. In other words, the method comprises removing one or more detected candidate peaks that have an amplitude less than a threshold level. Advantageously, the threshold level is determined at least in part according to subject specific information that is indicative of the signal-to-noise level of the signal indicative of the heartrate (e.g. an ECG signal). The threshold level is therefore adaptable based on the subject specific information. The threshold level can change for different subjects or for different circumstances surrounding a certain subject. The threshold level being adaptable based on subject-specific information can improve the removal of spurious peaks and can reduce the likelihood of a true peak being accidentally removed. The previous approach described in US8,032,206 B1 only adjusts the threshold based on whether the subject is breathing in or out and does not consider the signal-to-noise ration of the signal. The existing approach is unable to dynamically compensate changes in noise level in the signal. The signal indicative of the heartrate may be an ECG signal but this is not required in all examples and other signals indicative of the heartrate are within the scope of the present disclosure. Other signals indicative of the heartrate include photoplethysmography (PPG) signals, ballistocardiogram (BCG) signals, and electromagnetic cardiogram (EMCG) signals. The subject specific information may indicate that the signal-to-noise ratio of the signal has changed. The method may comprise adjusting the threshold level based on the change in the signal-to-noise ratio. The method may further comprise increasing the threshold level when the subject specific information indicates that the signal-to-noise ratio has increased. The method may further comprise decreasing the threshold level when the subject specific information indicates that the signal-to-noise ratio has decreased. The threshold level may be determined according to the product of an adjustable threshold value and a measure of the power of the filtered signal values. The method may comprise adjusting the threshold value according to the subject specific information. The subject specific information may comprise activity data for the subject. The activity data may indicate an activity level for the subject. The activity data may indicate that an activity level for the subject has changed. The change in the activity level is indicative of a change in the signal-to-noise ratio of the signal. The method may further comprise adjusting the threshold level based on the change in activity level. The activity data may be indicative of an increase in the signal-to-noise ratio of the signal. The method may further comprise increasing the threshold level according to the increase in the signal-to-noise ratio. The activity data may be indicative of a decrease in the signal-to-noise ratio of the signal. The method may further comprise decreasing the threshold level according to the decrease in the signal-to-noise ratio. The subject specific information may comprise information indicating a characteristic of the subject. The characteristic of the subject may comprise one or more of the age, weight, body fat percentage, gender, ethnicity, fitness level, diet, medical history, or lifestyle of the subject. The method may further comprise adjusting the threshold level based on the characteristic of the subject. The method may further comprise calculating one or more I Bl values from the remaining peaks having an amplitude greater than the threshold level. The method may be for detecting R-peaks in an ECG signal. The IBI values may be R-R interval values. The peaks are not required to be R-peaks. The method may be for detecting other characteristic peaks of an ECG signal such as S-peaks or other characteristic peaks in signals indicative of the heartrate of the subject. According to a second aspect of the disclosure, there is provided a computer readable medium having instructions recorded thereon which, when executed by a processor, cause the processor to perform the method according to the first aspect of the disclosure. According to a third aspect of the disclosure, there is provided an electronics module for a wearable article. The electronics module comprises an interface arranged to couple with sensing components such as electrodes to record signals indicative of a heartrate for a subject. The electronics module comprises a controller. The controller is operable to obtain subject specific information indicative of the signal-to-noise ratio of the signal. The controller is operable to receive signal values for the signal from the interface. The controller is operable to filter the signal values. The controller is operable to detect one or more candidate peaks in the filtered signal values. The controller is operable to determine if a detected candidate peak has an amplitude less than a threshold level, and if the detected candidate peak has an amplitude less than the threshold level, remove the detected candidate peak. The threshold level is determined at least in part according to the subject specific information. In other words, the controller is operable to remove one or more detected peaks that have an amplitude less than a threshold level. The subject specific information may comprise information indicating a characteristic of the subject. The controller may be operable to receive the information indicating a characteristic of the subject from an external device such as a user electronic device. The subject specific information may comprise activity data for the subject. The electronics module may comprise a motion sensor arranged to detect a change in the activity level for the subject and provide, to the controller, activity data for the subject indicative of the change in activity level. The electronics module may be operable to adjust the threshold level based on the change in activity level. According to a fourth aspect of the disclosure, there is provided a method of detecting peaks in a signal indicative of a heartrate for a subject. The method comprises obtaining subject specific information indicative of the signal-to-noise ratio of the signal. The method comprises obtaining signal values for the subject. The method comprises adjusting a threshold level using the subject specific information. The method further comprises detecting one or more peaks in the signal values using the adjusted threshold level. The method may comprise any of the features of the first aspect of the disclosure. Brief Description of the Drawings Examples of the present disclosure will now be described with reference to the accompanying drawings, in which: Figure 1 illustrates a signal trace for an ECG signal; Figure 2 illustrates an ECG waveform that includes electrical signals for two successive heartbeats; Figure 3 shows a schematic diagram for an example system according to aspects of the present disclosure; Figure 4 shows a schematic diagram for an example electronics module according to aspects of the present disclosure; Figure 5 shows a schematic diagram for another example electronics module according to aspects of the present disclosure; Figure 6 shows a schematic diagram for an example analogue-to-digital converter used in the example electronics module of Figures 4 and 5 according to aspects of the present disclosure; Figure 7 shows a flow diagram for an example method of detecting R-peaks according to aspects of the present disclosure; Figure 8 shows an ECG waveform for a subject when standing; Figure 9 shows an ECG waveform for a subject when jogging; and Figure 10 shows an ECG waveform for a subject when running; and Figure 11 shows a flow diagram for another example method of detecting R-peaks according to aspects of the present disclosure. Detailed Description The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of various embodiments of the disclosure as defined by the claims and their equivalents. It includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the various embodiments described herein can be made without departing from the scope and spirit of the disclosure. In addition, descriptions of well-known functions and constructions may be omitted for clarity and conciseness. The terms and words used in the following description and claims are not limited to the bibliographical meanings but are merely used by the inventor to enable a clear and consistent understanding of the disclosure. Accordingly, it should be apparent to those skilled in the art that the following description of various embodiments of the disclosure is provided for illustration purpose only and not for the purpose of limiting the disclosure as defined by the appended claims and their equivalents. It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. “Wearable article” as referred to throughout the present disclosure may refer to any form of device interface which may be worn by a user such as a smart watch, necklace, garment, bracelet, or glasses. The wearable article may be a textile article. The wearable article may be a garment. The garment may refer to an item of clothing or apparel. The garment may be a top. The top may be a shirt, t-shirt, blouse, sweater, jacket / coat, or vest. The garment may be a dress, garment brassiere, shorts, pants, arm or leg sleeve, vest, jacket / coat, glove, armband, underwear, headband, hat / cap, collar, wristband, stocking, sock, or shoe, athletic clothing, personal protective equipment, including hard hats, swimwear, wetsuit or dry suit. The term “wearer” includes a user who is wearing, or otherwise holding, the wearable article. The type of wearable garment may dictate the type of biosignals to be detected. For example, a hat or cap may be used to detect electroencephalogram or magnetoencephalogram signals. The wearable article / garment may be constructed from a woven or a non-woven material. The wearable article / garment may be constructed from natural fibres, synthetic fibres, or a natural fibre blended with one or more other materials which can be natural or synthetic. The yarn may be cotton. The cotton may be blended with polyester and / or viscose and / or polyamide according to the application. Silk may also be used as the natural fibre. Cellulose, wool, hemp and jute are also natural fibres that may be used in the wearable article / garment. Polyester, polycotton, nylon and viscose are synthetic fibres that may be used in the wearable article / garment. The garment may be a tight-fitting garment. Beneficially, a tight-fitting garment helps ensure that the sensor devices of the garment are held in contact with or in the proximity of a skin surface of the wearer. The garment may be a compression garment. The garment may be an athletic garment such as an elastomeric athletic garment. The garment has sensing units provided on an inside surface which are held in close proximity to a skin surface of a wearer wearing the garment. This enables the sensing units to measure biosignals for the wearer wearing the garment. The sensing units may be arranged to measure one or more biosignals of a wearer wearing the garment. “Biosignal” as referred to throughout the present disclosure may refer to signals from living beings that can be continually measured or monitored. Biosignals may be electrical or nonelectrical signals. Signal variations can be time variant or spatially variant. Sensing components may be used for measuring one or a combination of bioelectrical, bioimpedance, biochemical, biomechanical, bioacoustics, biooptical or biothermal signals of the wearer 600. The bioelectrical measurements include electrocardiograms (ECG), electrogastrograms (EGG), electroencephalograms (EEG), and electromyography (EMG). The bioimpedance measurements include plethysmography (e.g., for respiration), body composition (e.g., hydration, fat, etc.), and electroimpedance tomography (EIT). The biomagnetic measurements include magneton eurograms (MNG), magnetoencephalography (MEG), magnetogastrogram (MGG), magnetocardiogram (MCG). The biochemical measurements include glucose / lactose measurements which may be performed using chemical analysis of the wearer 600’s sweat. The biomechanical measurements include blood pressure. The bioacoustics measurements include phonocardiograms (PCG). The biooptical measurements include orthopantomogram (OPG). The biothermal measurements include skin temperature and core body temperature measurements. The examples described below refer to algorithms for detecting R-peaks in electrocardiograms. It will be appreciated that the present disclosure is not limited to this example and other peaks and other types of signals indicative of the heartrate may be used. Referring to Figures 3 to 7, there is shown an example system 10 according to aspects of the present disclosure. The system 10 comprises an electronics module 100, a wearable article in the form of a garment 200, and a user electronic device 300. The garment 200 is worn by a user who in this embodiment is the wearer 600 of the garment 200. The electronics module 100 is arranged to integrate with sensing units 400 incorporated into the garment 200 to obtain signals from the sensing units 400. The electronics module 100 and the wearable article 200 and including the sensing units 400 comprise a wearable assembly 500. The sensing units 400 comprise one or more sensors 209, 211 with associated conductors 203, 207 and other components and circuitry. The electronics module 100 is further arranged to wirelessly communicate data to the user electronic device 300. Various protocols enable wireless communication between the electronics module 100 and the user electronic device 300. Example communication protocols include Bluetooth ®, Bluetooth ® Low Energy, and near-field communication (NFC). The garment 200 has an electronics module holder in the form of a pocket 201. The pocket 201 is sized to receive the electronics module 100. When disposed in the pocket 201, the electronics module 100 is arranged to receive sensor data from the sensing units 400. The electronics module 100 is therefore removable from the garment 200. The present disclosure is not limited to electronics module holders in the form pockets. The electronics module 100 may be configured to be releasably mechanically coupled to the garment 200. The mechanical coupling of the electronic module 100 to the garment 200 may be provided by a mechanical interface such as a clip, a plug and socket arrangement, etc. The mechanical coupling or mechanical interface may be configured to maintain the electronic module 100 in a particular orientation with respect to the garment 200 when the electronic module 100 is coupled to the garment 200. This may be beneficial in ensuring that the electronic module 100 is securely held in place with respect to the garment 200 and / or that any electronic coupling of the electronic module 100 and the garment 200 (or a component of the garment 200) can be optimized. The mechanical coupling may be maintained using friction or using a positively engaging mechanism, for example. Beneficially, the removable electronic module 100 may contain all the components required for data transmission and processing such that the garment 200 only comprises the sensing units 400 e.g. the sensors 209, 211 and communication pathways 203, 207. The sensors 209, 211 may be electrodes. In this way, manufacture of the garment 200 may be simplified. In addition, it may be easier to clean a garment 200 which has fewer electronic components attached thereto or incorporated therein. Furthermore, the removable electronic module 100 may be easier to maintain and / or troubleshoot than embedded electronics. The electronic module 100 may comprise flexible electronics such as a flexible printed circuit (FPC). The electronic module 100 may be configured to be electrically coupled to the garment 200. Referring to Figure 4, there is shown a schematic diagram of an example of the electronics module 100 of Figure 1. A more detailed block diagram of the electronics components of electronics module 100 and garment are shown in Figure 5. The electronics module 100 comprises an interface 101, a controller 103, a power source 105, and one or more communication devices which, in the exemplar embodiment comprises a first antenna 107, a second antenna 109 and a wireless communicator 159. The electronics module 100 also includes an input unit such as a proximity sensor or a motion sensor 111, for example in the form of an inertial measurement unit (IMU). The electronics module 100 also includes additional peripheral devices that are used to perform specific functions as will be described in further detail herein. The interface 101 is arranged to communicatively couple with the sensing unit 400 of the garment 200. The sensing unit 400 comprises - in this example - the two sensors 209, 211 coupled to respective first and second electrically conductive pathways 203, 207, each with respective termination points 213, 215. The interface 101 receives signals from the sensors 209, 211. The controller 103 is communicatively coupled to the interface 101 and is arranged to receive the signals from the interface 101 for further processing. The interface 101 of the embodiment described herein comprises first and second contacts 163, 165 which are arranged to be communicatively coupled to the termination points 213, 215 the respective first and second electrically conductive pathways 203, 207. The coupling between the termination points 213, 215 and the respective first and second contacts 163, 165 may be conductive ora wireless (e.g. inductive) communication coupling. In this example the sensors 209, 211 are used to measure electropotential signals such as electrocardiogram (ECG) signals, although the sensors 209, 211 could be configured to measure other biosignal types as also discussed above. In this embodiment, the sensors 209, 211 are configured for so-called dry connection to the wearer’s skin to measure ECG signals. The power source 105 may comprise a plurality of power sources. The power source 105 may be a battery. The battery may be a rechargeable battery. The battery may be a rechargeable battery adapted to be charged wirelessly such as by inductive charging. The power source 105 may comprise an energy harvesting device. The energy harvesting device may be configured to generate electric power signals in response to kinetic events such as kinetic events performed by the wearer 600 of the garment 200. The kinetic event could include walking, running, exercising or respiration of the wearer 600. The energy harvesting material may comprise a piezoelectric material which generates electricity in response to mechanical deformation of the converter. The energy harvesting device may harvest energy from body heat of the wearer 600 of the garment. The energy harvesting device may be a thermoelectric energy harvesting device. The power source 105 may be a super capacitor, or an energy cell. The first antenna 107 is arranged to communicatively couple with the user electronic device 300 using a first communication protocol. In the example described herein, the first antenna 107 is a passive tag such as a passive Radio Frequency Identification (RFID) tag or Near Field Communication (NFC) tag. These tags comprise a communication module as well as a memory which stores the information, and a radio chip. The user electronic device 300 is powered to induce a magnetic field in an antenna of the user electronic device 300. When the user electronic device 300 is placed in the magnetic field of the communication module antenna 107, the user electronic device 300 induces current in the communication module antenna 107. This induced current triggers the electronics module 100 to retrieve the information from the memory of the tag and transmit the same back to the user electronic device 300. In an example operation, the user electronic device 300 is brought into proximity with the electronics module 100. In response to this, the electronics module 100 is configured to energize the first antenna 107 to transmit information to the user electronic device 300 over the first wireless communication protocol. Beneficially, this means that the act of the user electronic device 300 approaching the electronics module 100 energizes the first antenna 107 to transmit the information to the user electronic device 300. The information may comprise a unique identifier for the electronics module 100. The unique identifier for the electronics module 100 may be an address for the electronics module 100 such as a MAC address or Bluetooth ® address. The information may comprise authentication information used to facilitate the pairing between the electronics module 100 and the user electronic device 300 over the second wireless communication protocol. This means that the transmitted information is used as part of an out of band (OOB) pairing process. The information may comprise application information which may be used by the user electronic device 300 to start an application on the user electronic device 300 or configure an application running on the user electronic device 300. The application may be started on the user electronic device 300 automatically (e.g. without wearer 600 input). Alternatively, the application information may cause the user electronic device 300 to prompt the wearer 600 to start the application on the user electronic device. The information may comprise a uniform resource identifier such as a uniform resource location to be accessed by the user electronic device, or text to be displayed on the user electronic device for example. It will be appreciated that the same electronics module 100 can transmit any of the above example information either alone or in combination. The electronics module 100 may transmit different types of information depending on the current operational state of the electronics module 100 and based on information it receives from other devices such as the user electronic device 300. The second antenna 109 is arranged to communicatively couple with the user electronic device 300 over a second wireless communication protocol. The second wireless communication protocol may be a Bluetooth ® protocol, Bluetooth ® 5 or a Bluetooth ® Low Energy protocol but is not limited to any particular communication protocol. In the present embodiment, the second antenna 109 is integrated into controller 103. The second antenna 109 enables communication between the user electronic device 300 and the controller 100 for configuration and set up of the controller 103 and the peripheral devices as may be required. Configuration of the controller 103 and peripheral devices utilises the Bluetooth ® protocol. The wireless communicator 159 may be an alternative, or in addition to, the first and second antennas107, 109. Other wireless communication protocols can also be used, such as used for communication over: a wireless wide area network (WWAN), a wireless metro area network (WMAN), a wireless local area network (WLAN), a wireless personal area network (WPAN), Bluetooth ® Low Energy, Bluetooth ® Mesh, Thread, Zigbee, IEEE 802.15.4, Ant, a Global Navigation Satellite System (GNSS), a cellular communication network, or any other electromagnetic RF communication protocol. The cellular communication network may be a fourth generation (4G) LTE, LTE Advanced (LTE-A), LTE Cat-M1, LTE Cat-M2, NB-loT, fifth generation (5G), sixth generation (6G), and / or any other present or future developed cellular wireless network. The electronics module 100 includes configured a clock unit in the form of a real time clock (RTC) 153 coupled to the controller 103 and, for example, to be used for data logging, clock building, time stamping, timers, and alarms. As an example, the RTC 153 is driven by a low frequency clock source or crystal operated at 32.768 Hz. The electronics module 100 also includes a location device 161 such as a GNSS (Global Navigation Satellite System) device which is arranged to provide location and position data for applications as required. In particular, the location device 161 provides geographical location data at least to a nation state level. Any device suitable for providing location, navigation or for tracking the position could be utilised. The GNSS device may include device may include Global Positioning System (GPS), BeiDou Navigation Satellite System (BDS) and the Galileo system devices. The power source 105 in this example is a lithium polymer battery 105. The battery 105 is rechargeable and charged via a USB C input 131 of the electronics module 100. Of course, the present disclosure is not limited to recharging via USB and instead other forms of charging such as inductive of far field wireless charging are within the scope of the present disclosure. Additional battery management functionality is provided in terms of a charge controller 133, battery monitor 135 and regulator 147. These components may be provided through use of a 30 dedicated power management integrated circuit (PMIC). The USB C input 131 is also coupled to the controller 131 to enable direct communication with the controller 103 with an external device if required. The controller 103 is communicatively connected to a battery monitor 135 so that that the controller 103 may obtain information about the state of charge of the battery 105. The controller 103 has an internal memory 167 and is also communicatively connected to an external memory 143 which in this example is a NAND Flash memory. The memory 143 is used to for the storage of data when no wireless connection is available between the electronics module 100 and a user electronic device 300. The memory 143 may have a storage capacity of at least 1GB and preferably at least 2 GB. The electronics module 100 also comprises a temperature sensor 145 and a light emitting diode 147 for conveying status information. The electronic module 100 also comprises conventional electronics components including a power-on-reset generator 149, a development connector 151, the real time clock 153 and a PROG header 155. Additionally, the electronics module 100 may comprise a haptic feedback unit 157 for providing a haptic (vibrational) feedback to the wearer 600. The wireless communicator 159 may provide wireless communication capabilities for the garment 200 and enables the garment to communicate via one or more wireless communication protocols to a remote server 700. Wireless communications may include : a wireless wide area network (WWAN), a wireless metro area network (WMAN), a wireless local area network (WLAN), a wireless personal area network (WPAN), Bluetooth ® Low Energy, Bluetooth ® Mesh, Bluetooth ® 5, Thread, Zigbee, IEEE 802.15.4, Ant, a near field communication (NFC), a Global Navigation Satellite System (GNSS), a cellular communication network, or any other electromagnetic RF communication protocol. The cellular communication network may be a fourth generation (4G) LTE, LTE Advanced (LTE-A), LTE Cat-M1, LTE Cat-M2, NB-loT, fifth generation (5G), sixth generation (6G), and / or any other present or future developed cellular wireless network. The electronics module 100 may additionally comprise a Universal Integrated Circuit Card (UICC) that enables the garment to access services provided by a mobile network operator (MNO) or virtual mobile network operator (VMNO). The UICC may include at least a read-only memory (ROM) configured to store an MNO or VMNO profile that the garment can utilize to register and interact with an MNO or VMNO. The UICC may be in the form of a Subscriber Identity Module (SIM) card. The electronics module 100 may have a receiving section arranged to receive the SIM card. In other examples, the UICC is embedded directly into a controller of the electronics module 100. That is, the UICC may be an electronic / embedded UICC (eUlCC). A eUlCC is beneficial as it removes the need to store a number of MNO profiles, i.e. electronic Subscriber Identity Modules (eSIMs). Moreover, eSIMs can be remotely provisioned to garments. The electronics module 100 may comprise a secure element that represents an 35 embedded Universal Integrated Circuit Card (eUlCC). In the present disclosure, the electronics module may also be referred to as an electronics device or unit. These terms may be used interchangeably. The controller 103 is connected to the interface 101 via an analog-to-digital converter (ADC) front end 139 and an electrostatic discharge (ESD) protection circuit 141. Figure 6 is a schematic illustration of the component circuitry for the ADC front end 139. The ADC front end 139 forms part of the interface that couples the garment sensors 209, 211 (e.g. electrodes 209, 211) to the controller 103. In the example described herein the ADC front end 139 is an integrated circuit (IC) chip which converts the raw analogue biosignal received from the sensors 209, 211 into a digital signal for further processing by the controller 103. ADC IC chips are known, and any suitable one can be utilised to provide this functionality. ADC IC chips for ECG applications include, for example, the MAX30003 chip produced by Maxim Integrated Products Inc. The ADC front end 139 includes an input 169 and an output 171. Raw biosignals from the electrodes 209, 211 are input to the ADC front end 139, where received signals are processed in an ECG channel 175 and subject to appropriate filtering such as notch filters, high pass and low pass filters for mains noise, static discharge and interference reduction as well as for reducing bandwidth prior to conversion to digital signals. The reduction in bandwidth is important to remove or reduce motion artefacts that give rise to noise in the signal due to movement of the sensors 209, 211 and the effect of impact forces on the electrical components. The output digital signals may be decimated to reduce the sampling rate prior to being passed to a serial programmable interface (SPI) 173 of the ADC front end 139. Signals are output to the controller 103 via the SPI 173. The digital signal values output to the controller 103 are stored in a FIFO data buffer. The controller 103 performs operations to detect R-peaks from the digital signal values. The operations are performed in real-time while the ADC front end 139 are outputting new digital signals to the controller 103. Figure 7 provides a flow diagram for an example method performed by the controller 103 for identifying R-peaks in the ECG signal using the ECG digital signal values for the subject obtained from the ADC front end 139. The R-peaks are used to calculate R-R interval values. In step S101 the controller 103 obtains subject specific information indicative of the signal-to-noise ratio of the ECG signal. In step S102 the controller 102 obtains the ECG signal values from the ADC front end 139. The subject specific information is different to the ECG signal values. The subject specific information provides further context to the ECG signal values and is used to adapt the thresholding process used to remove spurious R-peaks. The subject specific information may comprise information relating to the activity of the subject and / or a characteristic of the subject. In step S103 the controller 103 filters the ECG signal values. Many different filtering operations can be used to aid in the detection of R-peaks. Example filters include notch filters, low pass filters, high pass filters, and bandpass filters. In step S104, the controller 103 detects one or more candidate R-peaks in the filtered ECG signal values. Many peak detection approaches may be used. These peak detection approaches typically involve detecting local maxima in the ECG signal values. In step S105, the controller 103 removes detected candidate R-peaks that have an amplitude less than a threshold level. The controller 103 does this by determining if a detected candidate peak has an amplitude less than a threshold level, and if the detected candidate peak has an amplitude less than the threshold level, removing the detected candidate peak. The threshold level is determined at least in part according to the subject specific information. Advantageously, this means that the threshold level is set based on subject specific information indicative of the signal-to-noise ratio of the ECG signal. This improves the accuracy of the R-peak detection method as it is adaptable based on particular properties of the subject. The subject specific information is indicative of a signal-to-noise ratio for the ECG signal. If the subject is likely to have a high signal-to-noise ratio for their ECG signal then a high threshold level can be selected because the true R-peaks in the ECG signal are likely to have a far higher amplitude than spurious peaks caused by noise, for example. If the subject is likely to have a low signal-to-noise ratio then a low threshold level can be selected because the true R-peaks in the signal are likely to have a lower amplitude and may be missed if the threshold level is too high. The present disclosure advantageously configures the threshold level according to the likely signal-to-noise ratio for the ECG signal as identified by the subject specific information. The signal-to-noise ratio is not calculated directly from the ECG signal. Instead, the subject specific information provides information relating to one or more properties of the subject that indicate a likely signal-to-noise ratio. This is computationally more efficient than calculating the signal-to-noise ratio directly and allows for real time R-peak detection and removal even at a high sampling rate such as 512 Hz. In some examples, the subject specific information for the subject comprises activity data for the subject such as an activity level of the subject. If the user has a high activity level (such as when they are running), the signal-to-noise ratio for the ECG signal is likely to be lower due to the introduction of greater motion artefacts. By adjusting the threshold level to compensate for the decreased signal-to-noise ratio, the controller 103 is able to reduce the number of missed true R-peaks. Meanwhile, if the user has a low activity level (such as when they are sitting or standing), the signal-to-noise ratio for the ECG signal is likely to be higher. By adjusting the threshold level to compensate for the increased signal-to-noise ratio, the controller 103 is able to remove a greater number of spurious peaks. In some examples, the subject specific information for the subject comprises information indicating a characteristic of the subject. Different characteristics of the user can be indicative of different signal-to-noise ratios that will be expected in the ECG signal. Factors such as gender, weight, and body fat level are indicative of different signal-to-noise ratios in the ECG signals for the subject. The controller 103 is able to adapt the threshold based on the particular characteristics of the subject to provide subject-specific R-peak detection. The R-peaks remaining after step S105 are used to calculate one or more R-R intervals for the subject. Only one R-R interval may be determined if only one R-peak remains after step S105. In this case, the R-R interval will be determined using the timestamp of the last R-peak found in the previous window of data. One or more additional steps may be performed prior to calculating the R-R intervals such as to further check the remaining peaks after step S105 and remove or compensate for any remaining spurious peaks. Although the number and extent of the spurious remaining speaks is at least reduced due to the subject specific information being used in setting the threshold level. These spurious peaks may be due to noise spikes, ectopic beats or other ECG components. An example process for detecting spurious peaks is disclosed in Mateo J, Laguna P. Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal. IEEE Trans Biomed Eng. 2003 Mar;50(3):334-43. Steps S104 and S105 may only be performed when at least N filtered ECG signal values are obtained. Here, N is a number that may be selected by the skilled person as desired to ensure that there are likely to be a certain desired number of peaks within the window of filtered signal values. For example, N may be selected such that filtered ECG signal values corresponding to at least 4 seconds of data are obtained to ensure that there are at least 2 peaks in any window. The number N will depend on the sampling rate of the signal values provided to the controller 103. For example, if the sampling rate is 512Hz and at least 4 seconds of data are required, then N = 2048. Other values of N are within the scope of the present disclosure. If less than N samples of filtered signal values have been obtained then method returns to step S101 so that additional samples are gathered and filtered. Steps S101 and S102 may be repeated until the N signal values are obtained. If N or more samples of the filtered signal values have been obtained, then steps S104 and S105 are performed. An example implementation of removing R peaks using subject specific information in the form of activity data for the user will now be described. In this example, motion detection is provided by the IMU 111 which may comprise an accelerometer and optionally one or both of a gyroscope and a magnetometer. A gyroscope / magnetometer is not required in all examples, and instead only an accelerometer may be provided, or a gyroscope / magnetometer may be present but put into a low power state. Known examples of IMUs that can be used for this application include the ST LSM6DSOX manufactured by STMicroelectronics. This IMU a system-in-package IMU featuring a 3D digital accelerometer and a 3D digital gyroscope. Another example of a known IMU suitable for this application is the LSM6DSO also be STMicroelectronics. The IMU 111 can include machine learning functionality, for example as provided in the ST LSM6DSOX. The machine learning functionality is implemented in a machine learning core (MLC). The machine earning processing capability uses decision-tree logic. The MLC is an embedded feature of the IMU 111 and comprises a set of configurable parameters and decision trees. As is understood in the art, decision tree is a mathematical tool composed of a series of configurable nodes. Each node is characterized by an “if-then-else” condition, where an input signal (represented by statistical parameters calculated from the sensor data) is evaluated against a threshold. Decision trees are stored and generate results in the dedicated output registers. The results of the decision tree can be read from the application processor at any time. Furthermore, there is the possibility to generate an interrupt for every change in the result in the decision tree, which is beneficial in maintaining low-power consumption. Decision trees can be generated using known machine learning tool such as Weka developed by the University of Waikato or using MATLAB or Python. A processor of the IMU 111 may perform processing tasks to classify different types of detected motion. The processor of the IMU 111 may use the machine-learning functions so as to perform this classification. Performing the processing operations on the IMU 111 rather than the controller 103 is beneficial as it reduces power consumption and leaves the controller 103 free to perform other tasks. In addition, it allows for motion events to be detected even when the controller 103 is operating in a low power mode. The IMU 111 communicates with the controller 103 over a serial protocol such as the Serial Peripheral Interface (SPI), Inter-Integrated Circuit (I2C), Controller Area Network (CAN), and Recommended Standard 232 (RS-232). Other serial protocols are within the scope of the present disclosure. The IMU 111 outputs activity data to the controller 103 when the IMU 111 detects that the activity level of the subject has changed. This may occur when the subject switches from a first activity associated with a high signal-to-noise ratio to a second activity associated with a lower signal-to-noise ratio or vice versa. For example, the IMU 111 may detect that the user switches from sitting to standing or walking to running. Other activities may be detected by the IMU 111 such as jumping, cycling or swimming. The subject may initially be standing. Figure 8 shows a plot of an ECG signal trace for a subject when standing. The ECG signal has a high signal-to-noise ratio with clearly defined R-peaks and limited noise between the R-peaks. The IMU 111 detects that the user has the first activity level (standing) and outputs a signal representative of the first activity level to the controller 103. As mentioned above, the IMU 111 of the electronics module 100 can be configured to use decision tree logic to determine the activity level of the subject wearing the electronics module 100 and to provide an output to the controller 103. The output signal may for example 0=lying down, 1 =standing, 2=walking, 3=jogging, and 4=running. Therefore, the IMU 111 may output a value of “1 ” to the controller 103. The IMU 111 is not required to perform the activity classification in all examples. The controller 103 may, for example, determine an activity level of the subject from activity data received from the IMU 111. This may be performed using machine-learning logic implemented by the controller 103. The controller 103 identifies from the received signal that the user has an activity level associated with a high signal-to-noise ratio for the ECG signal. The controller 103 adjusts the threshold level based on the received signal so as to optimise the peak removal process for the signal-to-noise ratio of the ECG signal. Subsequent iterations of steps S101 to S105 use the adjusted threshold level. The adjusted threshold level is used until another signal is received by the controller 103 from the IMU 111 that indicates that the activity level of the subject has changed. The subject may begin to jog. Figure 9 shows a plot of an ECG signal trace for a subject when jogging. The ECG signal has a lower signal-to-noise ratio than Figure 8. The amplitude of the R- peaks vary and there is more noise between the R-peaks. The IMU 111 detects that the user is now jogging and outputs a signal representative of the new activity level to the controller 103, e.g. the IMU 111 may output a value of “3”. The controller 103 identifies from the received signal that the user has an activity level associated with a lower signal-to-noise ratio for the ECG signal. The controller 103 adjusts the threshold level based on the received signal so as to optimise the peak removal process for the signal-to-noise ratio of the ECG signal. Subsequent iterations of steps S101 to S105 use the adjusted threshold level. The adjusted threshold level is used until another signal is received by the controller 103 from the IMU 111 that indicates that the activity level of the subject has changed. The subject may begin to run. Figure 10 shows a plot of an ECG signal trace for a subject when running. The ECG signal has a lower signal-to-noise ratio than Figures 8 and 9. The amplitude of the R-peaks vary and there is more noise between the R-peaks. The IMU 111 detects that the user is now running and outputs a signal representative of the new activity level to the controller 103, e.g. the IMU 111 may output a value of “4”. The controller 103 identifies from the received signal that the user has an activity level associated with a lower signal-to-noise ratio for the ECG signal. The controller 103 adjusts the threshold level based on the received signal so as to optimise the peak removal process for the signal-to-noise ratio of the ECG signal. In this example, the threshold level is only adjusted when the activity level for the subject changes and a signal representing the change in activity level is received from the IMU 111. This is computationally more efficient than continually adjusting the threshold level. An example implementation of subject specific information in the form of information characteristic of the subject for the user will now be described. In this example, the controller 103 receives the information characteristic of the subject from the user electronic device 300. The controller 103 may also directly obtain the information without the use of the user electronic device 300. The user electronic device 300 is configured to launch an application via which the subject or other user can input information characteristic of the subject. The information characteristic of the subject may include one or more of the age, weight, body fat level, gender, ethnicity, fitness level, diet, medical history, or lifestyle of the subject. The information is not required to be manually input by the user and instead could be automatically obtained by the user electronic device 300 or the electronics module 100 from a data source such as biosignal data for the subject. The controller 103 uses the information to determine a likely signal-to-noise ratio of the ECG signal. The controller 103 adjusts the threshold level based on the likely signal-to-noise ratio. Subsequent iterations of steps S101 to S105 use the adjusted threshold level. The adjusted threshold level is used until further information is received by the controller 103 indicating a change in the characteristics of the subject. This information may indicate a change in, for example, the age or weight of the subject of may indicate that a different subject is now using the electronics module 100. In some examples, the subject specific information includes both the information characteristic of the subject and the activity level of the subject. User electronic devices in accordance with the present invention are not limited to mobile phones or tablets and may take the form of any electronic device which may be used by a user to perform the methods according to aspects of the present invention. The user electronic device 300 may be a electronics module such as a smartphone, tablet personal computer (PC), mobile phone, smart phone, video telephone, laptop PC, netbook computer, personal digital assistant (PDA), mobile medical device, camera or wearable device. The user electronic device 300 may include a head-mounted device such as an Augmented Reality, Virtual Reality or Mixed Reality headmounted device. The user electronic device 300 may be desktop PC, workstations, television apparatus or a projector, e.g. arranged to project a display onto a surface. Figure 11 provides a flow diagram for an example method performed by the controller 103 for calculating the R-R intervals from the digital signals stored in the FIFO data buffer. This method can use the threshold level set according to the example of Figure 7. In step S201, the controller 103 reads signal values from the data buffer. Each of the signal values is a value that represents the amplitude of the ECG signal at a particular time point. In step S202, the controller 103 detrends the signal values so as to remove baseline wander and / or other low frequency components. In an example operation, the controller 103 calculates the trend in the signal values and then subtracts the calculated trend from each of the signal values. Calculating the trend comprises identifying the maximum and minimum signal values read from the data buffer. The maximum signal value is added to a buffer that stores the maximum signal values obtained over time. The minimum signal value is added to a buffer that stores the minimum signal values obtained overtime. The current trend is then calculated by calculating the average of the maximum value stored in the buffer of maximum signal values and the minimum value stored in the buffer of minimum signal values. As explained above, the detrended signal values are calculated by subtracting the calculated current trend from each of the signal values. The detrended signal values are added to a FIFO detrended signal buffer. In step S203, the detrended signal values are filtered. The filtering is performed to remove components from the signal that do not resemble R-peaks. A bandpass filter centred around the frequency associated with the shape and width of the R-peak can be used to perform this task. Some filtering approaches use a bandpass filter with a central frequency in the range of 17 to 19 Hz. HR or FIR filters may be used, however, they are generally not effective due to ripples and lobes that may be present around the R-peaks in the ECG signal. The interaction between these secondary peaks and other components of the ECG signal can lead to ambiguity in the identity of the actual main peak. Preferred bandpass filtering approaches analyse a signal of the instantaneous amplitude associated with the R-peak frequency. These approaches exploit the fact that R-peaks are approximately symmetrical features which means that the location of the peak in the spectral amplitude is normally close to the location of the centre of the R-peak itself. The signal of instantaneous amplitude can be obtained using a complex filter and by calculating the absolute magnitude of the real and imaginary component for each filtered signal value. In a preferred implementation, the complex filter used is a complex Morlet wavelet. The Morlet wavelet has optimal frequency resolution due to its Gaussian envelope. The Morlet wavelet is also useful because it is symmetrical across the y-axis which means that only half of the filter coefficients need to be stored in RAM. The filtered signal values are added to a FIFO filtered signal buffer. In step S204, the controller 103 determines whether at least N filtered signal values have been obtained. Here, N is a number that may be selected by the skilled person as desired to ensure that there are likely to be a certain desired number of peaks within the window of filtered signal values. For example, N may be selected such that the filtered signal buffer contains at least 4 seconds of data to ensure that there are at least 2 peaks in any window. The number N will depend on the sampling rate of the signal values provided to the controller 103. For example, if the sampling rate is 512Hz and at least 4 seconds of data are required, then N = 2048. Other values of N are within the scope of the present disclosure. If less than N samples of filtered signal values have been obtained then the method returns to step S101 so that additional samples are gathered, filtered, and added to the filtered signal buffer. Steps S201 to S204 are repeated until the N signal values are obtained. If N or more samples of the filtered signal values have been obtained, the method proceeds to step S205. In step S205, the controller 103 detects peaks in the filtered signal values. At this stage, the controller 103 is identifying any peaks, including small and spurious peaks, in the filtered signal values. The peak detection process identifies local maxima in the signal values. Peak detection can be performed by simply looking for negative gradients in the filtered signal values. In step S206, the controller 103 removes detected peaks that have an amplitude less than a threshold level. The thresholding process is intended to remove peaks that are not R-peaks in the ECG signal. The thresholding level is determined according to an adjustable threshold value multiplied by the average spectral power for the filtered signal values. Using the average spectral power enables the thresholding level to adapt based on the power of the signal. The adjustable threshold value can be set according to the method of Figure 7. In step S207, R-R intervals are calculated for the remaining peaks. R-R intervals are calculated by calculating the difference between time stamps for consecutive R-peaks. Only one R-R interval may be determined if only one R-peak remains after step S106. The R-R interval will be determined using the timestamp of the last R-peak found in the previous window of data. One or more additional steps may be performed prior to step S207 to such as to check the remaining peaks after step S206 and remove or compensate for spurious remaining peaks. These spurious peaks may be due to noise spikes, ectopic beats or other ECG components. An example process for detecting spurious peaks is disclosed in Mateo J, Laguna P. Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal. IEEE Trans Biomed Eng. 2003 Mar;50(3):334-43. In summary, there is provided a method and electronics module for detecting R-peaks in an electrocardiogram, ECG, signal for a subject. Subject specific information indicative of the signal-to-noise ratio of the ECG signal is obtained (S101). ECG signal values for the subject are obtained (S102). The ECG signal values are filtered (S103). One or more candidate R-peaks in the filtered ECG signal values are detected (S104). One or more detected candidate R-peaks that have an amplitude less than a threshold level are removed (S105). The threshold level is determined at least in part according to the subject specific information. In some embodiments, the described elements may be configured to reside on a tangible, persistent, addressable storage medium and may be configured to execute on one or more processors. These functional elements may in some embodiments include, by way of example, components, such as software components, object-oriented software components, class components and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. Although the example embodiments have been described with reference to the components, modules and units discussed herein, such functional elements may be combined into fewer elements or separated into additional elements. Various combinations of optional features have been described herein, and it will be appreciated that described features may be combined in any suitable combination. In particular, the features of any one example embodiment may be combined with features of any other embodiment, as appropriate, except where such combinations are mutually exclusive. Throughout this specification, the term “comprising” or “comprises” means including the component(s) specified but not to the exclusion of the presence of others. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and / or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and / or steps are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features. The invention is not restricted to the details of the foregoing embodiment(s). The invention extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying claims, abstract and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed.

Claims

1. A method of detecting peaks in signal indicative of a heartrate for a subject, the method comprising:obtaining subject specific information indicative of the signal-to-noise ratio of the signal;obtaining signal values for the signal;filtering the signal values;adjusting a threshold level based on a change in the signal-to-noise ratio indicated by the subject specific information, the adjusting comprising increasing the threshold level when the subject specific information indicates that the signal-to-noise ratio has increased and decreasing the threshold level when the subject specific information indicates that the signal-to-noise ratio has decreased,detecting one or more candidate peaks in the filtered signal values; anddetermining if a detected candidate peak has an amplitude less than the threshold level, and if the detected candidate peak has an amplitude less than the threshold level, removing the detected candidate peak.

2. A method as claimed claim 1, wherein the threshold level is determined according to the product of an adjustable threshold value and a measure of the power of the filtered signal values, and wherein the method comprises adjusting the adjustable threshold value according to the subject specific information.

3. A method as claimed in any preceding claim, wherein the subject specific information comprises activity data for the subject.

4. A method as claimed in claim 3, wherein the activity data indicates an activity level for the subject.

5. A method as claimed in claim 3 or 4, wherein the activity data indicates that an activity level for the subject has changed.

6. A method as claimed in claim 5, further comprising adjusting the threshold level based on the change in activity level.

7. A method as claimed in any of claims 3 to 6, wherein the activity data is indicativeof an increase in the signal-to-noise ratio of the signal, and wherein the method further comprises increasing the threshold level according to the increase in the signal-to-noise ratio.

8. A method as claimed in any of claims 3 to 7, wherein the activity data is indicative of a decrease in the signal-to-noise ratio of signal, and wherein the method further comprises decreasing the threshold level according to the decrease in the signal-to-noise ratio.

9. A method as claimed in any preceding claim, wherein the subject specific information comprises information indicating a characteristic of the subject.

10. A method as claimed in claim 9, the wherein the characteristic of the subject comprises one or more of the age, weight, body fat level, gender, ethnicity, fitness level, diet, medical history, or lifestyle of the subject.

11. A method as claimed in claim 9 or 10, further comprising adjusting the threshold level based on the characteristic of the subject.

12. A method as claimed in any preceding claim, further comprising calculating one or more inter-beat interval, IBI, values from the remaining peaks having an amplitude greater than the threshold level.

13. A method as claimed in any preceding claim, wherein the signal is an electrocardiogram signal.

14. A method as claimed in any of claims 1 to 12, wherein the signal is a photoplethysmography signal.

15. A method as claimed in any of claims 1 to 12, wherein the signal is an electromagnetic cardiogram signal.

16. A computer readable medium having instructions recorded thereon which, when executed by a processor, cause the processor to perform the method as claimed in any of claims 1 to 15.

17. An electronics module for a wearable article, the electronics module comprising: an interface arranged to couple with sensing components to record signals indicative of a heartrate for a subject; anda controller operable to:obtain subject specific information indicative of the signal-to-noise ratio of the signal;receive signal values for the signal from the interface;filter the signal values;adjust a threshold level based on a change in the signal-to-noise ratio indicated by the subject specific information, wherein the threshold level is increased when the subject specific information indicates that the signal-to-noise ratio has increased and the threshold level is decreased when the subject specific information indicates that the signal-to-noise ratio has decreased,detect one or more candidate peaks in the filtered signal values; anddetermine if a detected candidate peak has an amplitude less than the threshold level, and if the detected candidate peak has an amplitude less than the threshold level, remove the detected candidate peak,.

18. An electronics module as claimed in claim 17, wherein the subject specific information comprises information indicating a characteristic of the subject.

19. An electronics module as claimed in claim 18, wherein the controller is operable to receive the information indicating a characteristic of the subject from an external device.

20. An electronics module as claimed in any of claims 17 to 19, wherein the subject specific information comprises activity data for the subject.

21. An electronics module as claimed in claim 20, wherein the electronics module comprises a motion sensor arranged to detect a change in the activity level for the subject and provide, to the controller, activity data for the subject indicative of the change in activity level.

22. An electronics module as claimed in claim 21, wherein the electronics module is operable to adjust the threshold level based on the change in activity level.

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