WO2024058714A1

WO2024058714A1 - A strain sensing apparatus for monitoring muscle performance, a system and a method for monitoring muscle performance

Info

Publication number: WO2024058714A1
Application number: PCT/SG2023/050600
Authority: WO
Inventors: Joo Chuan Yeo; Edmond Jea Ginn CHANG; Marshall Whye Loong LEONG
Original assignee: Microtube Technologies Pte. Ltd.
Priority date: 2022-09-13
Filing date: 2023-08-31
Publication date: 2024-03-21

Abstract

The present invention relates to a wearable sensing apparatus, which employs the use of strain sensor(s) to determine at least one of muscle activity, body motion, muscle performance, muscle fatigue levels and so forth. Signals from the sensor can be processed by an operably coupled processing unit hub running an algorithm to provide analysis on the data based at least one of the following parameters: its range of motion, the time and speed in which the muscle expands or contracts, its response time to external stimuli, the stability of muscle expansion/contraction, muscle tremors/twitches and so forth.

Description

A STRAIN SENSING APPARATUS FOR MONITORING MUSCLE PERFORMANCE, A SYSTEM AND A METHOD FOR MONITORING MUSCLE PERFORMANCE

TECHNICAL FIELD

The present disclosure relates to a strain sensing device apparatus configured to monitor muscle performance, specifically by measuring changes in muscle volume. A corresponding system and method are also disclosed.

BACKGROUND

Evaluation of activity performance is important for many reasons. For instance, information about the performance level of a muscle(s) is useful for sports coaching, performance enhancement and injury prevention. There are two possible methods of detecting and monitoring muscle activity.

The first method is electromyography which are collected by measuring conductivity signals on the skin's surface through electrode(s) placed directly on the skin layer on top of the muscles. Generally, it is not desired as it is uncomfortable and hinders movements of users. Furthermore, perspiration on the users' skin surface affect signal quality, and lead to loss of adhesion for the electrodes on the skin.

The second method relies on the use of a strain sensor to measure muscle volume changes during muscle activity. It is less likely to hinder movement and more convenient to use especially during tasks involving muscle activity.

It is desirable if the preferred second method can be carried out in an accessible manner and allows users to carry out physical activity without being hampered while muscle activity is being monitored such that more accurate quantitative information can be obtained.

SUMMARY

In a first aspect, there is provided an apparatus for monitoring muscle performance, the apparatus comprising: at least one strain sensor configured to detect flexure/extension of at least one muscle, and to correspondingly transmit an activity signal; at least one processor configured to receive the activity signal; wherein the strain sensor is configured to detect a change in at least one of length and cross-sectional area of the strain sensor.

In a second aspect, there is provided a system for monitoring muscle performance, the system comprising at least one data processing configured to carry out the steps comprising: receive, from at least one strain sensor, an activity signal; transmit, from an apparatus processor, the activity signal; receive, at a central server, the activity signal; process, at the central server, the activity signal; and transmit, from the central server, an output signal.

In a final aspect, there is provided a data processor implemented method for monitoring muscle performance, the method comprising: receiving, from at least one strain sensor, an activity signal; transmitting, from an apparatus processor, the activity signal; receiving, at a central server, the activity signal; processing, at the central server, the activity signal; and transmitting, from the central server, an output signal.

It will be appreciated that the broad forms of the invention and their respective features can be used in conjunction, interchangeably and/or independently, and reference to separate broad forms is not intended to be limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

A non-limiting example of the present invention will now be described, by way of nonlimiting example only, with reference to the accompanying drawings in which:

FIG 1A illustrates a first example of an apparatus of the present invention;

FIG IB illustrates an example quantitative output of the apparatus of FIG 1A;

FIG 2 illustrates a second example of an apparatus of the present invention;

FIG 3 illustrates the apparatus of FIG 2 being mounted on a piece of fabric configured to be worn over a body part;

FIG 4 illustrates the apparatus of FIG 2 being worn over a body part during use; FIG 5 illustrates another view of the apparatus of FIG 2;

FIG 6 illustrates a schematic diagram of an example of a system of the present invention; FIG 7 illustrates an example schematic diagram of the apparatus of FIG 2;

FIG 8 illustrates a schematic diagram showing an example user device of the system of FIG 6;

FIG 9 illustrates a schematic diagram showing components of a central server of the system of FIG 6;

FIG 10 illustrates a usage scenario of the apparatus of FIG 2; and

FIG 11 illustrates an example process flow for a method of the present invention.

DETAILED DESCRIPTION

Embodiments of the present disclosure relate to a wearable sensing apparatus, which employs the use of strain sensor(s) to determine at least one of muscle activity, body motion, muscle performance, muscle fatigue levels and so forth. Signals from the sensor can be processed by an operably coupled processing unit hub running an algorithm to provide analysis on the data based at least one of the following parameters: its range of motion, the time and speed in which the muscle expands or contracts, its response time to external stimuli, the stability of muscle expansion/contraction, muscle tremors/twitches and so forth. The algorithm may further involve a scoring system to provide an overall muscle performance score based on a weightage of the parameters abovementioned. The muscle performance may be trackable over time or during a predefined activity set and the algorithm may be configured to predict muscle fatigue levels.

Referring to FIG 1A, in accordance with one embodiment, there is provided an apparatus 100 for monitoring muscle performance. The apparatus 100 includes at least two components, namely, a sensor 101 and a processing unit hub 102. The sensor 101 comprises a first end 105 secured to processing unit hub 102 and a second end 110. The sensor 101 is configured to detect and measure changes in strain. The strain sensor 101 can be selected from, for example, optical bend sensor, piezoelectric fiber, piezoelectric sensor, piezoresistive fiber, piezoresistive sensor, capacitive sensor, inductive sensor, strain gauge, ultrasonic-based motion sensor and so forth. The apparatus 100 can be powered by an integral power source.

Referring to FIG IB, there is shown a quantitative output of the apparatus 100. The second end 110 is configured to change the electrical properties of the sensor 101 according to a length of extension of the sensor 101. Alternatively, the electrical properties of the sensor 101 can also be changed by reducing a cross sectional area of the sensor 101. The sensor 101 can include a tubular polymeric structure filled with conductive liquid. The polymeric material can include, for example, silicone elastomers, polydimethylsiloxanes, polyurethanes, elastolefins, thermoplastic elastomers, fluorosilicone rubbers, acrylic rubbers, fluoroelastomers and the like. In addition, the conductive liquid can include, for example, liquid metals, eutectic gallium indium alloy, silver-based conductive liquids, graphene-based conductive liquids and the like.

FIG IB shows an example of a characteristic curve of the sensor 101, where vertical axis, strain is defined

where L is the length of the sensor 101 at a particular juncture,

and Lo is the original length of the sensor 101. Horizontal axis, normalized resistance Rn,

R~R is defined as where R is resistance at a particular juncture and Ro is the original resistance. Typically, the tubular structure reduces its cross-sectional area and increases its length when it is being stretched, resulting in a change in electrical resistance. More specifically, the sensor gauge factor, defined as the change in normalized resistance over the change in mechanical strain, is 2. This is demonstrated by a substantially linear gradient of the characteristic curve in FIG IB.

In other embodiments, the sensor 101 may be screen-printed on a stretchable substrate using conductive ink. Yet in other embodiments, the second end 110 may be configured to vary electrical properties of the sensor 101, for example, electrical capacitance, electrical inductance, triboelectric current and the like.

Referring to FIG 2, in accordance with another embodiment of the apparatus, an apparatus 200 includes a sensor 201 that is integral with a processing unit hub 202. The sensor 201 can be an elastic member with a movable end 203. The movable end 203 is configured to extend in accordance with muscle volume changes. Preferably, the strain sensing range is at least 10%. The processing unit hub 202 comprises of an interface 205 which can be configured to, for example, control the apparatus 200 and to display text and/or images. The apparatus 200 can be powered by an integral power source.

The interface 205 can comprise for example, a plurality of LED indicators, at least one actuator, a touch screen, a microphone, a vibrational haptic module and so forth. When the apparatus 200 is powered on, the processing unit hub 202 is configured to convert analog strain to digital signals as described earlier, and to transmit the digital signals to a central server for processing. Signals may also be activated by a user or pre-programmed to be sent from the central server to the processing unit hub 202 to generate haptic, audio or visual feedback to the user in relation to muscle activity.

The movable end 203 of the sensor 201 may be secured to a hook 206 configured to attach to a part of a fabric or garment. In other embodiments, the hook 206 may be in a form of fastener that can be attached to form part of a piece of clothing. In yet another embodiment, the movable end 203 of the sensor 201 may be molded with a thermoplastic object that can be secured to another body part.

Referring to FIG. 3, in one embodiment, the apparatus 200 is mounted to a piece of fabric 302 that is configured to be worn over a body part to measure muscle volume changes. In the given example, the fabric 302 comprises a fastener 301, a matching fastener surface 302 and a fabric loop 303. In this embodiment, the apparatus 200 is configured to be secured to the body part and to extend mechanically in response to any muscle volume changes. Accordingly, the piece of fabric 302 should be able to withstand at least 10% of mechanical strain. In one example, the fabric composition can be a 80% Nylon and 20% Spandex blend. Typically, the fabric composition should be able to allow the at least 10% of mechanical strain and should also not cause discomfort to a user of the apparatus 200.

Referring to FIG. 4, there is shown the apparatus 200 of the embodiment in FIG 3 being worn over an upper arm 401 of a user. At this position, muscle volume changes to a bicep and/or tricep can be determined. In this example, to facilitate elbow flexion, the biceps myosin fibers shorten, resulting in a change in biceps muscle volume. In turn, the expansion of the biceps muscle stretches the fabric 302 of the apparatus 200 and pulls the movable end 203 of the sensor 201, extending a length of the fabric 302 which correspondingly extends the sensor 201 which generates a change in an electrical signal. When the elbow extends, the biceps myosin fibers relaxes, causing the movable end 203 of the sensor 201 to return to its original length, resulting in the electrical signal reverting to its baseline state. In other embodiments, at least one apparatus 200 can be worn over, and not limited to, a finger, a hand, a forearm, a head, a neck, a torso, a shoulder blade, a gut, a groin, a thigh, a calf, an ankle, a toe, and so forth. In another embodiment, a plurality of apparatus 200 can be used at the same time to analyze multiple moving body parts and muscle volume changes. Referring to FIG. 5, further information will now be provided for the processing unit hub 202 of the apparatus 200. The hub 202 is configured to perform low-level, time-critical scheduling/coordinating of the data sampling on at least one of the sensors as well as ensuring user mobility by enabling wireless transmission of sensor data to a central server. The processing unit hub 202 can be powered by an integral power source, such as, for example, a lithium polymer battery, a nickel cadmium battery, any solid-state batteries and so forth. In another embodiment, power is delivered by a cable via a data port 501, or is delivered wirelessly using electromagnetic inductive means. The wireless transmission of data from the hub 202 is via Bluetooth® wireless transmission. It should be appreciated that other wireless transmission protocols including WIFI direct and/or using router devices can also be used. Alternatively, the data transmission can be transferred through the port 501 via a cable. Apart from the interface 205, communication to the user can also be delivered from an output port 502, using, for example, a plurality of LEDs, audio speakers, pneumatic haptics, actuators, electrodes, and so forth.

An example of a system 600 for monitoring muscle performance will now be described with reference to FIG 6. It should be noted that the apparatus 200 can be used as part of the system 600.

In this example, the system 600 includes one or more user devices 620, a communications network 650, a central server 660, and at least one apparatus 200. The user device 620 and the central server 660 are in communication with each other via the communications network 650. In addition, the user device 620 is also in communication with the at least one apparatus 200 via a wireless communication channel as described previously. The communications network 650 can be of any appropriate form, such as the Internet and/or a number of local area networks (LANs). Further details of respective components of the system 600 will be provided a following portion of the description. It will be appreciated that the configuration shown in FIG 6 is for the purpose of illustration only.

APPARATUS 200

Referring to FIG. 7, there is shown an example schematic diagram of the apparatus 200 which is configured to capture muscle volume changes, and to infer skeletal muscle activity corresponding to body motion, muscle performance, and muscle fatigue. The processing unit hub 202 includes a processor 701 is configured to receive the electrical signals from the sensor 201 and transmit signals to the user device 620 and/or the central server 660 via a transmission module 704. The battery 706 is used to power the apparatus 200 may be integral within the processor unit hub 202 or external of the processor unit hub 202. It should be appreciated that the sensor 201 can comprise a network of sensors, including, for example, strain sensors, pressure sensors, inertial sensors, accelerometers, gyroscopes, magnetometers, electromyography sensors and so forth. Communication between any of the sensor network and/or the processor 701 may be wireless, wired, or any combination thereof. The processing unit hub 202 can comprise in any combination of but not limited to, additional sensors 707, an analyzer 703, a transmission module 704, and an indicator 705 (configured to transmit visual and/or audio signals). It should be appreciated that the transmission module 704 can be using, for example, Bluetooth, WIFI Direct, WIFI or any wireless data transmission mechanism.

USER DEVICE 620

The user device 620 of any of the examples herein may be a computer device such as, for example, a mobile phone with a capability to download and operate mobile applications, a laptop computer and so forth. The user device 620 should be connectable to the communications network 650. An exemplary embodiment of the user device 620 is shown in FIG 8. As shown, the device 620 includes the following components in electronic communication via a bus 811:

1. a display 802;

2. non-volatile memory 803;

3. random access memory ("RAM") 804;

4. data processor(s) 801;

5. a transceiver component 805 that includes a transceiver(s);

6. an image capture module 810; and

7. input controls 807.

It should be appreciated that the image capture module 810 can also be configured to be used for scanning purposes, for example to scan graphical indicia (like a QR code) on the apparatus 200 to enable pairing with the apparatus 200. Although the components depicted in FIG 8 represent physical components, FIG 8 is not intended to be a hardware diagram; thus many of the components depicted in FIG 8 may be realized by common constructs or distributed among additional physical components. Moreover, it is contemplated that other existing and yet-to-be developed physical components and architectures may be utilized to implement the functional components described with reference to FIG 8.

CENTRAL SERVER 660

The central server 660 is a hardware and software suite comprised of pre-programmed logic, algorithms and other means of processing information coming in, in order to send out information which is useful to the objective of the system in which the central server 660 resides. For the sake of illustration, hardware which can be used by the central server 660 will be described briefly herein. In some embodiments, the central server 660 can be replaced by a cloud based processing service provider. It should be appreciated that the central server 660 can be part of a server array administered by the cloud based processing service provider.

The central server 660 can broadly comprise a database which stores data and communicates with user devices 620. The central server 660 may be underpinned by any suitable processing device, and one such suitable device is shown in FIG 9.

In this example, the central server 660 is in communication with a communications network 650, as shown in FIG 9. The central server 660 is able to communicate with the user devices 620 and/or other processing devices, as required, over a communications network 650 or directly with the respective devices.

The components of the central server 660 can be configured in a variety of ways. The components can be implemented entirely by software to be executed on standard computer server hardware, which may comprise one hardware unit or different computer hardware units distributed over various locations, some of which may require the communications network 650 for communication. In the example shown in FIG 9, the central server 660 is a commercially available computer system based on a 32 bit or a 64 bit Intel architecture, and the processes and/or methods executed or performed by the device 660 are implemented in the form of programming instructions of one or more software components or modules 902 stored on non-volatile (e.g., hard disk) computer-readable storage 903 associated with the central processor 760. For the implementation in the system 600, the modules 902 can include, for example: a comparison module, an analysis module, a tracking module, and so forth.

The central server 660 includes at least one or more of the following standard, commercially available, computer components, all interconnected by a bus 905:

1. random access memory (RAM) 906;

2. at least one central processing unit (CPU) 907; and

3. a transceiver 908.

The central server 660, in the current embodiment, processes the sensor data obtained from the sensor 201 to determine muscle performance, including but not limited to, body motion, muscle performance and muscle fatigue levels. The transceiver 908 receives sensor data and user data. The user data can be input via the interface 205 or via the user device 620. The user data can include the user's physical profile which can include, for example, body weight, height, age, gender, type of exercise and training weight during exercise, and so forth. In other embodiments, user may input more parameters relating to his/her fitness profile, including and not limited to, muscle mass, fat percentage, exercise frequency, resting heart rate, blood pressure, calorie consumption data, fitness goal plans and so forth. The exercise may be determined by a fitness trainer, physiotherapist, occupational therapist, or the user. The transceiver 908 may also be configured to transmit a notification to the user the commencement of the exercise. In other embodiments, the commencement of the exercise may be triggered by the indicator 705 of the apparatus 200.

Referring to FIG 10, a sensor signature 1008 may be presented on the user device 620 and may be processed by the central server 660 during or after the physical activity 1009. It should be appreciated that the physical activity 1009 involves flexure and/or extension of at least one muscle. The processing of the data relies on a software module in the processor 701. The software module configured for collecting and processing signal signatures 1008 resultant from the physical activity 1009. A calibration sequence may be required to determine a baseline reference for the sensor signature 1008. The calibration sequence may include asking the user to perform a certain exercise routine and adjusting the apparatus 200 to obtain a consistent repeatable signal waveform. Importantly, data analysis depending on the sensor signature 1008 and can be analyzed both in a time domain and a frequency domain. Parameters of the sensor signature 1009 being analysed can include, for example, peak values, trough values, time intervals, gradients of data, presence of multiple peaks, and so forth can form part of the data analysis. The functionality of the software module will now be provided.

The sensor signature 1008 is assessed to determine various parameters. In the current example, to obtain the requisite parameters, the software module determines fifteen feature vectors. Of these, nine are time domain features and six are frequency domain features. In one embodiment, the various parameters can provide calculated features pertaining to, and not limited to, range of motion, speed of motion, stability of motion, acceleration of motion, reaction time, muscle tremors, muscle force/power, muscle strength, muscle fatigue level, muscle performance level, muscle pain level, workout consistency, workout uniformity and so forth. It should be appreciated that fewer or additional parameters may be obtained without departing from the scope of the present disclosure. The software module can also be configured to train a classifier on the calculated features which will be used to classify/determine skeletal muscle identities as well as the state of skeletal muscle performance and fatigue. In the current embodiment, this may be achieved using machine learning techniques, supervised or otherwise, for example.

In relation to supervised machine learning, in order for the apparatus 200 to match new feature instances to a particular muscle group, it should first be trained using a labeled initial set of muscle signatures obtained over a specific training period from the given muscle group. Therefore, the central server 660 is initially provided with both the muscle signatures and an accompanying set of labels relating to the muscle performance. This initial set of muscle signatures and labels constitutes the training data used to train a supervised classifier. Once training data is input, the software module in the current embodiment, builds a supervised classifier. This classifier will be used to assign labels or muscle identities to future new data points and to infer individual muscle performance or fatigue level across the entire population dataset. At the end of the activity, in the given example, the central server 660 may provide at least one output such as, for example, an overall score, individual features performance rating, muscle activation profile, velocity profile, workout recommendations for the user and so forth. It should be appreciated that muscle activation profile relates to the pattern and level of muscle contraction across different muscles during a specific activity, and velocity profile relates to a pattern of muscle contraction velocities over a specific movement or activity.

Referring to FIG 11, there is shown an example process flow of a method 1100 for monitoring muscle performance. The method 1100 can be carried out in the system 600 or in any other system. In addition, the apparatus 200 can be used in the method 1100, but should not exclude other apparatus or devices that can be used to carry out or constitute an aspect of the method 1100. For the sake of illustration, respective components used in the system 600 will be referenced when describing the method 1100.

At step 1110, the strain sensor 201 of the apparatus 200 transmits an activity signal, the activity signal being generated during occurrence of at least one of a change in length or cross-sectional area of the strain sensor 201. At step 1120, the apparatus processor 701 receives the activity signal. Subsequently, at step 1130, the apparatus processor 701 transmits the activity signal to the central server 660, via the transmission module 704.

At step 1140, the activity signal is received at the central server 660 and the central server 660 processes the activity signal at step 1150. The processing of the activity signal at the central server 660 is associated with user data so that customizable output can be generated. At step 1160, the central server 660 transmits an output signal, the output signal including presentation of a sensor signature on a display, as shown in FIG 10. The output signal is configured to provide information selected from, for example, an overall score, individual features performance rating, muscle activation profile, velocity profile, workout recommendations for a user and so forth.

It should be appreciated that the apparatus 200, system 600 and method 1100 are all configured to provide substantially identical advantages and identical end user experiences. Many modifications will be apparent to those skilled in the art without departing from the scope of the present invention.

Throughout this specification, unless the context requires otherwise, the word "comprise", and variations such as "comprises" and "comprising", will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.

The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgment or admission or any form of suggestion that that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

Claims

1. An apparatus for monitoring muscle performance, the apparatus comprising: at least one strain sensor configured to detect flexure/extension of at least one muscle, and to correspondingly transmit an activity signal; at least one processor configured to receive the activity signal; wherein the strain sensor is configured to detect a change in at least one of length and cross-sectional area of the strain sensor.

2. The apparatus of claim 1, wherein the at least one strain sensor includes a tubular polymeric structure filled with conductive liquid.

3. The apparatus of claim 1, wherein the at least one strain sensor is screen-printed on a stretchable substrate using conductive ink.

4. The apparatus of any of claims 1 to 3, wherein the at least one strain sensor is integrated with a piece of fabric, the piece of fabric being worn over the at least one muscle.

5. The apparatus of claim 4, wherein the piece of fabric is configured to withstand at least 10% strain.

6. The apparatus of any of claims 1 to 5, further comprising a wireless transceiver.

7. A system for monitoring muscle performance, the system comprising at least one data processing configured to carry out the steps comprising: receive, from at least one strain sensor, an activity signal; transmit, from an apparatus processor, the activity signal; receive, at a central server, the activity signal; process, at the central server, the activity signal; and transmit, from the central server, an output signal.

8. The system of claim 7, wherein the output signal is configured to provide information selected from a group consisting of: an overall score, individual features performance rating, muscle activation profile, velocity profile, and workout recommendations for a user.

9. The system of either claim 7 or 8, wherein the activity signal is generated during occurrence of at least one of a change in length and cross-sectional area of the at least one strain sensor.

10. The system of any of claims 7 to 9, wherein the central server is also configured to receive user data for the output signal to be customisable.

11. The system of any of claims 7 to 10, wherein the output signal includes presentation of a sensor signature on a display.

12. The system of claim 11, wherein the output signal is dependent on a predefined calibration protocol.

13. A data processor implemented method for monitoring muscle performance, the method comprising: receiving, from at least one strain sensor, an activity signal; transmitting, from an apparatus processor, the activity signal; receiving, at a central server, the activity signal; processing, at the central server, the activity signal; and transmitting, from the central server, an output signal.

14. The method of claim 13, wherein the output signal is configured to provide information selected from a group consisting of: an overall score, individual features performance rating, muscle activation profile, velocity profile, and workout recommendations for a user.

15. The method of either claim 13 or 14, wherein the activity signal is generated during occurrence of at least one of a change in length and cross-sectional area of the at least one strain sensor.

16. The method of any of claims 13 to 15, wherein the central server is also configured to receive user data for the output signal to be customisable.

17. The method of any of claims 13 to 16, wherein the output signal includes presentation of a sensor signature on a display.

18. The method of claim 17, wherein the output signal is dependent on a predefined calibration protocol.