Wearable bio-signaling device and system for personalized therapy feedback

Abstract

The present invention relates to a new apparatus, system and method for providing therapeutic feedback by using a bio-signal collection device comprising at least one sensor and a monitoring device, such as for example a computing device, which are preferably wirelessly communicatively connected to each other, for measuring, collecting and analyzing biological signals. In a preferred embodiment, the proposed system features wearable muscle sensing devices and user-friendly mobile biofeedback applications for improving the daily life of neurological and motor impaired individuals. The system may provide users with immediate and real-time feedback regarding exercise supplementation, muscle participation, and coordination, as well as a series of customized and therapeutic exercises to enhance their rehabilitation training.

Description

[0001] Cross - reference to Related Applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 377,417, filed on September 28, 2022, entitled "Biosignal Device for Individualized Therapeutic Feedback", the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] Embodiments of the present invention generally relate to biomedical sensors, and more particularly, to a biosignal detection garment for measuring bioelectrical signals such as electromyography, the biosignal detection garment being configured to monitor and / or treat a subject. Background Art

[0004] Nearly 8 million people in the United States suffer from stroke, Parkinson's disease, cerebral palsy, and spinal cord injuries, which often result in survivors having limited ability to control their muscles. Movement - related diseases are typically addressed through neuromuscular re - education therapy (NMRT), which has been shown to improve a person's quality of life. Current forms of treatment include the evaluation of electromyography (EMG) signals measured by multiple EMG needles / wires or electro - adhesive patches attached to the human body. More specifically, these electro - adhesive patches are adhered to different parts of the human body to sense EMG signals and are electrically connected to a monitor to analyze the signals for display. These signals are then interpreted by medical experts.

[0005] For increased convenience, these electro - adhesive patches have been integrated with wearable garments such that the electro - adhesive patches adhere to the garment and are in close contact with the human skin. However, the electro - adhesive patches will lose their adhesiveness or fold in such a way that physiological signals cannot be measured or are unreliable.

[0006] The prior art includes EMG systems that typically collect bio-signals through needles that invasively penetrate the skin and are inserted into muscles or conventional wet silver chloride (AgCl) electrodes placed on the muscles. However, these systems have many drawbacks. First, AgCl electrodes require the user to have a basic understanding of the musculoskeletal system to ensure accurate electrode placement and valid, reproducible, and repeatable signals. Second, these systems cannot provide meaningful insights into muscle co-activation or co-contraction that occur during task-based movements. Third, these systems cannot provide meaningful information and feedback to patients and everyday users because they are not designed for the target audience suffering from traumatic movement deficit-induced injuries.

[0007] In view of the foregoing, it would be advantageous to provide a wearable surface sensor or electrode that is easy to use, does not require traditional electroadhesive patches, and provides reliable results. Further advantageously, a wearable bio-signal device is provided that can collect signals and send signals to a computing device to interpret and display the signals in a manner that is meaningful and understandable to non-experts. Additionally, it would be beneficial for the displayed results and feedback to include user-specific customization and therapeutic exercises as well as rehabilitative muscle training. Summary of the Invention

[0008] This summary is provided to introduce concepts that will be further described in the detailed description below. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it to be construed as limiting the scope of the claimed subject matter.

[0009] The embodiments described herein relate to a novel apparatus, system, and method for providing therapeutic feedback by using a bio-signal collection device that includes at least one sensor and a monitoring device (e.g., a computing device), and the at least one sensor and the monitoring device are preferably communicatively connected to each other wirelessly for measuring, collecting, and analyzing bio-signals (e.g., electroencephalogram, electromyogram, and electrocardiogram). In a preferred embodiment, the proposed system features a wearable muscle sensing device and a user-friendly biofeedback application for improving the daily lives of neurologically impaired and movement-impaired individuals. The system can provide the user with immediate and real-time (or near real-time) movement supplementation, muscle engagement, and coordination feedback, as well as provide a series of customized therapeutic exercises to enhance their rehabilitation training. The embodiments described herein provide a system capable of encouraging compliance and participation. These components are used to mitigate key factors that may impede the progress of a rehabilitation program.

[0010] An object of the present invention is to provide a treatment system, which includes a wearable and intuitive bio-signal device and serves as a treatment system for users. The system may include at least one sensor or a plurality of sensors embedded in wearable textiles, whereby the sensors transmit the collected bio-signals to a communication terminal, where the signals are pre-processed and further transmitted to a computing device. The bio-signal data is transmitted to a communication terminal including circuit components, which may be encapsulated in an electronic housing on the wearable device, where the bio-signal data is processed and then transmitted to a computing device external to the wearable bio-signal device. The computing device includes a processor, which further processes the data received from the circuit components into user-readable information and displays the information for the user. Then, the displayed information is used to provide meaningful feedback, which can be understood by the user without the help or interpretation of a medical professional or a person trained in bio-signal reading. In addition, the computing device analyzes the data and provides a game based on treatment actions for the user to encourage the user to participate in the treatment and rehabilitation process.

[0011] In one embodiment, the system includes at least one reusable sensor worn by the user against the user's skin, whereby the sensor contacts the user's skin and measures and collects bio-signals from the user. The sensor wirelessly transmits the collected bio-signals to a computing device. The sensor does not require the use of tape or any other adhesive component to maintain a proper skin-sensor position connection. The sensor is integrated and / or embedded in or on a fabric or other textile-based material to form a wearable bio-signal device (also referred to herein as a "wearable device"). The sensors may be arranged in various combinations, including but not limited to monopolar, bipolar, or multiple such combinations. Such bio-signals may be measured with or without a reference / ground sensor. The bio-signal device may also include sensors capable of collecting information about the user's breathing rate and position and kinematic data (e.g., acceleration, velocity, and sensed force data).

[0012] In a preferred embodiment, the sensor can be used to collect electromyogram (EMG) signals and can be embedded in a material such as a textile fabric so that the sensor is fixed within the fabric while keeping the contact surface of the sensor sufficiently exposed from the fabric to contact the user's skin when placed on the user. In this embodiment, the wearable biosignal device can be generally summarized as including: at least one electromyogram (EMG) sensor, a communication terminal, and a computing device, where the at least one electromyogram sensor responds to muscle activity corresponding to a posture performed by a user of the wearable biosignal device and provides a signal in response thereto; the communication terminal includes circuit components communicatively connected to the sensor; and the computing device is communicatively connected to the communication terminal. The computing device stores processor-executable instructions that, when executed by the processor, cause the processor to identify, in real time or near real time, the posture performed by the user based at least in part on the signal provided by the sensor. Additionally, the computing device analyzes biometric data using the multiple computations and measurements described herein.

[0013] The wearable biosignal device includes a set of communication paths carried by the wearable device and a set of components carried by the wearable device. The set of components can include at least one sensor (e.g., an electrode or other contact sensor) and at least one communication terminal, where the communication terminal includes circuit components, and wherein the communication terminal is communicatively connected to the sensor via at least one of the set of communication paths. The communication terminal can include at least one of a wireless communication terminal and / or a wired communication terminal. The communication connection between the sensor and the communication terminal can be regulated by at least one additional component of the set of components carried by the device.

[0014] The term "communication" in "communication path", "communication connection", and variants such as "communicatively connected" is generally used to refer to any engineering arrangement for transmitting and / or exchanging information. Exemplary communication paths include, but are not limited to, conductive paths (e.g., conductive wires, conductive traces), magnetic paths (e.g., magnetic media), and / or optical paths (e.g., optical fibers), and exemplary communication connections include, but are not limited to, electrical connections, magnetic connections, and / or optical connections. Additionally, the term "communication connection" is generally used to include direct, 1:1 communication connections and indirect or mediated communication connections. For example, component A can be directly communicatively connected to component B via at least one communication path, or component A can be indirectly communicatively connected to component B via at least one first communication path that directly connects component A to component C and at least one second communication path that directly connects component C to component B. In this case, component C is considered to be the medium of the communication connection between component A and component B.

[0015] The textile portion of the wearable device can be made in the form of a fabric sheet that includes a first surface (or outer surface) wearable outwardly relative to the user's body and a second surface (or inner surface) wearable inwardly relative to the user's body. In one embodiment, the fabric portion can be circumferentially applied around a part of the user's body such that the contact surface of the sensor is exposed on the inner surface of the fabric sheet and placed directly on the skin to make sufficient contact with the skin for measuring and / or recording bio-signals. The wearable device can be secured around the user by fasteners such as hook-and-loop, snap buttons, etc. Alternatively, the wearable device and materials can initially be manufactured in the form of a band that can slide around a part of the user's body (e.g., an arm or a leg). In addition to measuring and collecting bio-signals generated by the user, the sensors can also transmit signals through the body. These signals can be used to evaluate skin impedance, whereby a high-frequency current passes through the body via a single sensor A+ and is received, for example, by any sensor passing through the body (e.g., A-, B+, B-, etc.) to determine whether the sensors are in sufficient contact with the skin or whether they have lost their conductive ability and need to be replaced.

[0016] After the wearable device is applied to the user's body, the sensors measure and collect bio-signals generated by the user or the user's environment and transmit the collected signals to a communication terminal, which can be contained in an electronic housing and, preferably, the electronic housing is incorporated into the wearable device by means such as inserting into a sleeve that can be fixed to the fabric, an elastic band that can be fixed around the housing, or other such attachment means. The communication terminal collects, amplifies, and / or filters the received bio-signals for further transmission to a computing device wirelessly or through a direct analog connection. This may include, but is not limited to, transmission via Bluetooth, Bluetooth Low Energy (BLE), or an auxiliary cable connection. Alternatively, digital signal processing can be performed within the computing device. The processed data is used to modulate a screen display, where the modulation is a direct result of the collected signals or the interaction of the signals. In another embodiment, positive feedback can be provided in the form of motivational feedback and encouragement. In yet another embodiment, personalized feedback determined by whole muscle communication and a health score will be provided, where (some) values are calculated by including a number of EMG-based biomarkers, which may include, but are not limited to, muscle ratios, inter-muscular coherence, or muscle strength / signal intensity. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The foregoing and the following detailed description will be better understood when read in conjunction with the accompanying drawings. For purposes of illustration, exemplary embodiments are shown in the drawings. However, the presently disclosed subject matter is not limited to the specific methods and means disclosed. These and other features, aspects, and advantages of the present invention will become better understood with reference to the following description, the appended claims, and the drawings, in which:

[0018] Figure 1 A perspective view of an embodiment of a wearable biosignal device worn on the front of a user's arm.

[0019] Figure 2 A perspective view of an embodiment of a wearable biosignal device worn on the back of a user's arm.

[0020] Figure 3 A top view of an exemplary arrangement of a sensor and its connection to a communication terminal.

[0021] Figure 4 A cross-sectional view of an embodiment of a sensor and its connection to a communication terminal embedded in a wearable part of a biosignal device.

[0022] Figure 5A A block diagram of an exemplary embodiment of a communication terminal circuit of a wearable biosignal device.

[0023] Figure 5B A block diagram of an exemplary embodiment of the detailed components and functions of a communication terminal circuit of a wearable biosignal device.

[0024] Figure 6 A flowchart of an exemplary embodiment of a general operating environment system.

[0025] Figure 7 A flowchart of an exemplary embodiment of a user interacting with a wearable biosignal device and system.

[0026] Figure 8 A graph of raw and processed electromyogram signals.

[0027] Figure 9 An exemplary embodiment of an operating environment is shown, where, after a user modulates a wearable biosignal device, a visual representation of data is presented on a computing device.

[0028] Figure 10 A block diagram depicting an embodiment of a computing device according to the subject matter described herein. Detailed Description

[0029] The following description and the accompanying drawings are illustrative and should not be construed as restrictive. Numerous specific details are described to provide a thorough understanding of the present disclosure. However, in some instances, well-known or conventional details are not described in order to avoid obscuring the description. References in this specification to "one embodiment" or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are they separate or alternative embodiments mutually exclusive of other embodiments. Furthermore, various features are described that may be exhibited by some embodiments and not by others. Similarly, various requirements are described that may be requirements of some embodiments but not of others.

[0030] The terms used in this specification generally have their ordinary meanings in the art, in the context of the present disclosure, and in the particular context in which each term is used. Certain terms used to describe the invention will be discussed below or elsewhere in the specification to provide additional guidance to the practitioner. It should be understood that the same thing can be expressed in more than one way.

[0031] Alternative language and synonyms may be used for any one or more of the terms discussed herein. Whether a term is elaborated or discussed herein has no particular significance. Synonyms for certain terms are provided. The recitation of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification (including examples of any of the terms discussed herein) is merely illustrative and is not intended to further limit the scope and meaning of the present disclosure or any exemplary term. Similarly, the present disclosure is not limited to the various embodiments given in this specification.

[0032] Figure 1 and Figure 2Front and rear views of an embodiment of a wearable biosignal device 100 circumferentially worn on a user's arm are shown, the wearable biosignal device 100 having a bipolar sensor 101 configuration for bipolar biosignal acquisition. It should be understood that the number of sensors 101 can vary according to the optimal number required for biosignal collection. In fact, the advantage of this system is the ability to incorporate multiple sensors 101 on a single wearable device 100, thereby allowing the simultaneous measurement and collection of a large number of biosignals. The sensors 101 can include any type or multiple types of contact sensors, including but not limited to one or more EMG sensors, one or more mechanomyography sensors, one or more acoustic myography sensors, one or more mechanomyography sensors, one or more electrocardiogram sensors, one or more blood pressure sensors, one or more thermometers, and / or one or more skin conductance sensors. The contact sensors can include any type of biosensor that responds to signals detected through physical contact with the user's skin. If desired, the wearable biosignal device 100 can also include one or more other forms of sensors 101, such as one or more pedometers, one or more inertial sensors (e.g., one or more accelerometers and / or one or more gyroscopes), and one or more altimeters, etc.

[0033] In Figure 1 it, the positive sensor 101 (shown as "+") and the negative sensor 101 (shown as "-") are embedded in a fabric or textile band 102 having an inner surface and an outer surface. The positive sensor 101 and the negative sensor 101 form a sensor connection or a sensor pair. For example, embedding in the fabric or textile band can be achieved by weaving the sensors 101 into the band 102. The textile band 102 can include a single piece of material (e.g., elastic material, flexible material, stretchable material, etc.) or multiple material parts adaptively connected together to allow limited movement of these parts relative to each other. The textile band 102 can be substantially planar when laid flat, but is typically curved in use. In addition, in addition to the band, the textile part 102 of the device 100 can be made into any suitable shape (e.g., a fitted shirt or tights), thereby allowing the simultaneous analysis of multiple muscle groups (in the case of electromyography).

[0034] From this perspective view, sensor 101 is embedded in a position such that the top surface of sensor 101 is visible on the outer surface of textile belt 102. It should be understood that the top surface of sensor 101 may or may not be visible on the outer surface of textile belt 102, as long as the contact surface of sensor 101 is exposed on the inner surface of textile belt 102 such that sufficient skin-sensor contact can be made by sensor 101 to measure and / or collect biometric signals. In this arrangement, positive sensor 101 and negative sensor 101 together form sensor 101, which is connected to a single muscle or muscle group on the front side of the user's arm, and sensor 101 is communicatively connected to electronic housing 103. Electronic housing 103 contains communication terminal 104, and communication terminal 104 includes circuit assembly 105, which is capable of receiving, processing the biometric signals measured or collected by the embedded sensor 101, and transmitting them to computing device 106. The exterior of electronic housing 103 may have control functions (including on / off power switching and Bluetooth pairing).

[0035] Figure 2 A rear view of the user's arm depicts an embodiment of biometric signal device 100 in the same assembled state as Figure 1 shown. Positive sensor 101 (shown as “+”) and negative sensor 101 (shown as “−”) are also communicatively connected to communication terminal 104 via wire interconnect 107 at the attachment points of electronic housing 103. Figure 2 The positive sensor 101 and negative sensor 101 shown form a sensor connection or sensor pair different from Figure 1 the positive sensor 101 and negative sensor 101 shown. Optionally, communication terminal 104 may be directly communicatively connected to sensor 101, whereby the biometric signals collected can be wirelessly transmitted from sensor 101 to communication terminal 104. The placement of wearable device 100 of the shown embodiment can evaluate two muscles (or muscle groups) via bipolar signal acquisition. The process of signal collection, transmission, and evaluation will be discussed in more detail herein with reference to additional figures.

[0036] Figure 3 An exemplary arrangement of sensor 101 and its connection to communication terminal 104 is shown, with communication terminal 104 enclosed within electronic housing 103. Sensor 101 may be associated with a positive or negative terminal and transmit the conducted biometric signals along wire interconnect 107 to circuit assembly 105 of communication terminal 104 encapsulated within electronic housing 103. Figure 4 A cross-sectional view of a portion of wearable device 100 is shown, which includes sensor 101, wire interconnect 107, and communication terminal 104 within electronic housing 103. Figure 4 Shows Figure 3Position relative to being embedded in the textile belt 102. In Figure 4 , the upper surface of the textile 102 is cut away so that the arrangement of the sensor 101 connected to the wire interconnect 107 and the connection of the wire interconnect 107 to the communication terminal 104 can be more easily shown. In this cross-section, the top surface of the sensor 101 can be seen through the upper surface of the textile belt 102, and the contact surface of the sensor 101 is located inside the textile belt 102 to contact the user's skin (not shown).

[0037] Figure 5A is a block diagram of an exemplary embodiment of the circuit component 105 of the communication terminal 104 of the biosignal device 100. It should be understood that different processing components and steps can be employed to implement signal processing and transmission, and the processes and components described herein illustrate the preferred embodiments. Figure 5A The exemplary circuit component 105 in is a circuit (e.g., electrical and / or electronic circuit) communicatively connected to the sensor 101 and can include various components depending on the specific implementation. The circuit component 105 includes an amplification circuit that amplifies the signal provided by the sensor 101, a filtering circuit that filters the signal provided by the sensor 101, an analog-to-digital converter that converts the analog signal provided by the sensor 101 into a digital signal, and a digital processor that processes the signal provided by the sensor 101. In this preferred embodiment, the ADC chip 108 amplifies and samples the biosignal in a single chip, and the microcontroller 109 further processes the signal to be transmitted to the computing device 106. The microcontroller 109 prepares the signal data to be wirelessly transmitted to the computing device 106 via the antenna 110 to store the processor-executable instructions, which, when executed by the digital processor, cause the digital processor to process the signal provided by the (multiple) sensors 101. In other embodiments, the circuit component 105 can include, but is not limited to: one or more power supplies 111, and / or one or more communication terminals 104 (e.g., one or more wireless transmitters and / or receivers (either alone or combined as a wireless transceiver) employing wireless communication protocols such as Bluetooth, WiFi TM and / or NFC TM ), one or more tethered connector ports (e.g., one or more universal serial bus (USB) ports, one or more mini-USB ports, one or more micro-USB ports, and / or one or more ports) and / or any other form of communication terminal 104.

[0038] Throughout the specification and the appended claims, the term "communication terminal" is generally used to refer to any physical structure that provides a communication link through which data signals can enter and / or leave the device or a component of the device (e.g., the wearable biosignal device 100 of the present invention). The communication terminal represents the end (or "terminus") of the communication signal transmission within the device (or a component of the device) and the start of the communication signal transmission to an external device (or a separate component of the device). In the case of the communication terminal 104 in the circuit assembly 105, the term "terminal" means that the communication terminal 104 in the circuit assembly 105 represents the end of the communication signal transmission within the wearable biosignal device 100 and the start of the communication signal transmission to other components of the biosignal device 100 and / or one or more devices separated from the wearable part of the device (e.g., the computing device 106, such as one or more smartphones, one or more desktop computers, laptops, or tablets).

[0039] Figure 5B shows Figure 5A A more detailed description of a preferred embodiment of the communication terminal 104 of the circuit assembly 105 is presented. The circuit assembly 105 includes a power source 111 such as a battery, a pre-filter module 112, two chip packages 108, 109, and an antenna 110. In this embodiment, the power source 111 is provided by a portable voltage source (e.g., a battery that can be connected to a buck-boost converter), thereby generating a stable and constant voltage source (e.g., 3.3V or 5V). The signal passes through the pre-filter 112, which consists of cooperating resistors and capacitors assembled in series and parallel, and wherein the signal is filtered according to an algorithm suitable for the type of diagnostic test or signal collection being performed. For example, a noise reduction algorithm can be applied to reduce noise and improve the signal-to-noise ratio. In addition, pattern recognition or other detection algorithms can be applied to the signal to optimize the signal frequency. These algorithms are well known to those skilled in the art. In this embodiment, the pre-filter 112 is a band-pass filter set to a frequency range of 0.05Hz to 20000Hz.

[0040] The analog-to-digital converter (ADC) chip 108 amplifies, samples, and converts the filtered analog signal into a digital signal. In current medical device amplifiers and ADCs, traditional methods typically involve separate components or multiple chip packages for signal acquisition, amplification, and digital conversion. In these traditional systems, the analog biological or medical signal from the sensor is transmitted to a series of amplifiers for signal amplification and then sent to a separate ADC for digital signal conversion. This process can introduce potential signal attenuation, noise interference, increased complexity, and a larger system size. The amplification method described herein overcomes the limitations of traditional systems and provides combined amplification and sampling analog-to-digital conversion within a single chip package, enabling direct amplification and sampling of analog biological or medical signals without the need for external components or interconnections.

[0041] The ADC chip 108 has built-in functions for controlling gain, sampling rate, error checking, etc. The amplification factor, sampling rate, etc. are defined by configuring the control register (or memory) of the ADC chip 108. Custom firmware is flashed onto the microprocessor that controls the ADC chip 108 to allow dynamic setting of the registers of the ADC chip 108 based on the needs of the application, thus allowing an increase in the gain, sampling rate, and other functions of the ACD chip 108. The driver is customized to steer and process data as it passes through the serial interface 116 of the ADC chip 108. These customized drivers communicate with the ADC chip 108, store the input samples into various packets, and transmit them via BLE without delay. The driver utilizes the nRF software development kit (SDK) library in the communication path on the serial peripheral interface (SPI), which is an interface bus for sending data between a microcontroller and a peripheral (in this case, the ADC chip 108). These drivers are used to regulate the clock speed, change the sampling rate, synchronize data, and ensure the consistency of the packetized data in the BLE transmission, regardless of the sampling rate and without delay. In addition, the driver supports power saving and sample error checking and is capable of compressing data before transmission.

[0042] The modulator 113 determines at what point in the time domain to sample the signal, samples the signal, and then converts the signal from an analog signal to a digital signal. The digital filter 114 and the decimator 115 filter to a frequency range of 0.05 Hz to 20000 Hz and reduce the number of sampled inputs to match the requested sampling rate or clock rate set by the microcontroller 109. For example, if data from the biosignal sensor 101 enters at a rate of 64 kHz and the rate read by the microcontroller 109 programmed at the serial interface 116 is 8 kHz, the data will be decimated accordingly. The phase shifter 117 and the digital filter 114 rearrange the frequency waves into a continuous data stream to eliminate any delay in the wavelet phase and can be programmed by the user on the computing device 106, which is determined by the capabilities of the user-side computing device 106. The gain and offset calibrator 118 amplifies the signal to reach the wireless signal transmission threshold and, if necessary, will perform a voltage offset to adjust the signal. At the serial interface 116, the preprocessed data is transmitted from the ADC chip 108 to the microcontroller 109 that includes two cores: the application core 119, which includes the BLE host 121, where sample packing 120 preferably occurs in 24 bits but can be reduced (e.g., 16 bits) for efficiency reasons; and the network core 122, which controls the wireless signal transmission radio hardware (e.g., the BLE controller 123), whereby the signal is wirelessly transmitted to the computing device 106 via the antenna 110. The computing device 106 performs advanced signal processing and calculations, such as intermuscular coherence, muscle activation rate, co-contraction index, target amplitude accuracy and precision, mean power frequency, mean amplitude, signal envelope, data averaging, Fourier transform, root mean square, signal burst, complementary slope, peak frequency, and smoothed signal. These methods will be discussed in further detail herein.

[0043] Depending on the type of medical test being performed (e.g., electroencephalogram (EEG), electromyogram (EMG), or electrocardiogram (EKG)), the physiological signal will have unique signal characteristics, and the signal processing steps can be adjusted for the specific signal being collected. The software on the computing device 106 allows for adjustment of the frequencies of interest, whereby the microcontroller 109 conveys the appropriate sampling rate determined by the desired frequency range to the decimator 115.

[0044] During EEG, the measurement of brain electrical activity is typically divided into 5 frequency bands: delta (δ, 0.5 - 4 Hz), theta (θ, 4 - 8 Hz), alpha (α, 8 - 13 Hz), beta (β, 13 - 30 Hz), and gamma (γ, 30 - 100 Hz). Each is associated with a unique body state, and time - frequency analysis, including wavelet transform or extraction of this information via FFT, is performed. The EEG voltage range is typically 0 - 200 μV, and power analysis can also be conducted in the voltage domain to explore brain activity in various processes.

[0045] EMG is the measurement of muscle electrical activity, and its processing functions are similar to the EEG data above. However, EMG has a wider frequency range, typically from 10 - 500 Hz. Although the device 100 described in this article preferably focuses on 0.5 - 500 Hz, the device 100 is capable of collecting frequencies from 0.001 - 64000 Hz. Similar to EEG, various time - frequency analyses or voltage domain analyses (power analysis) can be performed. The voltage range of EMG is typically between 0.05 mV and 10 mV. EMG analysis can calculate measurements that reflect fatigue, muscle communication, or healthy movement.

[0046] When measuring cardiac electrical activity in EKG, although the frequency is typically in the range of 0 - 20 Hz, different frequency analyses are not always necessary. Instead, it is better to focus on the characteristics of the waves, such as the P wave (or atrial depolarization), QRS complex (or ventricular depolarization), and T wave (or ventricular repolarization). Voltage domain measurements are more important, where the P wave is in the range of 0.1 - 0.25 mV, the QRS complex is 0.5 - 1.5 mV, and the T wave is 0.1 - 0.5 mV.

[0047] Figure 6 The flowchart shows a simplified overview of the system operating environment. The flowchart shows the data stream collected by the sensor 101 of the user attached to the wearable biosignal device 100. This data is transmitted to the communication terminal 104 for interpretation and signal processing, and then further transmitted to the computing device 106 for additional processing, display, and local user feedback. The computing device 106 can communicate on the open network 124 to exchange user information and push new interactive experiences to the end - user. Additionally, any data collected by the computing device 106 can be stored on the device 106 itself or in cloud storage for retrieval.

[0048] Figure 7A flowchart showing an embodiment of the workflow of the interaction between the computing device 106 and the bio-signal, wherein the sensor 101 of the wearable bio-signal device 100 is an EMG sensor for detecting and processing electrical signals generated by muscle activity. In this embodiment, the wearable device 100 employs an EMG sensor 101, which responds to the potential range (usually μV - mV) involved in muscle activity. This exemplary operating environment is characterized by a first step 701, whereby the user performs muscle-based action items prompted by the computing device 106 while wearing the bio-signal device 100. The wearable device 100 detects muscle activity corresponding to the body postures performed by the user. The wearable device 100 processes the EMG signals corresponding to the detected muscle activity. The performed postures or actions can be any movement of the body or joints, as well as isometric contractions of the body. Measurements can also be taken while the user is at rest to determine if involuntary muscle movements (e.g., muscle spasms) occur when the user is not making intentional movements. In Figure 7 In a second step 702, based on the performed actions, the bio-signal data collected and measured from the EMG sensor 101 (or sensor array 101) is transmitted from the communication terminal 104 of the wearable device 100 to the computing device 106. During conventional EMG, the bio-signals are converted into graphs or numbers that are typically read by medical practitioners or professionals trained in biometric readings. However, by presenting a visual representation 127 of user-friendly biometric data and by providing therapeutic exercises and interactive games as a form of rehabilitation, this therapeutic system overcomes some of the drawbacks of traditional EMG. As Figure 7 shown in a third step 703, the computing device 106 of the present wearable device 100 is configured to convert the signals received from the communication terminal 104 of the wearable device 100 into a visual representation 127 of information and feedback that can be interpreted by the user. The information and feedback can be displayed to the user in the form of on-screen text, audio, graphics, or any other means of illustrating the converted data. The visual representation 127 of the data communicated to the user can be qualitative or quantitative to indicate the health of the muscles and the motor neurons controlling them. For example, personalized scores can be assigned to specific muscles or muscle groups, which, when compared to a baseline or control group, indicate the health and function of that muscle group.

[0049] In addition, software can suggest target actions within the computing device 106, such as rehabilitation exercises or a series of exercises. In Figure 7In the fourth step 704 shown, the computing device 106 of the treatment system modulates the information presented to the user based on the updated user actions in response to newly acquired input signals. In other words, the target action items are updated relative to the previously completed actions, and new action item recommendations are transmitted to the user in real-time (or near real-time), thereby creating a feedback loop that provides timely and relevant information 127 reflecting the user's activities.

[0050] The biosignal data transmitted from the communication terminal 104 of the wearable device 100 to the computing device 106 is preprocessed raw EMG data 125, which is further processed and smoothed by the computing device 106 to display a more streamlined volume or signal 126 to the user. Figure 8 An example of raw EMG biosignal data 125a, 125b, 125c in the form of three independent raw EMG channels is shown. The three independent raw EMG channels can respectively represent signals collected from different sensors or sensor pairs. Once the signals are filtered, the processed signals 126 display smoother and more easily interpretable data. The computing device 106 using one or more hardware processors analyzes the amplitude and time frequency and performs a discrete Fourier transform on the input biosignals. The biosignals are also decomposed into different frequency bands of interest. In the case of EMG, the computing device 106 can calculate intermuscular coherence to analyze the incoming biosignals from multiple sensors 101 to determine the similarity of the signals and evaluate how individual muscles work together to perform a specific action. The muscle ratio is also calculated to analyze the agonist / antagonist interaction between specific muscles or muscle groups. For example, in Figure 8 it, a smaller amplitude corresponds to a moderate muscle squeeze, while a larger amplitude corresponds to a more forceful muscle squeeze.

[0051] The collected biosignals are compared with predefined parameters, such as predefined target frequency and voltage parameters and other biosignal characteristics. For example, if the user is instructed to squeeze a muscle to fifty percent of its maximum, the amplitude of the user's waveform can be compared with the target amplitude to identify the delta between the maximum and the amplitude the user can sustain. In another example, the computing device 106 can analyze the transmitted biosignal data to look for signs of muscle fatigue. Certain frequency characteristics are associated with muscle fatigue, and the computing device 106 can analyze the biosignal data for certain waveform offsets to determine whether the user fatigues earlier or later than the target value. If the user fatigues earlier or later than the predefined target value, the computing device 106 can encourage the user to relieve the muscle burden or reduce the muscle effort, thereby maintaining a more effective treatment regimen.

[0052] This treatment system can use multiple sensors 101 to evaluate individual muscles within a specific muscle group for analyzing muscle function and their interactions relative to the muscle group. For example, current methods of collecting EMG signals can employ a large sensor to evaluate a muscle group (e.g., the quadriceps). However, this system can incorporate four smaller sensors 101 on a wearable device 100, with each sensor 101 positioned to measure each of the four quadriceps muscles within the quadriceps group, thereby evaluating all four quadriceps muscles separately relative to each other within the quadriceps group. In this example, the bio-signals of the four muscles are sent simultaneously but separately in parallel. Each quadriceps will generate a unique waveform (as Figure 8 shown) that is transmitted from the sensor 101 to the communication terminal 104 of the wearable device 100. The computing device 106 receives the bio-signal data from the communication terminal 104 and runs independent component analysis (ICA) using an artificial intelligence algorithm that extracts and separates wavelengths to determine which electrode within the wearable device 100 the wavelength belongs to.

[0053] The processing method performed by the computing device 106 and described herein is used in the programming of a treatment game provided on the computing device 106 of the treatment system. Figure 9 is a graphical description of the operating environment of the treatment system, whereby a muscle rehabilitation game is displayed on the computing device 106. In other words, a visual representation 127 of the biometric data is presented to the user in the form of a game. After the user's muscle-based action, the computing device 106 displays an action consistent with the user's movement. For example, if the user is playing a lumberjack game on the computing device 106, the flexion of the biceps will cause the lumberjack to swing an axe. The harder the user squeezes their biceps, the faster the lumberjack swings the axe. The games provided by the system encourage the user to participate and comply with the rehabilitation treatment.

[0054] Figure 10 depicts a block diagram illustrating an embodiment of the computing device 106. Referring to Figure 10 , the computing device can be, for example, Figure 5A , Figure 5B and Figure 6The computing device 106 mentioned. The computing device 106 may include a network interface 1001, at least one processor 1002, a memory 1003, a display 1004, and a UI 1005. The memory 1003 may be partially integrated with the processor 1002. The UI 1005 may include a keyboard and a mouse. The display 1004 and the UI 1005 may provide any GUI in the embodiments of the present disclosure. The computing device 106 may be a mobile device (such as a smart phone or a smart tablet), and may also include a camera, a WAN radio, and a LAN radio. In some embodiments, the mobile device may be a laptop computer, a tablet computer, etc. The input signals described herein may be received through the network interface 1001, processed by one or more processors 1002, and displayed on the display 1004 via one or more UIs 1005, and the signals may be further stored in the memory 1003.

[0055] The description of various embodiments of the present invention is given for illustrative purposes, but is not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and scheme of the embodiments. The terms used herein are chosen to best explain the principles of the embodiments, the practical application, or the technical improvement of the technology found in the market, or to enable other ordinary skilled artisans in the art to understand the embodiments disclosed herein.

[0056] It should be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element, or there may be intervening elements. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements.

[0057] For ease of description, spatially relative terms such as "below", "beneath", "lower", "above", "upper", etc. may be used herein to describe the relationship of one element or feature to another element or feature shown in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. Throughout the specification, like reference numerals in the figures denote like elements.

[0058] Embodiments of the subject matter of the present invention are described herein with reference to plan views and perspective views, which are schematic diagrams of idealized embodiments of the subject matter of the present invention. Similarly, variations from the illustrated shapes are to be expected as a result of, for example, manufacturing techniques and / or tolerances. Accordingly, the subject matter of the present invention should not be construed as limited to the specific shapes of the objects shown herein, but should include, for example, shape deviations resulting from manufacturing. Thus, the objects shown in the figures are schematic in nature, and their shapes are not intended to show the actual shape of the device area and are not intended to limit the scope of the subject matter of the present invention.

[0059] The terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the subject matter of the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms "a," "an," and "the" are intended to include the plural forms as well. It should also be understood that the terms "comprises" and "comprising," when used in this specification, specify the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0060] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the subject matter of the present invention belongs. It should also be understood that the terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art, and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein. The term "plurality" as used herein refers to two or more of the referenced items. Although any methods, devices, and materials similar to or equivalent to those described herein may be used in the practice or testing of the presently disclosed subject matter, representative methods, devices, and materials are described herein.

[0061] In the drawings and the specification, typical preferred embodiments of the subject matter of the present invention have been disclosed, and although specific terms have been used, they are used only in a general and descriptive sense and not for purposes of limitation. The scope of the subject matter of the present invention is set forth in the appended claims.

[0062] All apparatus or steps and corresponding structures, materials, acts, and equivalents of the functional elements in the appended claims are intended to include any structure, material, or act for performing the function in conjunction with other claimed elements for which the particular function is claimed. The description of the invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or to limit the invention to the disclosed form. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.

Claims

1. A system for collecting, processing, and analyzing biometric data, the system comprising: at least one sensor configured to be placed on a measurement site of a user for measuring a biological signal of the user; a communication terminal communicatively connected to the at least one sensor for receiving and transmitting the measured biological signal, a computing device communicatively connected to the communication terminal and configured to receive the transmitted biological signal from the communication terminal, the computing device comprising: a memory; one or more hardware processors configured to: receive the transmitted biological signal from the communication terminal; process the biological signal into a visual representation of biometric data collected from the user to compare the biometric data with a predetermined parameter; and generate a visual representation of the biometric data.

2. The system according to claim 1, wherein, the at least one sensor is selected from the group consisting of: an electromyography sensor, an electrocardiogram sensor, an electroencephalogram sensor, a magnetomyography sensor, a mechanomyography sensor, a blood pressure sensor, a heart rate sensor, an accelerometer, and a gyroscope.

3. The system according to claim 1, wherein, the communication terminal further comprises a microprocessor and an analog-to-digital converter chip; and wherein firmware is flashed on the microprocessor to configure the analog-to-digital converter chip to amplify the biological signal and convert the biological signal from an analog signal to a digital signal in a single chip.

4. The system according to claim 1, wherein, the at least one sensor is fixed within a textile-based material having a first surface and a second surface; wherein the first surface is worn outwardly relative to the user's body and the second surface is worn inwardly relative to the user's body; and wherein the at least one sensor is fixed within the textile-based material along the same plane of the textile-based material such that a contact surface of the at least one sensor is aligned parallel to the second surface of the textile-based material to expose the contact surface substantially for contact with the user's skin.

5. The system according to claim 4, wherein, the textile-based material is substantially planar and is made into an annulus.

6. A system for collecting, processing, and analyzing biometric data, the system comprising: a plurality of surface electromyography sensors configured to be placed on a measurement site of a user for measuring a biological signal of the user; a communication terminal communicatively connected to the plurality of surface electromyography sensors for receiving and transmitting the measured biological signal such that the biological signals are sent separately and in parallel; a computing device communicatively connected to the communication terminal and configured to receive the transmitted biological signal from the communication terminal, the computing device comprising: a memory; one or more hardware processors configured to: receive the transmitted biological signal from the communication terminal; determine which one of the plurality of surface electromyography sensors each biological signal belongs to; Process the bio-signal into a visual representation of biometric data collected from the user to compare the biometric data with a predetermined parameter; and Generate a visual representation of the biometric data.

7. The system according to claim 6, wherein, The biometric data is collected in the time domain and the frequency domain and is used to perform a variety of calculations, including intermuscular coherence, muscle activation rate, co-contraction index, target amplitude precision and accuracy, mean power frequency, mean amplitude, signal envelope, data averaging, Fourier transform, root mean square, signal burst, complementary slope, peak frequency, and smoothed signal.

8. The system according to claim 7, wherein, The calculations are combined into a single representation of the user's progress during rehabilitation.

9. The system according to claim 6, wherein, The visual representation of the biometric data includes muscle rehabilitation training and recommended muscle movements, and the recommended muscle movements are updated in real time by the computing device based on the bio-signals received from the communication terminal.

10. The system according to claim 9, wherein, The bio-signals received from the communication terminal are collected in the time domain and the frequency domain and are used to perform a variety of calculations including intermuscular coherence, muscle activation rate, co-contraction index, target amplitude precision and accuracy, mean power frequency, mean amplitude, signal envelope, data averaging, Fourier transform, root mean square, signal burst, complementary slope, peak frequency, and smoothed signal.

11. The system according to claim 6, wherein, The plurality of surface electromyography sensors are fixed within a textile-based material having a first surface and a second surface, wherein the first surface is worn outwardly relative to the user and the second surface is worn inwardly relative to the user; and wherein the plurality of surface electromyography sensors are fixed within the textile-based material such that the contact surfaces of the plurality of surface electromyography sensors are aligned parallel to the second surface of the textile-based material such that the contact surfaces are substantially exposed for contact with the user's skin.

12. The system according to claim 11, wherein, The textile-based material is substantially planar and is formed into an annulus for collecting electromyography bio-signals.

13. A method for neuromuscular rehabilitation, comprising the steps of: Fix at least one sensor within a textile-based material, wherein the contact surface of the sensor is exposed from the textile-based material; Place the textile-based material on the user such that the contact surface of the sensor is in sufficient contact with the user's skin to collect and transmit bio-signals; Transmit the bio-signal to a communication terminal for processing to filter the bio-signal into a frequency band in the range of 0.05 Hz to 20000 Hz; Amplify the bio-signal and convert the bio-signal from an analog signal to a digital signal in a single chip package; Transmit the bio-signal from the communication terminal to a computing device; Compare the bio-signal data from the communication terminal with a predetermined parameter; Provide the user with information about the muscle function of the predetermined parameter; and Suggest rehabilitation treatment to the user, where the rehabilitation treatment can be updated based on the input biological signal data.

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