GB2612303A

GB2612303A - Electronics module for a wearable article

Info

Publication number: GB2612303A
Application number: GB2114990.1A
Authority: GB
Inventors: William Headley Gregory; David Driscoll Connor
Original assignee: Prevayl Innovations Ltd
Current assignee: Prevayl Innovations Ltd
Priority date: 2021-10-20
Filing date: 2021-10-20
Publication date: 2023-05-03
Also published as: GB202114990D0; WO2023067315A1

Abstract

An electronics module, for a wearable article, comprises a front end that supplies 904 digital signal values obtained 902 from biological signals from sensor(s) to a controller of the electronics module. The controller stores 908 the values in a buffer and performs 910 a signal processing operation that comprises: (a) reading M of the values from the buffer; (b) detecting extrema in the M values; and (c) determining whether at least N>M of signal values have been read from the buffer during the signal processing operation. If fewer than N signal values have been read from the buffer, the controller repeats steps (a) - (c) until N signal values have been read from the buffer. Otherwise, the controller uses the extrema detected in the signal processing operation to determine a biological metric and repeats the performance of the signal processing operation. The biological signals may be indicative of heart rate or breathing rate and may be ECG, bioimpedance or PPG signals.

Description

ELECTRONICS MODULE FOR A WEARABLE ARTICLE

The present disclosure is directed towards an electronics module for a wearable article and a method performed by the electronics module. In particular, the present disclosure is directed towards efficient processing operations performed by the electronics module for determining a biological metric such as a breathing rate or heartrate of a wearer of the wearable article.

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 biological signals (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 electronic module, and/or related components) is attached in a prominent position, such as on the chest. 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 biosignals such as electrocardiogram (ECG) signals and the biosigna Is are coupled to the electronic device, via a communication interface of the wearable article.

The sensors may be coupled to the interface by means of conductors which are connected to terminals provided on the communication interface to enable coupling of the signals from the sensor to the communication interface.

Electronics modules for wearable articles such as garments are known to communicate with user electronic devices over wireless communication protocols such as Bluetooth 0 and Bluetooth 0 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 0 antenna for communicating with the user electronic device.

The electronics module 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 heartrate.

Typically, the detected ECG signals can be displayed as a trace to a user for information. Alternatively, or in addition to a signal trace, information can be derived from raw ECG signals through digital signal processing and displayed or presented to the user in other ways, for example such as simple hear rate figures in beats per minute.

The trace and/or the additional information can be displayed or presented to a user on a user electronic device such as a mobile phone. Within the context of the present disclosure the user can be a wearer of the electronics module of any other user of the electronics module.

Another useful metric is the breathing rate of the wearer. The breathing rate can be determined from heart rate variability data but is generally more accurately determined from bioimpedance data. Changes in tidal volume within the chest cavity are captured by recording bioimpedance signals (impedance pneumography). Hence, a cyclic wave is observed, which represents the inhalation and exhalation of the wearer. An algorithm analyses the recorded waveform to pick peak inhalations and compute a "breath time" as measured by the time peaks. This is then converted into a breaths-per-minute measure. Example algorithms for determining the breathing rate are disclosed in Schafer A, Kratky KW. Estimation of breathing rate from respiratory sinus arrhythmia: comparison of various methods. Ann Biomed Eng. 2008 Mar;36(3):476-85. doi: 10.1007/s10439-007-9428-1. Epub 2008 Jan 11. PMID: 18188703.

While biological metrics such as those described above can be computed after the fact by a user electronic device or workstation that is coupled to the wearable article, it is desirable for the electronics module to compute the metrics as the biosignals are being measured so as to provide real time feedback to the user. However, the small form factor of the electronics module places constraints on the processing that can be performed.

It is an object of the present disclosure, to provide memory efficient implementations of algorithms for determining biological metrics such as the heartrate and breathing rate that are suitable to be implemented on an electronics module for a wearable article in real-time.

SUMMARY

According to a first aspect of the disclosure, there is provided an electronics module for a wearable article. The electronics module comprises a front end arranged to receive biological signals from one or more sensors, and process the received signals to generate digital signal value. The electronics module comprises a controller coupled to the front end, wherein the controller is operable to receive digital signal values from the front end and store the digital signal values in a buffer.

The controller is operable to perform a signal processing operation comprising: (a) reading a number M of digital signal values from the buffer; (b) detecting one or more extrema in the M digital signal values; and (c) determining whether at least a number N of signal values have been read from the buffer during the signal processing operation, where N is greater than M. If fewer than N signal values have been read from the buffer, the controller is operable to repeat steps (a) -(c) until N signal values have been read from the buffer.

If at least N signal values have been read from the buffer, the controller is operable to use the extrema detected in the signal processing operation to determine a biological metric, and repeat the performance of the signal processing operation.

Advantageously, rather than wait for the N digital signal values to be read prior to performing the extrema detection, the controller of the present disclosure performs the extrema detection as new M digital signal values are output by the front end. This means that the N digital signal values do not need to be stored in a memory prior to performing the signal processing operation. Instead, only the detected extrema may be required to be stored prior to performing the signal processing operation.

As an example, in some applications it may be desirable to obtain at least 2048 signal values representing a four second window at a sampling rate of 512 Hz. This window may only comprise a small number of peaks (e.g. less than 10). Therefore, rather than having to store 2048 signal values in memory, the approach of the present disclosure can store far fewer values. Reducing memory consumption is particularly important in electronics modules for wearable articles as electronics modules are desired to have as small a form factor as possible so that they are comfortable to wear over extended time periods and do not affect the appearance of the wearable article. This small form factor reduces the available space for memory on the electronics module and also limits the overall battery capacity of the electronics module. The electronics module is therefore adapted to perform a memory efficient process for determining the biological metric in real-time.

The biological signals are signals indicative of the biological metric of the wearer of the wearable article.

Prior to detecting one or more extrema in the M digital signal values, the controller may be operable to filter the M digital signal values If at least N signal values have been read from the buffer, the controller may be operable to remove one or more anomalous extrema prior to determining the biological metric.

The controller being operable to remove one or more anomalous extrema may comprise the controller being operable to compare extrema to a threshold value and remove extrema having an amplitude less than the threshold value.

The threshold value may be determined according to the M digital signal values.

The threshold value may be determined according to the spectral power of the M digital signal values. The threshold value may be determined according to the extrema.

M may be between 1 and 50. M may be between 1 and 20.

N may be between 1000 and 10000. N may be between 4000 and 8000.

The biological signals may be signals indicative of the heartrate, the detected extrema may be peaks, and the biological metric may be the heartrate of the wearer. The signals indicative of the heartrate may be ECG signals.

The biological signals may be signals indicative of the breathing rate, the detected extrema may be local minima and local maxima, and the biological metric may be the breathing rate of the wearer.

The signals indicative of the breathing rate may be bioimpedance signals.

The bioimpedance signals may be impedance plethysmography signals.

The breathing rate may be determined by calculating the reciprocal of an average breathing rate duration determined from the extrema.

The electronics module may comprise a communicator arranged to transmit the biological metric to a remote device.

The electronics module may comprise an output unit arranged to output the biological metric. The buffer may be a first in, first out, FIFO, buffer.

The one or more sensors may be part of the electronics module or may be separate from the electronics module. The one or more sensors may be part of the wearable article.

The front end may be an analogue-to-digital front end that converts incoming analogue biological signals into digital signal values.

The electronics module may comprise a sensing interface that couples the signals to the front end.

According to a second aspect of the disclosure, there is provided a method performed by an electronics module for a wearable article.

The method comprises receiving, by a front end of the electronics module, biological signals from one or more sensors.

The method comprises processing, by the front end of the electronics module, the received signals to generate digital signal values.

The method comprises obtaining, by a controller of the electronics module, digital signal values from the front end.

The method comprises storing, by the controller, the digital signal values in a buffer.

The method comprises performing, by the controller, a signal processing operation comprising: (a) reading a number M of digital signal values from the buffer; (b) detecting one or more extrema in the M digital signal values; and (c) determining whether at least a number N of signal values have been read from the buffer during the signal processing operation, where N is greater than M. If fewer than N signal values have been read from the front end, the method comprises repeating steps (a) -(c) until N signal values have been read from the buffer, and If at least N signal values have been read from the buffer, the method further comprises using, by the controller, the extrema detected in the signal processing operation to determine a biological metric, and repeating the performance of the signal processing operation.

According to a third aspect of the disclosure, there is provided an electronics module for a wearable article, the electronics module comprising: a front end arranged to receive signals indicative of the heartrate of a wearer of the wearable article from one or more sensor and process the received signals to generate digital signal values; and a controller coupled to the front end, wherein the controller is operable to receive digital signal values from the front end and store the digital signal values in a buffer, wherein the controller is operable to perform a signal processing operation comprising: (a) reading a number M of digital signal values from the buffer; (b) detecting one or more peaks in the M digital signal values; and (c) determining whether at least a number N of signal values have been read from the buffer during the signal processing operation, where N is greater than M, wherein if fewer than N signal values have been read from the buffer, the controller is operable to repeat steps (a) -(c) until N signal values have been read from the buffer, and wherein if at least N signal values have been read from the buffer, the controller is operable to use the peaks detected in the signal processing operation to determine the heartrate of the wearer, and repeat the performance of the signal processing operation.

The electronics module may comprise any of the features of the electronics module of the first aspect of the disclosure.

Prior to detecting one or more extrema in the M digital signal values, the controller may be operable to detrend the M signal values.

The controller may be further operable to filter the detrended M signal values.

The controller may be operable to filter the detrended M signal values using a complex filter. The complex filter may be a complex Morlet wavelet filter.

The controller being operable to determine the heartrate of the wearer, may comprises the controller being operable to determine inter-beat interval, IBI, values from the detected peaks, calculate an average IBI value from the determined IBI values, and take the reciprocal of the average IBI value to obtain the heartrate.

According to a fourth aspect of the disclosure, there is provided a method performed by an electronics module for a wearable article. The method comprises: receiving, by a front end of the electronics module, signals indicative of the heartrate of a wearer of the wearable article from one or more sensors; processing, by the front end of the electronics module, the received signals to generate digital signal values; obtaining, by a controller of the electronics module, digital signal values from the front end; storing, by the controller, the digital signal values in a buffer; performing, by the controller, a signal processing operation comprising: (a) reading a number M of digital signal values from the buffer; (b) detecting one or more peaks in the M digital signal values; and (c) determining whether at least a number N of signal values have been read from the buffer during the signal processing operation, where N is greater than M, wherein if fewer than N signal values have been read from the front end, the method comprises repeat steps (a) -(c) until N signal values have been read from the buffer, and wherein if at least N signal values have been read from the buffer, the method comprises using, by the controller, the peaks detected in the signal processing operation to determine the heartrate of the wearer, and repeating the performance of the signal processing operation.

According to a fifth aspect of the disclosure, there is provided an electronics module for a wearable article, the electronics module comprising: a front end arranged to receive signals indicative of the breathing rate of the wearer of the wearable article from one or more sensors and process the received signals to generate digital signal values; and a controller coupled to the front end, wherein the controller is operable to receive digital signal values from the front end and store the digital signal values in a buffer, wherein the controller is operable to perform a signal processing operation comprising: (a) reading a number M of digital signal values from the buffer; (b) detecting one or more extrema in the M digital signal values; and (c) determining whether at least a number N of signal values have been read from the buffer during the signal processing operation, where N is greater than M, wherein if fewer than N signal values have been read from the buffer, the controller is operable to repeat steps (a) -(c) until N signal values have been read from the buffer, and wherein if at least N signal values have been read from the buffer, the controller is operable to use the extrema detected in the signal processing operation to determine the breathing rate of the wearer, and repeat the performance of the signal processing operation.

Prior to detecting one or more extrema in the M digital signal values, the controller may be operable to filter the M digital signal values.

The controller being operable to use the extrema to determine the breathing rate may comprise the controller being operable to: calculate the vertical differences between the extrema; use the vertical differences to set a threshold value; remove pairs of extrema that are separated by a distance which is less than the threshold value; and calculate the breathing rate from the remaining extrema.

The controller being operable to use the extrema to determine the breathing rate may comprise the controller being operable to: calculate the duration of breathing cycles from the extrema; average the duration of the breathing cycles to obtain an average breathing cycle duration; and calculate the reciprocal of the average breathing cycle duration to obtain the breathing rate.

According to a sixth aspect of the disclosure, there is provided a method performed by an electronics module for a wearable article, the method comprising.

receiving, by a front end of the electronics module, signals indicative of the breathing rate of the wearer of the wearable article from one or more sensors; processing, by the front end of the electronics module, the received signals to generate digital signal values; obtaining, by a controller of the electronics module, digital signal values from the front end; storing, by the controller, the digital signal values in a buffer; performing, by the controller, a signal processing operation comprising: (a) reading a number M of digital signal values from the buffer; (b) detecting one or more extrema in the M digital signal values; and (c) determining whether at least a number N of signal values have been read from the buffer during the signal processing operation, where N is greater than M, wherein if fewer than N signal values have been read from the front end, the controller is operable to repeat steps (a) -(c) until N signal values have been read from the buffer, and wherein if at least N signal values have been read from the buffer, the controller is operable to use the extrema detected in the signal processing operation to determine the breathing rate of the wearer, and repeat the performance of the signal processing operation.

According to a seventh aspect of the disclosure, there is provided an electronics module for a wearable article, the electronics module comprising: a front end arranged to receive signals indicative of the heart rate of a wearer of the wearable article and signals indicative of the breathing rate of the wearer from one or more sensors, and process the received signals to generate digital signal values; and a controller coupled to the front end, wherein the controller is operable to receive digital signal values from the front end and store the digital signal values obtained from the signals indicative of the heartrate of the wearer in a first buffer and store the digital signal values obtained from the signals indicative of the breathing rate of the wearer in a second buffer, wherein the controller is operable to perform a heartrate determination signal processing operation comprising: (a) reading a number M of digital signal values from the first buffer; (b) detecting one or more extrema in the M digital signal values; and (c) determining whether at least a number N of signal values have been read from the first buffer during the signal processing operation, where N is greater than M, wherein if fewer than N signal values have been read from the first buffer, the controller is operable to repeat steps (a) -(c) until N signal values have been read from the first buffer, and wherein if at least N signal values have been read from the first buffer, the controller is operable to use the extrema detected in the signal processing operation to determine the heartrate for the wearer and repeat the performance of the signal processing operation, and wherein the controller is operable to perform a breathing rate determination signal processing operation comprising: (a) reading a number P of digital signal values from the second buffer; (b) detecting one or more extrema in the P digital signal values; and (c) determining whether at least a number Q of signal values have been read from the second buffer during the signal processing operation, where 0 is greater than P, wherein if fewer than 0 signal values have been read from the second buffer, the controller is operable to repeat steps (a) -(c) until 0 signal values have been read from the second buffer, and wherein if at least Q signal values have been read from the second buffer, the controller is operable to use the extrema detected in the signal processing operation to determine the breathing rate for the wearer and repeat the performance of the signal processing operation.

According to an eighth aspect of the disclosure, there is provided a method performed by an electronics module for a wearable article, the method comprising: receiving, by a front end of the electronics module, signals indicative of the heart rate of a wearer of the wearable article and signals indicative of the breathing rate of the wearer from one or more sensors; processing, by the front end of the electronics module, the received signals to generate digital signal values; obtaining, by a controller of the electronics module, digital signal values from the front end; storing, by the controller, the digital signal values obtained from the signals indicative of the heartrate of the wearer in a first buffer and the digital signal values obtained from the signals indicative of the breathing rate of the wearer in a second buffer; performing, by the controller, a heartrate determination signal processing operation comprising: (a) reading a number M of digital signal values from the first buffer; (b) detecting one or more extrema in the M digital signal values; and (c) determining whether at least a number N of signal values have been obtained from the first buffer during the signal processing operation, where N is greater than M, wherein if fewer than N signal values have been read from the first buffer, the method comprises repeating steps (a) -(c) until N signal values have been read from the first buffer, and wherein if at least N signal values have been read from the first buffer, the method further comprises using, by the controller, the extrema detected in the signal processing operation to determine the heartrate for the wearer and repeating the performance of the signal processing operation, and performing, by the controller, a breathing rate determination signal processing operation comprising: (a) reading a number P of digital signal values from the second buffer; (b) detecting one or more extrema in the P digital signal values; and (c) determining whether at least a number Q of signal values have been obtained from the second buffer during the signal processing operation, where N is greater than M, wherein if fewer than Q signal values have been read from the second buffer, the method comprises repeating steps (a) -(c) until Q signal values have been read from the second buffer, and wherein if at least CI signal values have been read from the second buffer, the method further comprises using, by the controller, the extrema detected in the signal processing operation to determine the breathing rate for the wearer and repeating the performance of the signal processing operation.

BRIEF DESCRIPTION OF THE DRAWINGS

To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.

FIG. 1 illustrates an example system in accordance with aspects of the present disclosure.

FIG. 2 illustrates a schematic for an example electronics module in accordance with aspects of the present disclosure.

FIG. 3 illustrates an example wearable article in accordance with aspects of the present disclosure.

FIG. 4 illustrates an example wearable assembly comprising an electronics module and wearable article in accordance with aspects of the present disclosure.

FIG. 5A illustrates an external view of an example electronics module in accordance with aspects of the present disclosure FIG. 5B illustrates an external view of an example electronics module in accordance with aspects of the present disclosure.

FIG. 6 illustrates a schematic for an example electronics module in accordance with aspects of the present disclosure.

FIG. 7 illustrates a more detailed schematic for an example electronics module in accordance with aspects of the present disclosure FIG. 8 illustrates an example analogue-to-digital front end of an electronics module according to aspects of the present disclosure.

FIG. 9 illustrates an example method according to aspects of the present disclosure.

FIG. 10 illustrates an example method according to aspects of the present disclosure.

FIG. 11 illustrates an example method according to aspects of the present disclosure.

FIG. 12 illustrates an example method according to aspects of the present disclosure.

FIG. 13 illustrates an example user electronic device 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" refers to any form of article 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, armband, chestband, waistband, stocking, sock, or shoe, athletic clothing, personal protective equipment, including hard hats, swimwear, wetsuit or dry suit.

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 (e.g., a garment) may be constructed from a woven or a non-woven material. The wearable article 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. Polyester, polycotton, nylon and viscose are synthetic fibres that may be used in the wearable article.

The garment may be a tight-fitting garment or a loose-fitting (e.g. freeform garment). 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 tight-fitting garment may be a compression garment. The tight-fitting garment may be an athletic garment such as an elastomeric athletic garment. A loose-fitting garment is generally more comfortable to wear over extending time periods and during sleep.

The garment has sensing units provided on an inside surface which are typically 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.

"Wearer" refers to the person or other form of animal who is wearing, or otherwise holding, the wearable article and/or electronics module. The wearer may also be referred to as a user. Although the user and wearer may be different entities in certain situations "Biosignal", "biological signal" refers to signals from living beings that can be continually measured or monitored. Biosignals may be electrical or non-electrical signals. Signal variations can be time variant or spatially variant.

"Sensing units" refers to one or more elements more measuring signals from a wearer of the wearable article. A sensing unit may comprise the combination of a sensor, such as an electrode, a connection region, and a communication pathway coupling the electrode to the connection region. An electronics module communicatively coupled to the connection region is able to obtain measurement signals from the sensor via the communication pathway and connection region. The sensing units may be made of a (electrically) conductive material such as a conductive yarn, conductive ink, conductive transfer, or conductive paste. When formed form conductive yarn, the sensing units may be knitted, woven,

II

embroidered, stitched or otherwise incorporated into the wearable article. The sensing units may be integrally formed with the wearable article such as by being integrally knitted with the wearable article.

The sensing units may be arranged to measure one or more biosignals of a wearer wearing the wearable article.

Sensing units may be used for measuring one or a combination of bioelectrical, bioimpedance, biochemical, biomechanical, bioacoustics, biooptical or biothermal signals of the wearer. The sensing units may be incorporated into the wearable article, an electronics module coupled to or forming part of the wearable article, or may be shared between the electronics module and the wearable article. For example, the wearable article may comprise sensors (e.g. sensing electrodes) while the electronics module may comprise the processing logic for the sensing electrodes. The processing logic will review the signals from the sensors and perform operations such as filtering and analogue-to-digital conversion on the signals. 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 magnetoneurograms (MNG), magnetoencephalography (MEG), magnetogastrogram (MGG), magnetocardiogram (MCG). The biochemical measurements include glucose/lactose measurements which may be performed using chemical analysis of the wearer's sweat. The biomechanical measurements include blood pressure. The bioacoustics measurements include phonocardiograms (PCG). The biooptical measurements include photoplethysmography (PPG) and orthopantomograms (OPG). The biothermal measurements include skin temperature and core body temperature measurements.

"Electronics module" may refer to an electronic device that is able to communicatively couple with sensing units in a wearable article so as to obtain measurement signals from the sensing units and/or apply signals to the sensing units. The electronics module may also be a stand-alone component that performs measurements using internal sensors without communicatively coupling to a wearable article.

Electronics modules typically comprise a sensing interface for communicatively coupling with the wearable article, a controller, and a wireless communicator for communicating with an external device such as a user electronic device over a wireless communication protocol.

The electronics module is typically removably coupled to the wearable article such that it is retained by the wearable article when worn. The electronics module can be removed from the wearable article so that the wearable article can be washed without damaging the internal electronics of the electronics module. The electronics module can also be removed from the wearable article for charging. In other examples, the electronics module is integrally formed with the wearable article such as when the wearable article/electronics module form a smartwatch.

Generally, the electronics module comprises all of the components required for data transmission and processing such that the wearable article only comprises the sensing units. In this way, the manufacture of the wearable article may be simplified. In addition, it may be easier to clean a wearable article which has fewer electronic components attached thereto or incorporated therein. Furthermore, the removable electronics module may be easier to maintain or troubleshoot than embedded electronics. The electronics module may comprise flexible electronics such as a flexible printed circuit (FPC).

FIG. 1 shows a system according to aspects of the present disclosure. The system comprises a wearable assembly 102 and a user electronic device 104. The wearable assembly 102 is worn by a user who in this embodiment is the wearer 106 of the wearable assembly 102.

The wearable assembly 102 comprises a wearable article 108 which, in this is example, is in the form of a garment.

The wearable assembly 102 comprises an electronics module 110. The electronics module 110 is releasably coupled to the wearable article 108. The wearable article 108 comprises an electronics module holder (not shown) arranged to removably retain the electronics module 110. The electronics module holder enables the electronics module to be attached and removed from the wearable article 108.

In some examples, the electronics module holder comprises a pocket such as a garment pocket. The pocket has an opening through which the electronics module 110 may be inserted and removed from the pocket. The pocket may be formed from fabric layers of the wearable article 108.

The present disclosure is not limited to electronics module holders in the form pockets.

The electronics module 110 may be configured to be releasably mechanically coupled to the wearable article 108. The mechanical coupling of the electronics module 110 to the wearable article 108 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 electronics module 110 in a particular orientation with respect to the wearable article 108 when the electronics module 110 is coupled to the wearable article 108. This may be beneficial in ensuring that the electronics module 110 is securely held in place with respect to the wearable article 108 and/or that any electronic coupling of the electronics module 110 and the wearable article 108 can be optimized. The mechanical coupling may be maintained using friction or using a positively engaging mechanism, for example.

The electronics module 110 is arranged to wirelessly communicate data to the user electronic device 104. Various protocols enable communication between the electronics module 110 and the user electronic device 104. Example communication protocols include Bluetooth 0, Bluetooth ® Low Energy, and near-field communication (NFC).

The system also comprises a remote server 112 which may be in communication with the user electronic device 104 and/or the electronics module 110.

FIG. 2 shows a simplified diagram of an example electronics module 110 according to aspects of the present disclosure. The electronics module 110 comprises a controller 202 and a sensing interface 204 communicatively coupled to the controller 202.

The sensing interface 204 in this example comprises a first electrical contact 206 and a second electrical contact 208. The sensing interface 204 receives measurement signals from the electrical contacts 206, 208. The measurement signals, or a processed version thereof, are provided to the controller 202. The measurement signals may be any form of biosignal as described above. The sensing interface 204 is therefore able to receive physiological signals from a wearer of the electronics module 110. 13'

The controller 202 is able to process the signals received from the sensing interface. The controller 202 may control a wireless communicator (not shown) of the electronics module 110 to transmit data to an external device such as user electronic device 104 of FIG. 1.

FIG. 3 shows a simplified diagram of an example wearable article 108. The wearable article 108 comprises a fabric layer 302.

A first communication interface 304 is provided on the fabric layer 302. The first communication interface 304 is accessible from the electronics module holder of the wearable article 108.

The first communication interface 304 is communicatively coupled to a first sensor 306 via a first communication pathway 308. The first communication interface 304, first sensor 306 and first communication pathway 308 form a first sensing unit of the wearable article 108. The first sensor 306 is in the form of an electrode. The first sensor 306 may be arranged to be provided on the wearable article 108 such that it faces the skin surface of the wearer when the wearable article 108 is worn. This enables the first sensor 306 to contact the skin surface and measure biosignals from the skin surface and/or apply signals to the skin surface. Signals may be applied to the skin surface in therapeutic applications for example.

A second communication interface 310 is provided on the fabric layer 302. The second communication interface 310 is accessible from the electronics module holder of the wearable article 108.

The second communication interface 310 is communicatively coupled to a second sensor 312 via a second communication pathway 314. The second communication interface 310, second sensor 312, and second communication pathway 314 form a second sensing unit of the wearable article 108. The second sensor 312 is in the form of an electrode. The second sensor 312 may be arranged to be provided on the wearable article 108 such that it faces the skin surface of the wearer when the wearable article 108 is worn. This enables the second sensor 312 to contact the skin surface and measure biosignals from the skin surface and/or apply signals to the skin surface. Signals may be applied to the skin surface in therapeutic applications for example.

In this example, the first sensor 306 and second sensor 312 are electrodes. This is not required in all examples. Other forms of sensors such as temperature sensors, optical sensors, chemical sensors, and moisture sensors may be included. The wearable article 108 may include any combination of different types of sensors.

FIG. 4 shows a simplified diagram of an electronics module 110 coupled to a wearable article 108 to form an example wearable assembly 102. The electronics module 110 is positioned inside an electronics module holder 402 of the wearable article 108 which in this example is in the form of a pocket.

The first communication interface 304 and the second communication interface 310 are provided on a first surface of fabric layer 404 such that they are located within the pocket space. The first sensor 306 and the second sensor 312 are provided on a second surface of fabric layer 406 that opposes the first surface of fabric layer 404. The first sensor 306 and second sensor 312 are arranged such that they face towards the skin surface of the wearer of the wearable article 108. The first and second communication pathways are not shown in FIG. 4 but, as discussed above in relation to FIG. 3, couple the sensors to their respective communication interfaces 304, 310.

The electronics module 110 is positioned within the pocket space. The first electrical contact 206 of the electronics module 110 contacts and is electrically coupled to the first communication interface 304. The second electrical contact 208 of the electronics module 110 contacts and is electrically coupled to the second communication interface 310. The electronics module 110 is therefore coupled to the first sensor 306 and the second sensor 312 via the communication pathways, communication interfaces 304, 310, and electrical contacts 206, 208.

FIG. 5A and FIG. 5B show external views of an electronics module 110 according to aspects of the present disclosure. The electronics module 110 has a housing 502. Components of the electronics module 110 such as the controller 202 are disposed within the housing 502. The first electrical contact 206 and the second electrical contact 208 are located on an external surface of the housing 502.

The electronics module 110 may have a length of between 20 mm and 60 mm, a width of between 15 mm and 35 mm, and a depth of between 5 mm and 15 mm. In some examples, the electronics module 110 has a length of between 30 mm and 40 mm or between 35 mm and 38 mm. In some examples, the electronics module 110 has a width of between 20 mm and 30 mm or between 24 and 26 mm. In preferred examples, the electronics module 110 has a width of 25 mm. In some examples, the electronics module 110 has a depth of between 8 mm and 12 mm or between 9 mm and 11 mm. In preferred examples, the electronics module 110 has a depth of between 9.7 mm and 10 mm. In one particular example, the electronics module 110 has a length of 38 mm, a width of 25 mm and a depth of 9.6 mm.

FIG. 6 shows a simplified schematic diagram for an example electronics module 110 as shown in FIG. 4. It will be appreciated that not all of the components shown in FIG. 6 are required and additional components may also be provided.

The electronics module 110 comprises a controller 202 and a sensing interface 204 as described in FIG. 4. The sensing interface 204 comprises a first electrical contact 206 and a second electrical contact 208. The controller 202 is communicatively coupled to the sensing interface 204 and is operable to receive signals from the sensing interface 204 for further processing.

The sensing interface 204 comprises electrical contacts 206, 208 in this example. This means that the communicative coupling in this example is a conductive coupling formed by direct contact between the electrical contacts 206, 208 and the connection regions of the wearable article, but this is not required in all examples. The communicative coupling may be a wireless (e.g., inductive) coupling.

The electronics module 110 further comprises a power source 602 and a power receiving interface 604.

The power source 602 may comprise one or a plurality of power sources. The power source 602 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 602 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 of the wearable article. The kinetic event could include walking, running, exercising or respiration of the wearer. 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. The energy harvesting device may be a thermoelectric energy harvesting device. The power source may be a super capacitor, or an energy cell.

The power receiving interface 604 is operable to receive power from an external power store for charging the power source. The power receiving interface 604 may be a wired or wireless interface. A wireless interface may comprise one or more wireless power receiving coils for receiving power from the external power store. In some examples, one or both of the first and second electrical contacts 206, 208 may also function as the power receiving interface 604 to enable power to be received from the external power store.

The power receiving interface 604 may also be coupled to the controller 202 to enable direct communication between the controller 202 and an external device if required.

The electronics module 110 further comprises a wireless communicator 606. The wireless communicator 606 may utilise any communication protocol such as used for communication over: a wireless wide area network (AN), a wireless metro area network (WMAN), a wireless local area network (WLAN), a wireless personal area network (1NPAN), 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-IoT, fifth generation (5G), sixth generation (6G), and/or any other present or future developed cellular wireless network.

The electronics module 110 further comprises a sensor 608. The sensor 608 may comprise one or a combination of an optical sensor, temperature sensor, motion sensor, magnet sensor, and location sensor. Other sensors may also be included in the electronics module 110.

FIG. 7 shows a more detailed schematic diagram for the example electronics module 110 shown in FIG. 4 and FIG. 6.

The electronics module 110 comprises a controller 202, sensing interface 204, first electrical contact 206, second electrical contact 208, sensor 608, power source 602, and power receiving interface 604 as described above.

The controller 202 comprises an internal memory 702. The controller 202 is also communicatively connected to an external memory 704 which in this example is a NAND Flash memory. The external memory 704 is used to for the storage of data when no wireless connection is available between the electronics module 110 and an external device such as a user electronic device (e.g., user electronic device 104 of FIG. 1). The external memory 704 may have a storage capacity of at least 1GB and preferably at least 2 GB. The electronics module 110 also includes additional peripheral devices that are used to perform specific functions as will be described in further detail herein.

The power source 602 in this example is a lithium ion battery. The battery is rechargeable and charged via power receiving interface 604. The power receiving interface 604 is arranged to receive wireless power inductively. Of course, the present disclosure is not limited to recharging via inductive charging and instead other forms of charging such as a wired connection or far field wireless charging are within the scope of the present disclosure. Additional battery management functionality is provided in terms of a charge controller 706, battery monitor 708 and regulator 710. These components may be provided through use of a dedicated power management integrated circuit (PMIC).

The controller 202 is communicatively connected to a battery monitor 708 so that that the controller 202 may obtain information about the state of charge of the battery.

The electronics module 110 comprises a first wireless communicator 712 and a second wireless communicator 714.

The first wireless communicator 712 is arranged to communicatively couple with an external device over a first wireless communication protocol. The first wireless communication protocol may be a Bluetooth protocol, Bluetooth 0 5 or a Bluetooth 0 Low Energy protocol but is not limited to any particular communication protocol. In the present embodiment, the first wireless communicator 712 is integrated into controller 202. The first wireless communicator 712 enables communication between the external device and the controller 202 for configuration and set up of the controller 202 and the peripheral devices as may be required. Configuration of the controller 202 and peripheral devices utilises the Bluetooth 0 protocol in this example.

Other wireless communication protocols can also be used, such as used for communication over: a wireless wide area network (WAN), a wireless metro area network (WMAN), a wireless local area network (WLAN), a wireless personal area network (VVPAN), Bluetooth 0 Low Energy, Bluetooth 0 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-IoT, fifth generation (5G), sixth generation (6G), and/or any other present or future developed cellular wireless network.

The second wireless communicator 714 is arranged to communicatively couple with an external device using a second communication protocol. The external device is powered to induce a magnetic field in an antenna of the second wireless communicator 714. When the external device is placed in the magnetic field of the antenna of the second wireless communicator 714, the external device induces current in the second wireless communicator 714. This induced current is used to retrieve the information from a memory and transmit the same back to the external device. The controller 202 is arranged to energize the second wireless communicator 714 to transmit information.

In an example operation, the external device is a user electronic device (e.g., user electronic device 104 of FIG. 1). The user electronic device is brought into proximity with the electronics module 110. In response to this, the electronics module 110 is configured to energize the second wireless communicator 714 to transmit information to the user electronic device over the second wireless communication protocol. Beneficially, this means that the act of the user electronic device approaching the electronics module 110 energizes the second wireless communicator 714 to transmit the information to the user electronic device.

The information may comprise a unique identifier for the electronics module 110. The unique identifier for the electronics module 110 may be an address for the electronics module 110 such as a MAC address or Bluetooth 0 address.

The information may comprise authentication information used to facilitate the pairing between the electronics modules 110 and the user electronic device over the first wireless communication protocol. This means that the transmitted information is used as part of an out of band (00B) pairing process.

The information may comprise application information which may be used by the user electronic device to start an application on the user electronic device or configure an application running on the user electronic device. The application may be started on the user electronic device automatically (e.g., without user input). Alternatively, the application information may cause the user electronic device to prompt the user 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 110 can transmit any of the above example information either alone or in combination. The electronics module 110 may transmit different types of information depending on the current operational state of the electronics module 110 and based on information it receives from other devices such as the user electronic device.

The electronics module 110 has sensors 608 including a motion sensor 716, a temperature sensor 718, a magnetic field sensor 720, and a location sensor 722. It will be appreciated that not all of these sensors 608 are required in all examples and additional sensors, such as optical sensors, chemical sensors, humidity sensors, and pressure sensors may also be provided.

The location sensor 722 may be a GNSS (Global Navigation Satellite System) device which is arranged to provide location and position data for applications as required. In particular, the location sensor 722 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 Global Positioning System (GPS), BeiDou Navigation Satellite System (BDS) and the Galileo system devices.

The motion sensor 716 in this example is in the form of an inertial measurement unit (IMU) 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.

The IMU can therefore be used to detect can detect orientation and gestures with event-detection interrupts enabling motion tracking and contextual awareness. It has recognition of free-fall events, tap and double-tap sensing, activity or inactivity, stationary/motion detection, and wakeup events in addition to 6D orientation. A single tap, for example, can be used enable toggling through various modes or waking the electronics module 110 from a low power mode.

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 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 211 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 "ifthen-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 a known machine learning tool such as Waikato Environment for Knowledge Analysis software (Weka) developed by the University of Waikato or using MATLABO or Python TM.

The electronics module 110 further comprises a light source 724, such as a light emitting diode, for conveying status information about the electronics module 110 and/or the wearer of the electronics module 110. More generally, any form of output unit may be provided in addition to or instead of the light source 724. The output unit may comprise one or a combination of an audio output unit, a visual output unit (e.g., light source 724 or a display) and a haptic feedback unit.

The electronics module 110 also comprises conventional electronics components which are not shown in FIG. 7 including a power-on-reset generator, a development connector, a real time clock and a FROG header.

The electronics module 110 in this example comprises first wireless communicator 712 and second wireless communicator 714 but this is not required in all examples. More generally, the electronics module 110 may have one or a plurality of wireless communicators to enable the electronics module 110 to communicate wirelessly over an external device such as a user electronic device or a remote server.

The electronics module 110 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 110 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 11D. That is, the UICC may be an electronic/embedded UICC (eUICC). A eUICC 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 110 may comprise a secure element that represents an embedded Universal Integrated Circuit Card (eUICC).

The sensing interface comprises an analogue-to-digital front end 726 that couples signals received from the electrical contacts 206, 208 to the controller 206 and optionally an electrostatic discharge (ESD) protection circuit. The analogue-to-digital front end is shown in detail in FIG. 8.

FIG. 8 is a schematic illustration of the component circuitry for the analogue-to-digital front end 726 shown in FIG. 7.

In the example described herein, the analogue-to-digital front end 726 is an integrated circuit (IC) chip which converts the raw analogue biosignal received via the sensing interface into a digital signal for further processing by the controller (e.g., controller 202 of FIG. 7). ADC IC chips are known, and any suitable one can be utilised to provide this functionality. ADC IC chips for ECG and bioimpedance applications include, for example, the MAX30001 chip produced by Maxim Integrated Products Inc. The analogue-to-digital front end 726 includes an input 802 and an output 804.

Raw biosignals from the sensing interface (e.g., sensing interface 204 of FIG. 7) are input to the analogueto-digital front end 726, where received signals are processed in an ECG channel 806 and a bioimpedance (BIOZ) channel 808 and subject to appropriate filtering through high pass and low pass filters for 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 coupled to the sensing interface.

The output digital signals may be decimated to reduce the sampling rate prior to being passed to a serial programmable interface 810 of the analogue-to-digital front end 726. Signals are output to the controller via the serial programmable interface 810. The digital signal values are a time series of scalar values. This time series is provided as a data stream, meaning that values are provided as they are recorded.

The digital signal values output to the controller 202 are stored in a FIFO data buffer. The controller 202 performs operations to generate biological metrics from the digital signal values. The operations are performed in real-time while the ADC front end 726 are outputting new digital signals to the controller 202.

Referring to FIG. 9, there is shown an example method performed by an electronics module 110 according to aspects of the present disclosure.

The electronics module 110 comprises the sensing interface 204, analogue-to-digital front end 726, and controller 202 as described above. The electronics module 110 may additionally comprise any of the other components described above.

Step 902 comprises the sensing interface 204 receiving biological signals from one or more sensors. The sensors may be part of the electronics modules or the wearable article. The received biological signals are analogue signals and may be, for example, analogue voltage or impedance signals.

The analogue-to-digital front end 726 is coupled to the sensing interface 204 and receives the analogue signals from the sensing interface 204. In step 904, the analogue-to-digital front end 726 processes the received signals to generate digital signals values. The analogue-to-digital front end 726 outputs the digital signal values to the controller 202.

In step 906, the controller 202 obtains the digital signal values from the analogue-to-digital front end 726. The controller 202 is receiving the digital signal values in real or near real-time.

In step 908, the controller 202 stores the digital signal values in a buffer. The buffer is a first-in first-out (FIFO) buffer in this example but this is not required. The buffer may be any other form of buffer.

It will be appreciated that steps 902 -908 may be repeatedly performed as the sensors are measuring biosignals. The digital signal values may be output by the analogue-to-digital front end 726 according to a sample rate. The data stored in the FIFO buffer is repeatedly refreshed as digital signal values are received from the analogue-to-digital front end 726.

As the controller 202 is receiving digital signal values from the analogue-to-digital front end 726, the controller starts a signal processing operation in step 910. The signal processing operation involves processing the digital signal values to determine a biological metric as explained in greater detail below. The signal processing operation is repeatedly performed as digital signal values are being received from the analogue-to-digital front end 726. The signal processing operation is performed in real-time as signals are being received from the sensors.

FIG. 10 shows an example of the signal processing operation 910 of FIG. 9.

Step 1002 comprises reading a number M of digital signal values from the buffer. The chunk of M digital signal values are read into a memory of the controller 202.

M may be any number as selected as appropriate by a skilled person. In some examples, M is between 1 and 100. In some examples, M is between 1 and 50. In some examples, M is between 1 and 30. In some examples, M is between 1 and 25. In some examples, M is between 1 and 20. In some examples, M is between 2 and 100. In some examples, M is between 2 and 50. In some examples, M is between 2 and 30. In some examples, M is between 2 and 25. In some examples, M is between 2 and 20. In some examples, M is between 4 and 20. In some examples, M is between 4 and 100. In some examples, M is between 4 and 50. In some examples, M is between 4 and 30. In some examples, M is between 4 and 25. In some examples, M is between 4 and 20. M may be determined to be equal to the number of signal values output by the analogue-to-digital front end 726 by processing cycle.

Step 1004 comprises detecting one or more extrema in the M digital signal values. The one or more extrema may be local extrema. The one or more extrema may be local maxima (peaks), local minima (troughs), or both local maxima and local minima. The type of extrema to detect will depend on the biological signal and the biological metric. For example, for ECG signals used in determining the heartrate it may only be required to detect local maxima (peaks). Meanwhile, for bioimpedance signals used in determining the breathing rate it may be desired to detect both local maxima and local minima.

Generally, the extrema are detected by analysing the gradient or slope between two consecutive samples. For three consecutive samples, A, B, and C, a change from a positive gradient between A & B to a negative gradient between B & C indicates that B is a peak. Similarly, a negative gradient between A & B and a positive gradient between B & C indicates that B is a trough, Step 1006 comprises adding the detected extrema to an array of extrema values. The detected extrema are therefore saved for further analysis.

Step 1008 comprises determining whether at least a number N of signal values have been obtained from the FIFO buffer during the signal processing operation. N is greater than M. The number N may be selected as appropriate by the skilled person by considering the length of the time window over which they wish to compute the biological metric and the sampling rate of the analogue-todigital front end 726.

For example, for heartrate data it may be desirable to obtain at least 4 seconds of data to ensure that at least two R-peaks are present in the digital signal values (assuming a minimum heart rate of 30 BPM for a healthy individual). The number N would therefore be desired to be greater than or equal to 4 x the sampling rate. For example, if the sampling rate is 512 HZ, the number N is set to 2048.

For example, for breathing rate data is may be desirable to obtain at least 120 seconds of data to ensure that sufficient extrema values are present in the digital signal values to compensate for temporary fluctuations in breathing rate. The number N would therefore be desired to be greater than or equal to 120 x the sampling rate. For example, if the sampling rate is 64 Hz, the number N is set to 7680.

N may be between 4 and 360 times the sampling rate. N may be between 4 and 240 times the sampling rate. N may be between 4 and 120 times the sampling rate.

In some examples, N is between 1000 and 10000. In some examples, N is between 2000 and 10000. In some examples, N is between 3000 and 10000. In some examples, N is between 4000 and 10000. In some examples, N is between 1000 and 9000. In some examples, N is between 1000 and 8000. In some examples, N is between 4000 and 8000.

If fewer than N signal values have been obtained from the analogue-to-digital front end 726, the controller 202 repeats steps 1002-1006 until N signal values have been read from the FIFO buffer. This means that the controller 202 reads new chunks of digital signal values from the buffer, detects extrema in the digital signal values and adds the detected extrema to the array of extrema values until N signal values representing a predetermined time duration are read.

If at least N signal values have been read from the FIFO buffer, the controller 202 uses the extrema to determine a biological metric. The controller 202 then repeats the performance of the signal processing operation.

Advantageously, the process outlined in FIG. 10 is optimised for performance in real-time as digital signal values are being determined by the analogue-to-digital front end 726 and output to the controller 202. Extrema are detected in the signal values as they are being obtained from the analogue-to-digital front end 726 and then the determination of the biological metric is only performed when N of the signal values have been read from the FIFO buffer. This approach is memory efficient as it does not require that the N signal values are stored in memory prior to the performance of step 1010. Instead, only a smaller number of values representative of the extrema are required to be stored. Being memory efficient is particularly important for wearable electronics modules as their small size limits the available space for memory units and battery capacity.

The present disclosure is not limited to any particular biological signal and biological metric.

In some examples, the biological signal is a signal indicative of the heartrate such as an ECG signal. The biological metric may be the heartrate of the subject. The heartrate is determined by detecting peaks, such as R-peaks, in the ECG signal, and determining inter-beat interval (IBI) values from the detected peaks.

The IBI values represent the time difference between successive (R-)peaks. An average of the IBI values is then typically taken. IBI values are typically in milliseconds. The heartrate is therefore obtained by dividing 60000 by the average IBI value in milliseconds.

In some examples, the biological signal is a signal indicative of the breathing rate such as a bioimpedance signal. The biological metric may be the breathing rate of the subject. The breathing rate is determined from the detected extrema by determining the duration of the breathing cycles contained within the N digital signal values. One or more averages of the breathing cycle durations is taken. The breathing cycle duration is typically in seconds. The breathing rate is obtained by dividing 60 by the average breathing cycle duration.

FIG. 11 shows a more detailed example of the signal processing operation 910 of FIG. 9. The signal processing operation is used to calculate the hear-trate for the wearer. The digital signal values are ECG signal values received in real-time via the analogue-to-digital front end 726 of the electronics module 110. The ECG signal values are received by the controller 202 from the analogue-to-digital front end 726 and stored in the FIFO buffer.

The controller repeatedly performs the signal processing operation as outlined above in relation to Fig, 9.

In step 1102, the controller reads a number M of signal values from the FIFO buffer. Each of the signal values is a value that represents the amplitude of the ECG signal at a particular time point. In this example M is 16 such that 16 ECG signal values are read from the FIFO buffer.

In step 1104, the controller detrends the signal values so as to remove baseline wander and/or other low frequency components. In an example operation, the controller 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 FIFO 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 over time. 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 1106, 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. IIR 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 1108 the controller detects peaks in the filtered signal values. At this stage, the controller 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. Other peak detections will be known by the skilled person. The detected peaks are added to an array of peaks.

In step 1110, the controller determines whether at least N signal values have been read from the FIFO buffer. Here, N is a number that is greater than M. N 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 array of peaks. For example, N may be selected such that signal values representative of at least 4 seconds of data have been obtained to ensure that there are at least 2 characteristic (or true) peaks in array. The number N will depend on the sampling rate of the signal values provided to the controller. 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 signal values have been obtained then the method returns to step 1102 so that additional samples are gathered, filtered, and added to the filtered signal buffer. Steps 1102 to 1108 are repeated until the N signal values are obtained.

If N or more signal values have been obtained, the method proceeds to step 1112.

In step 1112, the controller removes anomalous detected peaks. In some examples, this means that the controller 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. This is to account for different users having different peak amplitudes. The adjustable threshold value may also be modified based on user parameters or other information.

In step 1114, the heartrate is calculated from the remaining peaks. R-R intervals by determining the time duration between successive ones of the remaining R-peaks. Only one R-R interval may be determined if only one R-peak remains after step 1112. 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. An average of the R-R intervals is taken and the reciprocal of the average R-R interval gives the heartrate.

One or more additional steps may be performed prior to step 1114 such as to check the remaining peaks after step 1112 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 Blomed Eng. 2003 Mar;50(3):334-43. Another example process for detecting spurious peaks is disclosed in the inventor's pending UK Patent Application No. GB2018354.7 filed on 23 November 2020, the disclosures of which are hereby incorporated by reference.

Once the heartrate is determined, the controller enters a new signal processing operation such that the process outlined in FIG. 11 is repeated.

Advantageously, the process outlined in FIG. 11 is optimised for performance in real-time as ECG signal values are being determined by the analogue-to-digital front end 726 and output to the controller 202. Peaks are detected in the signal values as they are being obtained from the analogue-to-digital front end 726 and then the correction of the detected peaks (step 1112) and the calculation of R-R intervals (step 1114) are only performed once a sufficient number, N, of signal values have been read from the FIFO buffer. This approach is memory efficient as it does not require that the N signal values are stored in memory prior to the performance of steps 1112 and 1114. Instead, only a smaller number of values representative of the peaks are required to be stored. Being memory efficient is particularly important for wearable electronics modules as their small size limits the available space for memory units and battery capacity.

FIG. 12 shows an aspect of a method for calculating a breathing rate from bioimpedance values received in real-time via the analogue-to-digital front end 726 of the electronics module 110. The bioimpedance signal values are received by the controller 202 from the analogue-to-digital front end 726 and stored in the FIFO buffer.

The controller repeatedly performs a signal processing operation.

In step 1202 the controller reads a number M of signal values from the FIFO buffer. Each of the signal values is a value that represents the amplitude of the impedance signal at a particular time point.

In step 1204, the controller filters the M signal values. Any kind of filter may be used as desired by the skilled person. In preferred examples, the filter is a bandpass filter with a passband of 0.1 Hz to 0.5 Hz or 0.05 Hz to 2 Hz.

In step 1206, the controller detects extrema in the M filtered signal values and adds the filtered signal values to an array. The extrema are local maxima and minima in the M filtered signal values.

In step 1208, the controller determines whether at least N signal values have been read from the FIFO buffer. Here, N is a number that is greater than M. N may be selected by the skilled person as desired to ensure that there are likely to be a certain desired number of extrema within the array such that an accurate breathing rate can be determined. For example, N may be selected such that signal values representative of at least 120 seconds of data have been obtained. The number N will depend on the sampling rate of the signal values provided to the controller. For example, if the sampling rate is 64Hz and at least 120 seconds of data are required, then N = 7680. Other values of N are within the scope of the present disclosure.

If less than N signal values have been obtained then the method returns to step 1202 so that additional samples are gathered, filtered, and extrema detected. Steps 1202 to 1206 are repeated until the N signal values are obtained.

If N or more signal values have been obtained, the method proceeds to step 1210.

In step 1210, the controller calculates the vertical differences (differences in amplitude) between subsequent local extrema and takes their absolute values. These values are referred to as absolute amplitude deltas. The absolute amplitude deltas are computed between each consecutive local maxima (peak) and local minima (trough).

The absolute amplitude deltas are then compared to a threshold value to determine a sub-set of absolute amplitude deltas which are used to determine the breathing rate. In some examples, the threshold value is determined from the third quartile (75 percentile) Q3 of the absolute amplitude deltas. This means that the third quartile Q3 is determined and used to set the threshold value. The threshold value is a multiple of Q3. The multiple can be set as appropriate by the skilled person. It will be appreciated that the multiple is less than 1, preferably less than 0.5 and preferably still in the range of 0. 2 to 0.4. In a preferred example, the multiple is 0.3 such that the threshold value is 0.3 x Q. In step 1214, the controller finds the pair of subsequent extrema that are separated by the smallest absolute amplitude delta.

In step 1216, the identified smallest absolute amplitude delta is compared to the threshold value defined in step 1210.

If the smallest absolute amplitude delta is less than the threshold value, then the pair is interpreted to be caused by a random fluctuation, and is removed from the array of extrema values in step 1218. The method then returns to step 1214 to again find the pair of subsequent extrema in the array with the smallest absolute amplitude delta. In this way pairs of extrema are removed from the array until the smallest absolute amplitude delta of the remaining pairs is greater than or equal to the threshold value.

If the smallest difference is greater than or equal to the threshold value, the controller proceeds to step 1220 and calculates the breathing rate from the remaining extrema values in the array. This generally involves selecting the remaining peaks (which are considered valid inspirations) to form an array of breaths. The time-domain spacing between the peaks is used to compute a breathing cycle. That is, the time between two consecutive peak inspirations.

The breathing rate may be calculated by determining the duration of each of the breathing cycles contained within the array and dividing this duration by the total number of breathing cycles contained within the array. The reciprocal of this value is then taken to obtain the breathing rate. The duration of each of the breathing cycles can be determined by calculating the difference between index values for successive local maxima in the array In some examples, the array is divided into segments and the breathing rate is determined for each of these windows. For example, if the array covers 120 seconds of data, the array is divided into 30 second segments, and the breathing rate is determined for each of these segments. These average values may then be smoothed using a moving average filter. The moving average filter can consider breathing rate values determined in a previous signal processing operation. The moving average filter may have a window of 120 seconds for example.

Once the average breathing rate is determined, the controller enters a new signal processing operation such that the process outlined in FIG. 12 is repeated.

Advantageously, the process outlined in FIG. 12 is optimised for performance in real-time as impedance signal values are being determined by the analogue-to-digital front end 726 and output to the controller 202. Extrema are detected in the signal values as they are being obtained from the analogue-to-digital front end 726 and then the correction of the detected peaks (step 1214 -1218) and the calculation of breathing rate (step 1220) are only performed once a sufficient number, N, of signal values have been read from the FIFO buffer. This approach is memory efficient as it does not require that the N signal values are stored in memory prior to the performance of steps 1210-1220. Instead, only a smaller number of values representative of the extrema (e.g., their amplitude values and index values) are required to be stored. Being memory efficient is particularly important for wearable electronics modules as their small size limits the available space for memory units and battery capacity.

After determining the biological metric, the controller 202 of the electronics module 110 may control a communicator of the electronics module 110 such as the second wireless communicator 714 to transmit the biological metric to an external device such as user electronic device 104. User electronic device 104 may display or otherwise output the biological metric to the wearer.

Referring to FIG. 13, there is shown a schematic diagram of a user electronic device 104 according to an example aspect of the present disclosure. The user electronic device 104 is in the form of a mobile phone or tablet and comprises a controller 1302, a memory 1304, a wireless communicator 1306, a display 1308, a user input unit 1310, a capturing device in the form of a camera 1312 and an inertial measurement unit 1314. The controller 1302 provides overall control to the user electronic device 104.

The user input unit 1310 receives inputs from the user such as a user credential. The memory 1304 stores information for the user electronic device 104.

The display 1308 is arranged to display a user interface for applications operable on the user electronic device 104.

The inertial measurement unit 1314 provides motion and/or orientation detection and may comprise an accelerometer and optionally one or both of a gyroscope and a magnetometer.

The user electronic device 104 may also include a biometric sensor. The biometric sensor may be used to identify a user or users of device based on unique physiological features. The biometric sensor may be: a fingerprint sensor used to capture an image of a user's fingerprint; an iris scanner or a retina scanner configured to capture an image of a user's iris or retina; an ECG module used to measure the user's ECG; or the camera of the user electronic arranged to capture the face of the user. The biometric sensor may be an internal module of the user electronic device 104. The biometric module may be an external (standalone) device which may be coupled to the user electronic device 104 by a wired or wireless link.

The controller 1302 is configured to launch an application which is configured to display insights derived from the biosignal data processed by the analogue-to-digital front end (e.g., analogue-to-digital front end 726 of FIG. 8) of the electronics module (e.g., electronics module 110 of FIG. 7) , input to electronics module controller (e.g., controller 202 of FIG. 7), and then transmitted from the electronics module. The transmitted data is received by the wireless communicator 1306 of the user electronic device 104 and input to the controller 1302.

Insights include, but are not limited to, heartrate, respiration rate, core temperature but can also include identification data for the wearer using the wearable assembly (e.g., wearable assembly 102 of FIG. 1).

The display 1308 is also configured to display an ECG signal trace. To display a signal trace may use raw ECG data from the electronics module.

The display 1308 may be a presence-sensitive display and therefore may comprise the user input unit 1310 The presence-sensitive display may include a display component and a presence-sensitive input component. The presence sensitive display may be a touch-screen display arranged as part of the user input unit 1310.

User electronic devices 104 in accordance with the present disclosure 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 disclosure. The user electronic device 104 may be 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 head-mounted device. The user electronic device 104 may be desktop PC, workstations, television apparatus or a projector, e.g. arranged to project a display onto a surface.

In use, the electronics module is configured to receive raw biosignal data from the sensors of the wearable article and which are coupled to the controller via the sensing interface and the analogue-to-digital front end 726 for further processing and transmission to the user electronic device 104 as described above. The data transmitted to the user electronics user electronic device 104 includes raw or processed biosignal data such as ECG data, heart rate, respiration data, breathing rate, core temperature, IMU data and other insights as determined, and as required.

The controller 1302 is also operable to launch an application which is configured to receive, process and display data, such as raw or processed biosignal data, from the electronics module. A user, such as the wearer, is able to configure the application, using user inputs, to receive, process and display the received data in accordance with these user inputs.

The user electronic device 104 is arranged to receive the transmitted data from the electronics module via the communicator 1306 and which are coupled to the controller 1302, and then to process and display the data in accordance with the user configuration.

The controller 1302 of the user electronics user electronic device 104 is operable to display information to a user on the display 1308 as part of the user interface. Information displayed can include the biological metric determined by the electronics module 110 as described above. Other insights and data can be displayed on the display 1308 as part of the user interface and as required. Examples might be a hearftate in beats per minute, core temperature data and respiration rate.

In summary, there is provided an electronics module for a wearable article. The electronics module comprises a front end that supplies digital signal values obtained from biological signals to a controller of the electronics module. The controller stores the values in a buffer and performs a signal processing operation that comprises: (a) reading M of the values from the buffer (1002); (b) detecting extrema in the M values (1004); and (c) determining whether at least N>M of signal values have been obtained from the buffer during the signal processing operation (1008). If fewer than N signal values have been obtained from the buffer, the controller repeats steps (a) -(c) until N signal values have been read from the buffer. Otherwise, the controller uses the extrema detected in the signal processing operation to determine a biological metric (1010), and repeats the performance of the signal processing operation.

At least some of the example embodiments described herein may be constructed, partially or wholly, using dedicated special-purpose hardware. Terms such as 'component', 'module' or 'unit' used herein may include, but are not limited to, a hardware device, such as circuitry in the form of discrete or integrated components, a Field Programmable Gate Array (FPGA) or Application Specific Integrated Circuit (ASIC), which performs certain tasks or provides the associated functionality. 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

CLAIMS1. An electronics module for a wearable article, the electronics module comprising: a front end arranged to receive biological signals from one or more sensors, and process the received signals to generate digital signal values; and a controller coupled to the front end, wherein the controller is operable to receive digital signal values from the front end and store the digital signal values in a buffer, wherein the controller is operable to perform a signal processing operation comprising: (a) reading a number M of digital signal values from the buffer; (b) detecting one or more extrema in the M digital signal values; and (c) determining whether at least a number N of signal values have been read from the buffer during the signal processing operation, where N is greater than M, wherein if fewer than N signal values have been read from the buffer, the controller is operable to repeat steps (a) -(c) until N signal values have been read from the buffer, and wherein if at least N signal values have been read from the buffer, the controller is operable to use the extrema detected in the signal processing operation to determine a biological metric and repeat the performance of the signal processing operation.
2. The electronics module of claim 1, wherein prior to detecting one or more extrema in the M digital signal values, the controller is operable to filter the M digital signal values.
3. The electronics module of claim 1 or 2, wherein if at least N signal values have been read from the buffer, the controller is operable to remove one or more anomalous extrema prior to determining the biological metric.
4. The electronics module of claim 3, wherein the controller being operable to remove one or more anomalous extrema comprises the controller being operable to compare extrema to a threshold value and remove extrema having an amplitude less than the threshold value.
5. The electronics module of claim 4, wherein the threshold value is determined according to the M digital signal values.
6. The electronics module of claim 5, wherein the threshold value is determined according to the spectral power of the M digital signal values.
7. The electronics module of claim 4, wherein the threshold value is determined according to the extrema.
8. The electronics module of any preceding claim, wherein M is between 1 and 50.
9. The electronics module of claim 8, wherein M is between 1 and 20.
10. The electronics module of any preceding claim, wherein N is between 1000 and 10000.
11. The electronics module of claim 10, wherein N is between 4000 and 8000.
12. The electronics module of any preceding claim, wherein the biological signals are signals indicative of the heartrate, the detected extrema are peaks, and the biological metric is the heartrate of the wearer.II
13. The electronics module of claim 12, wherein the signals indicative of the heartrate are ECG signals.
14. The electronics module of any preceding claim, wherein the biological signals are signals indicative of the breathing rate, the detected extrema are local minima and local maxima, and the biological metric is the breathing rate of the wearer.
15. The electronics module of claim 14, wherein the signals indicative of the breathing rate are bioimpedance signals.
16. The electronics module of claim 15, wherein the bioimpedance signals are impedance plethysmography signals.
17. The electronics module of any of claims 14 to 16, wherein the breathing rate is determined by calculating the reciprocal of an average breathing rate duration determined from the extrema.
18. The electronics module of any preceding claim, wherein the electronics module comprises a communicator arranged to transmit the biological metric to a remote device.
19. The electronics module of any preceding claim, wherein the electronics module comprises an output unit arranged to output the biological metric.
20. The electronics module of any preceding claim, wherein the buffer is a first in, first out, FIFO, buffer.
21. A method performed by an electronics module for a wearable article, the method comprising: receiving, by a front end of the electronics module, biological signals from one or more sensors; processing, by the front end of the electronics module, the received signals to generate digital signal values; obtaining, by a controller of the electronics module, digital signal values from the front end; storing, by the controller, the digital signal values in a buffer; performing, by the controller, a signal processing operation comprising: (a) reading a number M of digital signal values from the buffer; (b) detecting one or more extrema in the M digital signal values; and (c) determining whether at least a number N of signal values have been obtained from the buffer during the signal processing operation, where N is greater than M, wherein if fewer than N signal values have been read from the buffer, the method comprises repeating steps (a) -(c) until N signal values have been read from the buffer, and wherein if at least N signal values have been read from the buffer, the method further comprises using, by the controller, the extrema detected in the signal processing operation to determine a biological metric and repeating the performance of the signal processing operation.