Muscle activation detection device

Abstract

A muscle activation detection device includes: a first sEMG (surface electromyogram) sensor configured to receive a first sEMG signal associated with a user; a second sEMG sensor configured to receive a second sEMG signal associated with the user; and a processing unit configured to determine a muscle contraction of the user based on the first and second sEMG signals.

Description

TECHNICAL FIELD

[0001] The present invention relates to a muscle activation detection device, in particular, but not exclusively, to a muscle activation detection device based on surface electromyogram (sEMG) sensing. BACKGROUND

[0002] Nowadays, physical sensors such as IMU and pressure sensors are widely used for robot pose detection and control. Compared with physical sensors, biological signals have inherent advantages in detecting human activities and controlling wearable robots that collaborate with humans. In human-robot collaboration, surface electromyogram (sEMG) is one of the effective biological signals available.

[0003] To assist in activities of daily living (ADL) tasks, wearable robots such as exoskeletons, soft wearable robots, and prosthetic robots have been widely developed. One of the challenges of wearable robots is the synchronization between human muscles and artificial muscles or actuators, which means that the instantaneous detection of human motion intention is required. SUMMARY

[0004] According to a first aspect, there is provided a muscle activation detection device comprising:

[0005] a first sEMG (surface electromyogram) sensor configured to receive a first sEMG signal associated with a user;

[0006] a second sEMG sensor configured to receive a second sEMG signal associated with the user; and

[0007] a processing unit configured to determine a muscle contraction of the user based on the first and second sEMG signals.

[0008] In one example, the muscle activation detection device further comprises a phase comparator configured to compare a phase difference between the first and second sEMG signals to determine a real-time muscle contraction.

[0009] In one example, a muscle in a non-activated state is represented by sEMG signals detecting zero phase difference on both sides of IZ, while a muscle in an activated state is represented by sEMG signals detecting non-zero phase difference or anti-phase difference at different locations on both sides of IZ.

[0010] In one example, a relative distance between the first and second sEMG sensors on muscle fibers is positively correlated with a phase difference between the first and second sEMG signals.

[0011] In one example, the processing unit is configured to send an output signal associated with a force or torque parameter of the robotic device based on the determined degree of muscle contraction.

[0012] In one example, the switch control of the robotic device is determined based on the determined start and end of muscle contraction.

[0013] In one example, the sEMG sensor further comprises soft electrodes configured to physically contact a skin surface of the user.

[0014] In one example, the muscle activation detection apparatus further comprises an amplifier configured to amplify the electromyographic signal to a measurable level.

[0015] In one example, the muscle activation detection apparatus further comprises a high pass filter configured to perform high pass filtering on the sEMG signal.

[0016] In one example, the muscle activation detection apparatus further comprises a wireless radio frequency module configured to control a remote robotic device.

[0017] In one example, the muscle activation detection apparatus further comprises a CAN chip module configured to control a remote robotic device through a wired means.

[0018] In one example, the muscle activation detection apparatus further comprises a serial communication module configured to monitor data and transmit the data to a remote robotic device.

[0019] In one example, the muscle activation detection apparatus further comprises a trigger generation unit in communication with the phase comparator signal, and the trigger generation unit is configured to trigger a robotic device based on an output from the phase comparator associated with a phase difference compared between the first and second sEMG signals.

[0020] In one example, the trigger generation unit further comprises a mixer configured to mix the first and second sEMG signals to generate a mixed signal.

[0021] In one example, the trigger generation unit is configured to detect a change in the mixed signal corresponding to a start of muscle activation.

[0022] In one example, the trigger generation unit is configured to generate a binary trigger signal in response to the detected change in the mixed signal.

[0023] In one example, the binary trigger signal indicates a transition from a non-activated muscle state to an activated muscle state.

[0024] In one example, the trigger generation unit is configured to perform a zero-crossing detection on the mixed signal to generate the binary trigger signal.

[0025] In one example, the sEMG sensor further comprises a linear electrode configured to detect an IZ position and connect to a preamplifier of the sEMG sensor.

[0026] In one example, the linear electrode further comprises a linear electrode array formed by the first sEMG sensor and the second sEMG sensor and is configured to be placed along a length of a muscle.

[0027] In one example, the linear electrode comprises a plurality of skin contact points and corresponding sEMG sensors spaced apart from each other at uniform distances.

[0028] The term "comprising" (and grammatical variations thereof) is used herein in the inclusive sense of "having" or "including" and not with the meaning of "consisting only of.

[0029] The term "user" as used herein refers to a human wearer, or other living beings such as pets and animals.

[0030] It will be appreciated that, if any prior art information is referred to herein, such reference constitutes no admission that the information forms part of the common general knowledge of the person skilled in the art in any country. BRIEF DESCRIPTION OF DRAWINGS

[0031] Embodiments of the present application will now be described, by way of example, and with reference to the accompanying drawings, in which:

[0032] Figure 1A The overall design of a wireless muscle activation detection system according to one example embodiment of the present application is shown.

[0033] Figure 1B The presence of an IZ position according to one example embodiment of the present application is shown.

[0034] Figure 1C The relationship between various parameters during waveform propagation is shown.

[0035] Figure 1D A linear electrode model and physical electrodes according to one example embodiment of the present application is shown.

[0036] Figure 1E A segment of a surface electromyography (sEMG) signal collected by a linear electrode according to one example embodiment of the present application is shown.

[0037] Figure 1FA scenario of a subject wearing linear electrodes is shown in accordance with one example embodiment of the application.

[0038] Figure 1G Processing steps for muscle activation detection using EMG signals on opposite sides of the IZ position are shown.

[0039] Figure 2 A switch control and force tracking implementation diagram is shown in accordance with one example embodiment of the application.

[0040] Figure 3 A idealized model of the neuromuscular junction (NMJ) and electrode placement is shown in accordance with one example embodiment of the application.

[0041] Figure 4 Phase and amplitude comparison outputs at different electrode distances are shown.

[0042] Figure 5 A sample of ultra-thin soft electrode design and fabrication is depicted in accordance with one example embodiment of the application.

[0043] Figure 6 A general diagram of a wireless sEMG and muscle activation detection sensor is depicted in accordance with one example embodiment of the application.

[0044] Figure 7 EMG signals and spectral plots collected using soft electrodes are depicted in accordance with one example embodiment of the application. Figure 8 A wireless sEMG sensor and a commercial wireless sEMG sensor are depicted in accordance with one example embodiment of the application.

[0045] Figure 9 Baseline noise of a wireless EMG sensor and a commercial sensor are depicted.

[0046] Figure 10 Collected sEMG signals and spectral plots are depicted.

[0047] Figure 11 WL, SSC, ZC, MAV, and MDF of EMG signals from two types of sensors are depicted.

[0048] Figure 12 Linear array electrode configuration schemes and collected signals are depicted.

[0049] Figure 13 sEMG signals collected on both sides of the IZ position are depicted.

[0050] Figure 14 Phase output of two-phase sEMG signal detection is depicted.

[0051] Figure 15 Dual-phase triggering compared to traditional methods is depicted. DETAILED DESCRIPTION

[0052] Muscle activation detection has a wide range of applications in the fields of sports and fitness, rehabilitation training, and medical diagnosis.

[0053] The present invention relates to a new type of sensor that can collect surface electromyography signals and detect muscle activation status through new technology. For example, the present invention particularly relates to a wireless muscle activation detection sensor.

[0054] In addition, in order to make the sensor more comfortable to wear, the inventors of the present invention have developed a thin soft electrode. Using a soft electrode for muscle activation detection in the above-mentioned fields will be more comfortable and flexible.

[0055] In order to integrate the sEMG sensor into a robot control system, a signal amplifier circuit needs to be developed. In addition, a trigger system based on the sensor also needs to be developed.

[0056] The overall design block diagram according to one example embodiment of the present invention is shown in Figure 1A Generally, the sensor 100 in the present invention can include the following components: a soft electrode 101, a front-end amplifier 102, a back-end filter circuit 103, a phase comparator 104, an AD converter, an MCU 105, a power module, a wireless radio frequency module 106, a controller area network (CAN) communication chip, and an external connection interface.

[0057] The functions of the parts are as follows: the soft electrode 101 is used to attach to the skin surface to collect surface electromyography (sEMG) signals; the front-end amplifier 102 is used to amplify weak signals to a measurable level; the back-end filter circuit 103 is used to remove power frequency noise and low frequency interference; the phase comparator 104 is used to detect the phase difference information of the surface electromyography; the AD converter is used to read data to the MCU 105, while the MCU 105 is the data processing and control unit; the power module supplies power to various circuit components; the controller area network (CAN) communication chip is used for communication with other controllers; and the external connection interface is used to expand other functions. The sensor 200 can be applied to fields that require muscle detection, such as robot automatic control, sports and fitness, medical diagnosis, and rehabilitation medicine, etc.

[0058] Electromyography signals are physiological signals sent from the nervous system to muscles and control muscle movement. This physiological signal can generate an electromotive force on the skin surface. Using high-precision sensors and equipment, this weak electromotive force can be read. In the present invention, the inventors have designed a complete system from the skin surface electrode to the signal generation and calculation circuit.

[0059] In one example embodiment, the present invention relates to a system for obtaining amplitude and phase difference from a sEMG sensor to understand the activation status of the muscle.

[0060] Muscle activation means that the muscle contracts and generates torque on the joint, thus driving limb movement. In the present invention, a sensor is provided that can detect the degree of muscle activation, which can output raw signals and RMS signals, thus obtaining the muscle activation time based on the raw signals with opposite characteristics. The principle of detecting muscle activation relies on the inverse phenomenon of the raw signal. The phase difference depends on the application of linear electrodes. Therefore, the activation state of the muscle can be indicated by the amplitude and phase difference.

[0061] The skeletal muscles of the human body receive movement instructions from the brain through neuromuscular junctions (NMJ), which are nerve impulses. Neuromuscular junctions (NMJ) are connected to a location in the muscle, Figure 1B The existence of the innervation zone (IZ) position is shown.

[0062] After receiving the signal through the neuromuscular junction, the muscle will reflect the IZ area on the skin surface. The electromyographic signal propagates from this area to both sides, starting from the IZ position and ending at the musculotendinous junction (MTJ). From one side of the IZ position, due to the propagation of the electromyographic signal, a phase difference can occur when the signal is detected at different locations. From both sides of the IZ position, the signals of the equivalent positions will exhibit opposite phenomena.

[0063] The detection of the electromyographic signal relies on differential electrodes. When using electrodes for sEMG signal acquisition, if the electrodes are placed at the IZ position, the sEMG signal collected will be too small due to the characteristics of the signal transmission from the IZ position to both sides. Different electrode placement positions will result in different signals. If the IZ position is considered as the signal source, there will be a phase difference when the electrodes are arranged on one side, while the signals will have opposite phases when they are symmetrically arranged on both sides.

[0064] In Figure 1B , ABCDEF are detection points. If the electrode method is differential AB and differential CD, then the two EMG signals obtained will have a phase difference. If it is the difference between AB and EF, then the two signals will be inverted. With this characteristic, the phase of the two signals can be used as the starting point of muscle contraction, thus achieving the detection of human movement intention.

[0065] Figure 1C The relationship between various parameters during waveform propagation is shown. In the case where the frequency, velocity and wavelength parameters remain unchanged, the phase difference is directly proportional to the distance. The propagation of the electromyographic signal in the muscle has a certain attenuation, but it can be ignored, so its waveform parameters will not change. Linear electrodes can be used to find the IZ position.

[0066] Figure 1D The design of linear electrode 110 and the physical image of electrode 110 are shown according to an example embodiment of the present application. A plurality of sEMG sensors 112 are provided on linear electrode 110, which are uniformly spaced from each other. Such linear electrode 110 can be used to find the IZ position of a skeletal muscle.

[0067] Figure 1E Surface electromyography (sEMG) signals collected using the electrode 110 are shown. In Figure 1E , it can be observed that the blue line next to the middle black line 123 and the red lines 121, 122 are opposite. In their individual graphs 124, 125, the spectral analysis shows that the two surface electromyography (sEMG) signals have the same frequency distribution. The middle position of the opposite signals on the linear electrode 110 is the IZ position.

[0068] Figure 1F The scenario of a subject using linear electrode 110 is shown. Linear electrode 110 is attached to the skin along the muscle direction, with each raised patch entering sEMG sensor 112. By differentiating the front and back patches, the EMG signal can be obtained. By using the linear electrode 110, a total of 12 signals corresponding to the phase difference between each two adjacent EMG sensors 112 can be obtained. From these signals, two opposite signals can be obtained, and then the operation process in Figure 1G can be performed.

[0069] Figure 1G The processing steps for muscle activation detection using the electromyography signals on the opposite sides of the IZ position are shown. After determining the IZ position described above, the two opposite signals are detected and transmitted to the mixer 132 to generate the mixed frequency signal. When the muscle is activated, the mixed signal will be negative, while when the muscle is not activated, the mixed signal will be positive. By utilizing this characteristic, zero-crossing analysis 134 is performed on the mixed frequency signal, the time and period of muscle activation can be obtained. The image reflects the process of processing the opposite signals. After mixing and zero-crossing comparison, the obtained switch level signal 136 can be used to represent the activation time of the muscle.

[0070] A wireless sEMG sensor suitable for wearable exoskeleton control is designed and evaluated. The sensor applies an analog-front-end (AFE) chip to convert the raw sEMG signal into a sampled sEMG signal. The integrated right leg driven (RLD) circuit is used to compensate for the power line noise of the sEMG signal. In addition, the sensor meets the requirements related to wearability, portability, size, ergonomics and power consumption.

[0071] The key features show that the signal from the low-cost sEMG system has no significant difference compared with the commercial sEMG system. However, most of the ADC chips used in these studies are the built-in ADC of the MCU, and in addition, there is a lack of compensation circuit for 50 Hz power line noise.

[0072] In order to use the two-phase sEMG signal to detect the intention of human limb movement, the first step is to verify the transmission of sEMG on the skin surface using a multi-channel sEMG sensor, resulting in a phase delay between the signals.

[0073] By using the AD8302 chip and EMG sensor circuit described above, phase detection and amplitude detection of the two-phase sEMG signal can be achieved. By using the phase difference of the two-phase sEMG for human motion intention detection, the MCU computing power required for raw signal processing is avoided, and it is characterized by the inevitable phenomenon in the muscle contraction process, thereby improving the accuracy of detection.

[0074] The phase comparison result of the two-phase sEMG signal can provide good conditions for the switch control of the wearable robot. The start and end of the signal, i.e. the start and end of muscle contraction, can be clearly reflected in the amplitude of the phase comparison. Therefore, by reading this signal with a microcontroller and performing real-time high-frequency detection, a robot start trigger signal can be obtained within milliseconds.

[0075] By calculating the obtained raw signal, the degree of muscle activation can be calculated, so that the wearable robot can follow this activation level and provide a certain force or torque, so that the resultant force between the human body and the wearable robot can reach the level of ADL action. The hardware implementation scheme of the trigger and activation detection system 200 is as shown in Figure 2 .

[0076] In one example embodiment, the trigger and activation detection system 200 includes a first sEMG sensor 202 and a second sEMG sensor 204, each configured to physically contact a skin surface of a user and receive electrode input 206 from the skin surface of the user. Each output measured by the first sEMG sensor 202 and the second sEMG sensor 204 is coupled into a single input and transmitted to a phase and amplitude discriminator 208 for further data processing. The phase and amplitude discriminator 208 can compare the phase difference between the sEMG signals captured by the first sEMG sensor 202 and the second sEMG sensor 204, as well as amplify the phase difference between the electromyographic signals to a measurable level, and generate a phase amplitude raw output 210, which can then further trigger an output and activation detection 212 to control a robot controller 214.

[0077] Advantageously, a precision power supply 220 is also provided for powering the first sEMG sensor 202, the second sEMG sensor 204, and the phase and amplitude discriminator 208.

[0078] The action potential model of the myoelectric signal can be simply represented as a triphasic shape, the expression of which can be represented as a second derivative of an exponential model. The expression V m (z) is as follows.

[0079] V m (z) = A(λ z ) 3 e -λz -B

[0080] The expression of the phase difference and the idealized model 300 are shown in Figure 3 The neuromuscular junction (NMJ) is idealized as a point 310 on a muscle fiber 302, and the electrodes are idealized as two points A, B on the muscle fiber 302 that are d AB AB apart. Based on the expression of the action potential, the phase difference when the signal is transmitted to the two points A, B can be obtained.

[0081]

[0082] According to the expression of the phase difference, as the distance dAB between the two electrodes increases, the phase difference will gradually increase, showing a positive correlation. To verify this hypothesis, an experiment was conducted using a phase comparator and a two-phase sEMG signal. The muscle tested was the tibialis anterior (TA). The distance between the electrodes was 18 mm, 55 mm, 75 mm, 105 mm, 125 mm, and 150 mm, respectively.

[0083] Table 1. Phase difference at different electrode distances

[0084] Distance First Second Third Mean Phase difference 18 mm 1.6023 1.5962 1.6481 1.6155 0.1845 55 mm 1.4622 1.4058 1.4470 1.4383 0.3617 75 mm 1.3908 1.3600 1.4371 1.3960 0.4040 105 mm 1.3580 1.3518 1.3511 1.3536 0.4464 125 mm 1.2251 1.1933 1.2779 1.2321 0.5679 150 mm 0.9117 0.9727 0.9547 0.9463 0.8357

[0085] The experimental results are shown in Figure 4 From the graph, it can be seen that the phase output result is positively correlated with the distance between the electrodes.

[0086] The sEMG sensor is first designed as a front-end detection circuit to realize the function of the application. It uses a high-precision instrument operational amplifier to detect and amplify weak myoelectric signals, and eliminates other physiological signals such as crosstalk and low-frequency electrocardiogram signals through the common-mode rejection characteristics of the precision operational amplifier. After the sEMG signal passes through the analog front-end, some low-frequency environmental noise such as 50Hz power frequency interference will still be mixed in. Therefore, the application designs an analog back-end filter, which mainly performs high-pass filtering based on the frequency distribution of the sEMG signal in the range of 80-350Hz, to obtain a relatively pure sEMG signal. Due to the phase difference of the myoelectric signal propagating on the muscle, the sEMG near the IZ position is used for real-time phase detection.

[0087] When the muscle is in a non-activated state, the phase difference between the two-phase sEMG is zero or remains unchanged. When the muscle is in an activated state, the phase difference between the two-phase sEMG is non-zero or changes. By taking advantage of this feature, it can accurately detect whether the muscle is in an activated state. At the same time, the analog front-end and back-end of the sEMG sensor designed can detect the activation degree of the muscle. Using an ADC chip can perform data acquisition, an MCU can perform data transmission and processing, an ESP32 microcontroller can perform wireless data transmission, and a CAN bus interface can perform reliable wired data transmission. The activation state of the muscle can provide a trigger for the robot, and the activation degree of the muscle can provide force tracking information for the robot. Thus, the problem of using sEMG to accurately and real-time control the robot is solved.

[0088] The design of the ultra-thin soft electrode mainly includes four parts: a TPU substrate, an Ag / AgCl material printed circuit, double-sided adhesive, and an electrode adapter plate. The design of each part and the physical photos are shown in Figure 5 .

[0089] The hardware design of the sensor mainly includes power management, front-end amplification circuit topology, back-end high-pass filter design, phase comparator topology, wireless module design, AD conversion chip, and MCU. All modules will be integrated on a PCB circuit board. The specific circuit design of each module is shown in Figure 6 .

[0090] In one example embodiment, the circuit design 600 of the sensor includes power management, which includes a power supply 601 that powers multiple modules. The front-end amplifier circuit topology here is an analog sEMG front-end sensor 602, and the back-end high-pass filter design is a high-pass filter 603. The phase comparator topology includes a phase detector 604. An AD conversion chip constitutes an AD converter 605, which is capable of data acquisition, and an MCU 606 is also provided, which is capable of data transmission and processing. The wireless module design includes an RF module 607, which is capable of wireless data transmission. In addition, a CAN bus 608 is also provided, which is capable of reliable wired data transmission.

[0091] The sEMG signal is collected using the soft electrode proposed in the present application, and the signal is subjected to frequency spectrum analysis. The collected data and frequency spectrum analysis are shown in the following figures. From Figure 7 It can be seen that the present application can effectively collect the electromyographic signal.

[0092] The wireless sEMG sensor 800 according to one example embodiment of the present application and the commercial wireless sEMG sensor 810 are shown in Figure 8 respectively. The baseline noise of the two sensors 800, 810 is shown in Figure 9 .

[0093] The signal of the sEMG sensor is collected from the muscle of the gastrocnemius lateralis (GL). From Figure 10 the frequency spectrum shown in the following figure, it can be seen that the frequency distribution of the sEMG signal collected from the GL muscle before digital filtering is mainly between 80-200 Hz, and the signal-to-noise ratio is about 21, which meets the sEMG signal requirements recommended by SENIAM.

[0094] The wireless sEMG sensor proposed in the present application is compared with the commercial EMG sensor. The parameters for comparison include VPP, VRMS, SC and ICC, as shown in Figure 11 . The comparison results are shown in the following table.

[0095] Table 2. VPP and VRMS of the wireless EMG sensor and the commercial EMG sensor

[0096]

[0097]

[0098] Table 3. Average SC and ICC between the wireless EMG sensor and the commercial EMG sensor

[0099] Wireless EMG - Commercial EMG SC 0.6156 ICC 0.7780

[0100] As Figure 12The linear array sEMG signals shown are obtained unilaterally at the IZ position of the TA muscle. If the electrodes are symmetrically arranged on both sides of the IZ position, the anti-phase phenomenon should be observed in channels 1 and 6, as Figure 13 shown.

[0101] By utilizing the phase difference of the two-phase sEMG signal for human motion intention detection, as Figure 14 shown, the MCU computing power required for raw signal processing is avoided, and it is characterized by the inevitable phenomenon in the muscle contraction process, thereby improving the accuracy of detection. The comparison between the two-phase sEMG triggered start 1500 and the traditional threshold-based start 1510 is as Figure 15 shown.

[0102] The existing sEMG sensor has the following disadvantages:

[0103] The first point is that the electrodes used to be pasted to the skin usually require button connections, which increases the overall thickness of the electrode. And the electrode has poor ductility, which may cause the electrode to fall off when the skin is significantly deformed.

[0104] The inventor proposes a thin and soft electrode that is more flexible and comfortable to wear on the skin surface when collecting sEMG signals to solve the first disadvantage.

[0105] In terms of soft electrodes, the inventor uses 0.2mm stretchable TPU material as the substrate and prints the electrode circuit on it to solve the electrode problem. By connecting a 0.16mm FPC to the printed circuit of the soft electrode, the thickness of the electrode itself and the button between the electrode and the wire is greatly reduced, so that the overall thickness reaches 300 microns. Due to the thin and soft characteristics of the entire electrode, it can be well fitted to the skin and follow the stretching of the skin.

[0106] The second point is that although existing sEMG sensors can collect sEMG signals, they do not have the function of judging muscle activation in real time. The existing method of determining muscle activation is based on single-channel thresholding, which is easily disturbed by noise and has low robustness. The activation time of the muscle usually needs to be post-processed based on the collected signal.

[0107] The sEMG triggered fast assist strategy for pneumatic back support exoskeleton involves using sEMG to control the robot movement. It needs to control the target robot is the exoskeleton with pneumatic back support. In order to achieve its fast assist strategy by sEMG trigger. However, its control still adopts the TKEO method which uses single channel sEMG to generate the control signal of the robot through TKEO operator and threshold method. In addition, its signal needs to be transmitted wirelessly to the host computer for decision making, which may cause significant delay. Compared with this reference, the method for detecting muscle initiation proposed in the invention has significant advantages in implementation and data transmission.

[0108] Teager-Kaiser energy operator (TKEO) of surface EMG improves muscle activity initiation detection. The proposed detection method is based on single channel sEMG signal. The proposed TKE operator still needs to use threshold to judge when identifying the initiation. The proposed method does not perform back-end signal processing, so it is difficult to achieve good results under the influence of noise. The algorithm proposed in the invention does not use hardware to realize muscle activation initiation detection. Based on the above points, the muscle initiation detection method proposed in this document is fundamentally different from the invention.

[0109] Regarding the problem of muscle activation detection using sEMG, the invention first designs an sEMG sensor as a front-end detection circuit to realize the function of the invention. It uses a high-precision instrument operational amplifier to detect and amplify weak myoelectric signals, and eliminates other physiological signals such as crosstalk and low-frequency electrocardiogram signals through the common-mode rejection characteristic of the precision operational amplifier. After the EMG signal passes through the analog front-end, some low-frequency environmental noise such as 50Hz power frequency interference will still be mixed in. Therefore, the invention designs an analog back-end filter, which mainly performs high-pass filtering based on the frequency distribution of sEMG signals in the range of 80-350Hz to obtain a relatively pure sEMG signal.

[0110] Due to the phase difference of myoelectric signals propagating on the muscle, two-phase sEMG is used for real-time phase detection. When the muscle is in a non-activated state, the phase difference between the two-phase sEMG is zero or remains unchanged. When the muscle is in an activated state, the phase difference between the two-phase sEMG is non-zero or changes. By taking advantage of this characteristic, it can accurately detect whether the muscle is in an activated state. Because two sEMG signals are used to determine the offset point of the muscle, common-mode noise interference with the myoelectric signal can be eliminated, thereby improving the robustness of the sensor.

[0111] Through the two-phase difference detection technology, the inventor solves the problem of muscle activation detection, which can realize real-time detection and transmission, and also has certain anti-noise ability. This method uses the phase difference of signals from two channels to realize muscle activation detection.

[0112] Thirdly, existing sensors are usually limited in terms of communication interface and data transmission, which affects customization applications.

[0113] The inventors propose a dual-mode sensor for wireless and wired communication, which has greater freedom in data transmission and storage to meet customization needs. The inventors use a dual-mode solution of wireless and wired to solve the problem of sensor data transmission and communication. Data acquisition can be performed using an ADC chip, data transmission and processing can be performed using an MCU, wireless data transmission can be performed using an ESP32 microcontroller, and reliable wired data transmission can be performed using a CAN bus interface. In this way, both the requirements of signal acquisition and storage and the requirements of communication with other controllers can be met.

[0114] The novel elements of the present invention include, but are not limited to:

[0115] A thin and soft electrode that uses screen printing to make circuits, FPCs, and wire connections, and is comfortable to wear. It can be stretched and has good contact with the skin.

[0116] Two sEMG sensors and a matching phase comparator are used. The phase comparator can output phase difference information of the two sEMG signals and be used for muscle activation detection.

[0117] The sensor has a wireless data transmission module, a CAN communication module, and a serial communication module. Having all three modules at the same time makes it more convenient to apply and expands its application scenarios.

[0118] The present invention relates to a novel device and method for real-time detection of muscle activation onset using phase-based surface electromyography (sEMG). The present invention is significantly different from conventional techniques that typically rely on changes in bioimpedance, muscle response to transcutaneous electrical nerve stimulation (TENS), or anatomical localization (such as uterine contractions or muscle fiber location).

[0119] In one embodiment, muscle onset detection is achieved through sEMG signal acquisition based on phase characteristics rather than amplitude or energy metrics. This method eliminates the need for bioimpedance differentiation or stimulation-induced response analysis used in existing solutions.

[0120] Advantageously, signal measurement is performed using a phase-based sEMG method, in which the phase difference between two physiological signals from different anatomical locations is calculated in real time. This is fundamentally different from existing technologies that rely on calculating signal amplitude changes or comparing signal amplitudes to fixed thresholds.

[0121] Furthermore, the present invention includes an analog hardware phase comparator circuit that performs an instantaneous analog comparison between the two-phase sEMG signals. This comparator is directly coupled to a trigger generation module, such as a zero-crossing detector, that produces a binary output indicating the onset of muscle activation.

[0122] Importantly, this system architecture omits digitization and complex computation, thereby minimizing latency and computational overhead. The analog implementation enables real-time response suitable for functional control applications.

[0123] The data processing method employed in the present invention also utilizes an analog circuit design, which is fundamentally different from the digital signal processing method or the TENS-based digital modulation technique in conventional systems.

[0124] The trigger circuit operates in a manner largely independent of threshold, detecting changes in signal state, such as, in particular, crossing of the baseline, rather than relying on a fixed amplitude threshold. This feature enhances robustness to signal noise and variation in contraction strength.

[0125] By discarding the "compute-then-compare-to-threshold" paradigm, the present invention introduces a new operational framework for muscle activation detection. This paradigm shift not only improves response speed but also enhances reliability and noise immunity.

[0126] Therefore, the present invention provides a novel and non-obvious solution to the problem of high-latency muscle activation detection, introducing a phase-driven, analog hardware-based method that redefines the state-of-the-art in neuromuscular signal processing and control.

[0127] Advantageously, the wireless sensor for muscle activation detection proposed by the inventors has great application potential in various scenarios of human-robot collaborative assistance. This method is particularly suitable for people who experience muscle atrophy due to aging and need assistance devices such as assistive robots. It is also suitable for patients who need rehabilitation training due to muscle injury, including but not limited to stroke patients, amputees, etc. When these patients need to provide assistance in strength training, the method of the present invention can be applied. In the field of sports and fitness, it can help trainers analyze the activation time of muscles and the activation time difference of several muscles that need to cooperate with each other, thereby better guiding training and improving performance.

[0128] Those skilled in the art will appreciate that numerous variations and / or modifications can be made to the present invention as shown in the particular embodiments without departing from the spirit or scope of the invention as broadly described. Accordingly, the present embodiments are meant to be taken only by way of example and not limitation.

[0129] Any reference to prior art contained herein is not to be taken as an admission that such information is common general knowledge.

[0130] Furthermore, embodiments can be described as processes depicted as flow diagrams, flow charts, structure diagrams, or block diagrams. Although a flow diagram can describe operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations can be re-arranged. A process is terminated when its operations are completed. A process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or the main function.

Claims

1. A muscle activation detection apparatus, characterized by, It comprises: a first sEMG (surface electromyography) sensor configured to receive a first sEMG signal associated with a user; a second sEMG sensor configured to receive a second sEMG signal associated with the user; and a processing unit configured to determine a muscle contraction of the user based on the first and second sEMG signals. It further comprises a phase comparator configured to compare a phase difference between the first and second sEMG signals to determine a real-time muscle contraction.

2. The muscle activation detection apparatus of claim 1, wherein, Wherein, 3. The muscle activation detection apparatus of claim 2, wherein, a muscle in an inactive state is represented by the sEMG signals detecting zero phase difference on both sides of IZ, while a muscle in an active state is represented by the sEMG signals detecting non-zero phase difference on both sides of IZ. Wherein, 4. The muscle activation detection apparatus of claim 2, wherein, a relative distance between the first and second sEMG sensors on a muscle fiber is positively correlated with a phase difference between the first and second sEMG signals. Wherein, 5. The muscle activation detection apparatus of claim 2, wherein, the processing unit is configured to send an output signal associated with a force or torque parameter of a robotic device based on the determined degree of muscle contraction. Wherein, 6. The muscle activation detection apparatus of claim 5, wherein, a switch control of the robotic device is determined based on a start and end of the determined muscle contraction. Wherein, 7. The muscle activation detection apparatus of claim 1, wherein, the sEMG sensor further comprises a soft electrode configured to physically contact a skin surface of the user. It further comprises an amplifier configured to amplify an electromyography signal to a measurable level.

8. The muscle activation detection apparatus of claim 1, wherein, It further comprises a high-pass filter configured to perform high-pass filtering on the sEMG signal.

9. The muscle activation detection apparatus of claim 1, wherein, It further comprises a wireless radio frequency module configured to control a remote robotic device.

10. The muscle activation detection apparatus of claim 1, wherein, It further comprises a CAN chip module configured to control a remote robotic device through a wired manner.

11. The muscle activation detection apparatus of claim 1, wherein, It further comprises a serial communication module configured to monitor data and transmit the data to a remote robotic device.

12. The muscle activation detection apparatus of claim 1, wherein, It further comprises a trigger generation unit in communication with the phase comparator signal, and the trigger generation unit is configured to trigger a robotic device based on an output from the phase comparator associated with a compared phase difference between the first and second sEMG signals.

13. The muscle activation detection apparatus of claim 2, wherein, Wherein, 14. The muscle activation detection apparatus of claim 13, wherein, the trigger generation unit further comprises a mixer configured to mix the first and second sEMG signals to generate a mixed signal. Wherein, 15. The muscle activation detection apparatus of claim 14, wherein, the trigger generation unit is configured to detect a change in the mixed signal corresponding to a start of muscle activation. Wherein, 16. The muscle activation detection apparatus of claim 15, wherein, the trigger generation unit is configured to generate a binary trigger signal in response to the detected change in the mixed signal. Wherein, 17. The muscle activation detection apparatus of claim 16, wherein, the binary trigger signal indicates a transition from an inactive muscle state to an active muscle state. Wherein, 18. The muscle activation detection apparatus of claim 17, wherein, the trigger generation unit is configured to perform a zero-crossing detection on the mixed signal to generate the binary trigger signal. It further comprises a linear electrode configured to detect an IZ position and connect a preamplifier with the sEMG sensor.

19. The muscle activation detection apparatus of claim 1, wherein, Wherein, 20. The muscle activation detection apparatus of claim 19, wherein, ​ The linear electrode further comprises a linear electrode array formed by the first sEMG sensor and the second sEMG sensor and is configured to be placed along a length of a muscle.

21. The muscle activation detection apparatus of claim 20, wherein, wherein, The linear electrode comprises a plurality of skin contact points and corresponding sEMG sensors spaced apart from each other at uniform distances.

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