A muscle enhancement system with real-time feedback and intervention

Abstract

The present disclosure provides a NMES system (100) carrying at least one integrated sensor to sample and / or monitor information associated to acceleration and angular velocity exerted on the integrated sensor, attached adjacent or proximate to the body part of the muscle to be stimulated, in a three-dimensional environment. The integrated sensor continuously feed the sampled information to the system (100) for real-time dynamic adjustment of the amplitude and / or pulse of the NMES delivered to stimulate the muscle to achieve real-time adaptive and / or personal therapy.

Description

[0001] A MUSCLE ENHANCEMENT SYSTEM WITH REAL-TIME FEEDBACK AND INTERVENTION

[0002] Technical Field

[0003] The present disclosure relates to a system applicable for enhancement, rehabilitation and / or training of muscle of a subject through sending numerous neuromuscular electrical stimulations towards one or more muscles of the subject at a predetermined fashion. More specifically, the system improves the efficiency of the enhancement, rehabilitation and / or training by consistently detecting potential occurrence of over stimulation and / or fatigue, which may result in reduced therapeutic effectiveness.

[0004] Background

[0005] Sarcopenia or natural loss of muscle strength in the ageing process has caused a high rate of muscle related injury like falling and walking difficulties. This is not only a personal problem but also a burden to the family members and healthcare system. One of the many approaches to slow down or even reverse the progress of the muscle degeneration is associated with application of electrical stimulation to the muscle of interest throughout one or more therapeutic session of a predefined regimen.

[0006] Generally, there are few types of electrical stimulation, namely functional electrical stimulation (FES) and neuromuscular electrical stimulation (NMES). The purpose of FES is to stimulate the muscle to contract to perform a certain movement or action. FES works by giving out a shorter pulse -frequency stimulation and varied amplitude according to the movement going to be performed (eg: dorsiflexion or plantarflexion) while NMES is discharged at a longer pulse frequency and an amplitude that is as high as the subject can tolerate as the goal is to generate a contraction that is between 60% to 70% of their max voluntary contraction. NMES prompts the continual involuntary muscle contraction via external signal to prevent muscle disuse thus prevent further degeneration.

[0007] Further, Mechanomyogram (MMG) is a method complementary to the electrical stimulation to measure multimodal properties of muscle characteristics by detecting the vibration coming from the activated muscle. The multimodal muscle properties include strength, fatigue and balance. Due to its method of directly measuring the muscles activity, the MMG measurement has high signal to noise ratio enabling changes to be made between the therapeutic sessions or regimens to yield the desired outcome based on the measurement obtained. For instance, United States patent no. 9727139 discloses a closed loop FES system incorporated with means for dynamically mapping the electrode location on the body such that another means for confirming the position is enabled to disengage the electrode affecting the vital human parts. The disclosed system is also capable of adjusting the muscle stimulation signal in real-time in response to at least one characteristics of a detected muscle response.

[0008] NMES and MMG devices are typically large and non-portable in the clinical settings thus rendering conducting a training or therapeutic session at home by the patients has become almost impossible, while attending the training or therapeutic session at a weekly basis may not provide enough stimulus for effective muscle training. The infrequent use of the muscle through the training session may limit the ability to track meaningful changes in muscle activity. On the other hand, excessive muscle rehabilitation or training sessions may adversely lead to muscle injuries due to fatigue further aggravating the diseased state of Sarcopenia. Therefore, it is necessary to constantly monitor condition of one or more muscles undergoing rehabilitation and / or training to avoid potential muscle fatigue.

[0009] To overcome the aforesaid issues, development of a NMES and MMG integrated portable device capable of enabling home-based treatment for increasing the frequency of muscle stimulation yet preventing muscle fatigue to improve rehabilitation outcomes is greatly needed.

[0010] Summary

[0011] The present disclosure aims to provide a system to prevent muscle atrophy or loss in a human subject. Specifically, the disclosed system is incorporated with an electrode module or electrode assembly to provide a training electrical stimulation of a predetermined amplitude and / or frequency towards the muscle throughout a training session of a therapeutic regimen.

[0012] Further object of the present disclosure is directed to a closed-loop NMES system carrying at least one integrated sensor to sample and / or monitor information associated to acceleration and angular velocity exerted on the integrated sensor, attached adjacent or proximate to the body part of the muscle to be estimated, in a three-dimensional environment. The integrated sensor continuously feed the sampled information, preferably present and / or mapped in the form of MMG, the system for real-time dynamic adjustment of the amplitude and / or pulse of the NMES delivered to stimulate the muscle to achieve real-time adaptive and / or personal therapy.

[0013] Still, the present disclosure offers a NMES and MMG integrated system featuring real-time adaptive therapy yet avoiding injuries towards the stimulated muscle due to potential overstimulation or muscle fatigue. Particularly, the disclosed system establishes multiple thresholds corresponding to various levels of performance of the stimulated muscle within a training or therapy session. The training session becomes terminated or adjusted when at least one of the thresholds is crossed indicating corresponding degree of muscle fatigue.

[0014] More object of the present disclosure is to disclose a NMES and MMG integrated system, which is detachably wearable by the subject. In more specific, the entire or almost entire system of the present disclosure is wearable onto the subject and can be dissembled into few major components to facilitate storage after use.

[0015] Another object of the present disclosure relates to a NMES and MMG integrated system with improved usability through a mobile application. The disclosed system includes a mobile application to be installed to a user terminal, preferably a smart phone or the like, equipping the disclosed system with mobile connectivity for personalized therapy.

[0016] At least one of the preceding objects is met, in whole or in part, by the present disclosure, in which one of the embodiments of the present disclosure refers to a muscle enhancement system. The system essentially comprises an electrode module comprising an electrode assembly being configured to contact with a body part, underneath of which at least a portion of a muscle requiring enhancement located, to provide a training electrical stimulation of a predetermined amplitude and / or frequency towards the muscle prompting a series of muscle activities throughout a training session of a predetermined period; a sensor module having an integrated sensor being positioned onto the body part for deriving measurements associated to acceleration and angular velocity exerted on the integrated sensor in connection to the series of muscle activities within a three-dimensional environment at a given timeframe to generate corresponding training MMG readings, the integrated sensor comprising an accelerometer coupled to a gyroscope and magnetometer to form a 9-axis inertial measurement unit (IMU) sensor; a primary controller module being configured to retrieve the training MMG readings, convert the training MMG readings to corresponding training voltage signals using a mapping formula, process the converted training voltage signals, compute training root mean square (RMS) values of the processed training voltage signals, perform analysis towards the training RMS values and / or generate data associated to the analysis; a communication module operably connecting to the primary controller module to retrieve the data, training voltage signals and / or training RMS values from the controller module for relaying the data, training voltage signals and / or training RMS values to a remotely located user terminal having a user terminal interface; a power module comprising a battery electrically connecting to the various modules for powering operations of each module; a casing with a top, bottom, and sidewalls to define a hollow space in which the sensor module, the primary controller module, the communication module, and the power module are resided; a wearable strap attachable to the body part of a subject covering at least a portion of the muscle, the strap comprising a planar top surface and a planar bottom surface for respective mounting of the casing and the electrode module; a plurality of male mounting elements disposed on the top surface of the strap to detachably couple with a plurality of corresponding female mounting elements positioned at the bottom of the casing, coupling of the male and female mounting elements enables electrical communication between the primary controller module and the electrode module. Preferably, the user terminal is communicating with the controller module wirelessly and able to adjust the predetermined amplitude and / or frequency of the training electrical stimulation throughout the training session.

[0017] Preferably, the primary controller module is responsible for coordinating data collection, signal processing, and controlling the NMES output. It ensures real-time monitoring of muscle activity and adjusts stimulation based on processed information and data.

[0018] In some embodiments, the system further comprises a database connected to the primary controller to store training MMG readings, training voltage signals, training RMS values, and / or data of the training session.

[0019] In some embodiments, the system further comprises a control interface in electrical connection with the primary controller module forming at least part of the casing top to display at least one of the information, signals, values, or data. For some embodiments, the primary controller is configured to constantly check the computed training RMS value against a baseline RMS value and terminate the training session before end of the predetermined period upon detecting the computed training RMS value drops below a first threshold of the baseline RMS value.

[0020] For some embodiments, the primary controller is configured to constantly check the computed training RMS value against the baseline RMS value and reduce the predetermined amplitude and / or frequency of the training electrical stimulation upon detecting the training RMS value drops below a second threshold of the baseline value.

[0021] For some embodiments, the first threshold is greater than the second threshold.

[0022] Formore embodiments, the processing the converted voltage signals by the primary controller module comprises filtering the converted voltage signals using a high-pass filter, filtering the converted voltage signals using a Butterworth filter and performing a Fast Fourier Transform (FFT) analysis towards the filtered voltage signal.

[0023] For more embodiments, the training MMG readings comprises measured acceleration of the body part around a X-axis, a Y-axis and a Z-axis within the three-dimensional environment.

[0024] For several embodiments, the conversion of the training MMG readings to corresponding training voltage signals comprises (i) computing a total acceleration of the body part expressed in g-force based on a vector calculation formula as in:

[0025] R

[0026]

[0027] = yJ^accY + (Xacc)2+ (Zacc)2

[0028] where R is a measurement of the total acceleration, Xaccis a measured acceleration value along the X-axis by the integrated sensor, Yaccis a measured acceleration value along the Y-axis by the integrated sensor, Zaccis a measured acceleration value along the Z-axis by the integrated sensor; and (ii) converting the total acceleration into the corresponding voltage value using the mapping formula as in:

[0029] (R + 2g)

[0030] V = - - — x 5F

[0031]

[0032] where V is the training voltage value corresponding to R.

[0033] For several embodiments, the computing the root mean square (RMS) values of the processed voltage signals is performed using a formula as in

[0034] N

[0035]

[0036] Where Vi is the voltage value of each sampled information within a defined time window, and N is the number of samples over the time window.

[0037] For several embodiments, the primary controller module is configured to establish the baseline RMS value prior to providing the training electrical stimulation. Preferably, the baseline RMS value is established by way of (i) applying an initial electrical stimulation to the muscle according to an initial parameter for an initial voltage, initial frequency, initial pulse duration of the initial electrical stimulation; (ii) deriving an initial MMG readings from the integrated sensor for a predetermined duration; and (iii) computing the baseline RMS value using the initial MMG readings.

[0038] Accordingly, in more embodiments, the primary controller module adjustably increases or decreases the predetermined amplitude of the training electrical stimulation, the predetermined frequency of the training electrical stimulation, and / or the predetermined period of the training session based on the computed baseline RMS value. Preferably, the first threshold is 50 to 70% of the baseline RMS value in some embodiments while the second threshold is 30 to 60% of the baseline RMS value.

[0039] In more embodiments, the user terminal is a smart phone installed with an application to drive operation of the primary controller module using a wireless communication through the communication module. The user terminal may comprise a secondary controller module permitting further analysis of the or modification to be made towards the training session.

[0040] Brief Description Of The Drawings Fig. 1 illustrates a perspective explosive view of one embodiment of the disclosed system without the mobile application;

[0041] Fig. 2 illustrates architectural arrangement of one embodiment of the disclosed system;

[0042] Fig. 3 present a process flow adopted by one embodiment of the disclosed system to yield the desired outcome;

[0043] Fig. 4 shows arrangement of the integrated sensor and electrodes respectively in sampling measurement from and sending NMES to (a) rectus femoris and (b) extensor digitorum;

[0044] Fig. 5 are graphs showing pre-processed raw data view from the AcqKnowledge software used in the examples of the present disclosure;

[0045] Fig. 6 are graphs showing results of the examples in plot of peak amplitude, rms voltage and frequency change of three different quadriceps muscle points, namely (a) Vastus lateralis and (b) Vastus Medialis as well as (c) Rectus Femoris, across three measured time point;

[0046] Detailed Description

[0047] Hereinafter, the disclosure shall be described according to the preferred embodiments and by referring to the accompanying description and drawings. However, it is to be understood that referring the description to the preferred embodiments of the disclosure and to the drawings is merely to facilitate discussion of the various disclosed embodiments and it is envisioned that those skilled in the art may devise various modifications without departing from the scope of the appended claim.

[0048] As used herein, the phrase “in embodiments” means in some embodiments but not necessarily in all embodiments.

[0049] The directional term such as “top”, “bottom”, “parallel”, “side”, “perpendicular”, “distal” and “proximal” used throughout herein the specification generally refers to the relative direction of the described preferred embodiments with regard to the position of the clinician deploying the disclosed system onto a body part of a subject.

[0050] As used herein, the terms “approximately” or "about", in the context of concentrations of components, conditions, other measurement values, etc., means + / - 5% of the stated value, or + / - 4% of the stated value, or + / - 3% of the stated value, or + / - 2% of the stated value, or + / -1% of the stated value, or + / - 0.5% of the stated value, or + / - 0% of the stated value.

[0051] The phrase “muscle activity” used herein throughout of this specification shall refer to a sequence of events in repetitive muscle contraction generally includes excitation, excitationcontraction coupling, contraction and relaxation that at least one of such events give rise to a signal measurable by an integrated sensor of the disclosed system to generate corresponding MMG signal, measurement or data.

[0052] One aspect of the present disclosure refers to a muscle enhancement system (100). Referring to Fig. 1, the disclosed system (100) generally comprises an electrode module (110) comprising an electrode assembly being configured to contact with a body part, underneath of which at least a portion of a muscle requiring enhancement located, to provide a training electrical stimulation of a predetermined amplitude and / or frequency towards the muscle prompting a series of muscle activities throughout a training session of a predetermined period; a sensor module (120) having an integrated sensor being positioned on the body part for deriving measurements associated to acceleration and angular velocity exerted on the integrated sensor in connection to the series of muscle activities within a three-dimensional environment at a given timeframe to generate corresponding training MMG readings, the integrated sensor comprising an accelerometer coupled to a gyroscope and magnetometer to form a 9-axis inertial measurement unit (IMU) sensor; a primary controller module (130) being configured to retrieve the training MMG readings, convert the training MMG readings to corresponding training voltage signals using a mapping formula, process the converted training voltage signals, compute training RMS values of the processed training voltage signal, perform analysis towards the training RMS values and / or generate data associated to the analysis; a communication module (140) operably connecting to the primary controller module (130) to retrieve the analysis data, training voltage signals and / or training RMS value from the controller module for relaying the analysis data, training voltage signals and / or training RMS value to a remotely located user terminal having a terminal user interface; a power module (150) comprising a rechargeable battery electrically connecting to the various modules for powering operation of each module; a casing (160) having a top, a bottom and sidewalls to define a hollow space in which the sensor module (120), the primary controller module (130), the communication module (140) and the power module (150) are resided; a wearable strap (170) attachable to the body part of a subject covering at least a portion of the muscle, the strap (170) comprising a planar top surface and a planar bottom surface for respective mounting of the casing (160) and the electrode module (110); a plurality of male mounting elements (173) disposed on the top surface of the strap (170) to detachably couple with a plurality of corresponding female mounting elements (174) positioned at the bottom of the casing (160), coupling of the male and female mounting elements (174) enables electrical communication between the primary controller module (130) and the electrode module (110). Preferably, the user terminal is communicating with the controller module wirelessly and able to adjust the predetermined amplitude and / or frequency of the training electrical stimulation throughout the training session.

[0053] As stated in the foregoing, the sensor module (120) of the disclosed system (100) comprises an integrated sensor for deriving training MMG readings associated to acceleration and angular velocity exerted on the integrated sensor by the body movement, vibration and / or contraction of the stimulated muscle, throughout the training session, on which the sensor module (120) attached to. The MMG signal derived is subsequently used by the primary controller module ( 130) to analyse activities of the stimulated muscle. It was found by the inventors of the present disclosure that measurements derived from the integrated sensor solely, without the assistance of other sensor electrodes, is sufficient to compute the training MMG readings or measurement corresponding to mere muscle activities of the subject free from any significant interferences or noises relating to accidental body movement and shifts. More preferably, the disclosed system (100) has a preliminary filter employed to remove readings relating to unintentional body movement or motion. Particularly, the derived motion-filtered training MMG readings is subjected to further modification, calculation, process and / or conversion by the primary controller module (130) to generate the corresponding training RMS values being indicative of contraction strength and / or overall energy level of the stimulated muscle. Through the method employed by the primary controller module (130), the disclosed system (100) removes and filters noises, in the training MMG readings and the relevant voltage signals derived thereof, associated to any unintentional body shift as described hereinafter. Preferably, the integrated sensor is a 9-axis IMU created using at least a 3 -axis accelerometer, 3 -axis gyroscope, and 3-axis magnetometer for enabling precise motion and orientation tracking. The 3 -axis accelerator measures acceleration exerted on the integrated sensor in the three-dimensional space to track muscle contractions and overall movement during the training session. The 3-axis gyroscope is tasked with detection of angular velocity helping to determine muscle bulging or changes in orientation that occur during contractions. Further, the disclosed system (100) combines measurements derived from all three accelerometer, gyroscope, and magnetometer for comprehensive motion tracking useful for both muscle performance analysis and body movement monitoring.

[0054] Also, the electrode assembly may be fabricated from different materials in accordance with the various embodiments of the disclosed system (100). Preferably, the electrode assembly comprises at least a pair of hydrogel electrodes, which are hydrogel scaffolds embedded with a network of carbon nanotubes and graphene flakes. The hydrogel electrodes are highly flexible, durable, and biocompatible. The hydrogel materials also provide excellent adhesion to the skin ensuring consistent contact and even distribution of the stimulation currents. For some embodiments, the electrodes assembly may be a pair of metal discs or rods, with a layer of hydrogel preferably coated on or laminated thereto, that the metal surface is free from contacting the skin of the subject directly. The metal usable to produce the electrodes can be any one of stainless steel, gold, platinum, or Ag-AgCl. Alternatively, the electrode assembly can be made of silvered polyamide with Spandex forming a layer of conductive fabric to deliver the electrical stimulation. These electrodes are typically placed on the skin in close proximity to the muscles that are to be stimulated. The electrical current or impulses from the electrodes travels into the tissues and causes a muscle contraction. The size of the electrode can affect the comfort and effectiveness of the treatment. Larger electrodes are generally more comfortable and effective. In addition to stimulation delivery, the electrode assembly may be used to receive bioelectrical signals from the muscle for monitor the muscle's electrical activity during the training session. The information or data about the muscle’s electrical activity may be used in complementary to the MMG readings derived from the integrated sensor by the primary controller module (130) to acquire more precise activities about the stimulated muscles. Preferably, the training electrical stimulation delivered by the electrode assembly is in the form ofNMES. In more embodiment, the electrode module (110) may further comprise a H-bridge disposed in between the electrode assembly and the primary controller module (130). The presence of the H-bridge permits the primary controller module (130) to regulate and adjust the output of the electrical stimulation in terms of the predetermined period, frequency and / or amplitude based on the feedback from the integrated sensor and training RMS values analysis. More specifically, the H-bridge Modulates the intensity of the NMES output by adjusting the PWM duty cycle, offering control over voltage and current delivered to the muscle. Also, the H-bridge provides overcurrent and thermal protection to ensure safe stimulation and avoid system (100) damage during prolonged or high-intensity training sessions.

[0055] Pursuant to more preferred embodiments, the primary controller module (130) of the disclosed system (100) is a microprocessor or microcontroller possessing appropriate computing power to carry out the needed steps to calculate the RMS value, analysis as well as other data relating to the efficiency of the training session. The microcontroller or microprocessor is tasked to collects real-time MMG readings or measurements such as measured acceleration along the X-, Y- and Z-axis from the integrated sensor (accelerometer and gyroscope) throughout the training session. The microprocessor also runs the control logic to adjust the NMES parameters such as voltage, current, pulse and period based on the real-time muscle activity information derived and processed. It is possible to run multiple programs about the health being of the subject, relating to the training session or not, through the primary controller module ( 130). For instance, a program may be activated to measure calories used in the training session. Both circuit logic, firmware and / or the programs can be updated from time to time to further improve performance of the disclosed system (100).

[0056] For some embodiments, the disclosed system (100) may further comprise a database (190) or database module connecting to the primary controller to store the collected MMG readings, measurements, voltage signals, values and / or data of the training session according to each personal profile created in the database (190). Multiple databases (190) may be used in some of the embodiments. For example, the casing (160) houses a locale database (190) to store and keep the collected MMG readings, measurements, voltage signals, values and / or data onboard while a remotely connecting database (190) such as those located in a server facility is employed to receive and store the MMG readings, measurements, voltage signals, values and / or data once the disclosed system (100) becomes online. The database (190) also keeps relevant personal information of the subject undergoing the training session.

[0057] To facilitate wireless data transfer, the communication module (140) is preferably equipped with various submodules capable of performing communications under different protocols. The communication may communicate with the databases (190) and / or user terminals using any one of internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), file transfer protocol (FTP), real-time transport protocol (RTP), etc. The communication module (140) capitalizes on the connectivity of the cellular network too for file transfer with the remote user terminal or server in some of the embodiments. There are submodules in the communication module (140) enabling communications via cellular network protocols such as global system for mobile communication (GSM), code-division multiple access (CDMA), long term evolution (LTE), general packet radio service (GPRS), enhanced data rates for GSM evolution (EDGE), universal mobile telecommunications service (UMTS), high speed packet access (HSPA), worldwide interoperability for microwave access (WiMAX), etc. For example, the submodules may include at least any one of a Wi-Fi card, a Bluetooth transceiver, a Subscriber Identity Module card (SIM card) slot and relevant cellular transceiver, embedded SIM, etc. The different communication submodules facilitate data logging in the disclosed system (100) for the therapist to track progress over time and evaluate the therapeutic regimen. For example, the therapist can recall stored results and analysis data of any training sessions from the remote database (190) using both the primary controller module (130) and the user terminal to evaluate the therapeutic progress. Meanwhile, the therapist planning the training session for the therapeutic regimen is enabled to adjust remotely the NMES parameters (e.g., stimulation intensity, frequency, or pulse width) via a mobile application (or mobile app) installed in the user terminal, providing great flexibility and control over the therapy.

[0058] Accordingly, the user terminal can be any one of a smart phone, a personal computer, a laptop, a smart tablet or the like with compatible communication capability to establish a connection with the communication module (140) of the disclosed system (100) for data exchange. It is important to note that multiple user terminals of various types may be used to connect with the disclosed system (100). A program (for laptop or personal computer) or mobile application (for smartphone) of the disclosed system (100) is preferably installed at the user terminal to effectuate meaningful communication and analysis. The user terminal for the therapist to remotely monitor, assess, analyse, and / or adjust the therapeutic regimen including each of the training session based on the feedback received from the disclosed system (100). For more embodiments, the user terminal comprises a secondary controller module, which can be a mobile chip for a smart phone or a centre processing unit (CPU) for a laptop, to perform at least conversion of the MMG readings to the corresponding voltage signals of the given time frame using the mapping formula, processing the converted voltage signals, computing the training RMS values of the processed voltage signals, performing analysis and / or generating data associated to the performed analysis. Some of these tasks shall be conducted at the primary controller module (130), by default in other embodiments, but being offloaded to the secondary controller module in these embodiments to take advantage of the stronger computing power of the secondary controller module. The primary controller module (130) only prompts the communication module (140) to transmit the training MMG readings collected from the integrated sensor to the secondary controller module without substantially processing the readings in such embodiments. The secondary controller module in fact transmits the computed training voltage signals, training RMS values and / or analysis data back to the communication module (140) followed by presenting at least part of the computed training voltage signals, training RMS values and / or analysis data on the controller interface and storing at least part of the computed voltage signals, training RMS values and / or analysis data in the onboard database (190). The secondary controller module may even remotely adjust the parameters of the training session and / or therapeutic regimen with or without intervention from the therapist based on the logic or method embedded to the corresponding program or application installed at the user terminal.

[0059] For a number of embodiments, the power module (150) comprises a battery electrically connecting to the various modules for powering operation of each module residing in the casing (160). Preferably, the battery is rechargeable with a voltage ranging between 5 V to 15V providing portable power to the various modules of the disclosed system (100). To recharge the battery, the power module (150) comprises a charging port of, but not limited to, type-C or micro-USB interface. Alternatively, the power module (150) may comprise a wireless charging submodule coupled to the battery in addition to the charging port for some embodiments. Furthermore, the power module (150) preferably comprises a step-up voltage module in more embodiments to boost the input voltage up to 35V to 60V facilitating effective training electrical stimulation or NMES stimulation. The power module (150) also serves to constantly regulate voltage supply in the disclosed system (100) ensuring consistent performance.

[0060] In order to protect various modules of the disclosed system (100), the external casing (160) or housing is preferably made of mechanically strong yet lightweight materials such as metal alloy or thermostable plastic. The metal alloy can be stainless steel, reinforced aluminium, carbon fibre, magnesium alloy, etc. The thermostable or high performance plastic can be polyethylene terephthalate), polyether ether ketone, polyphenylene sulfide, polysulfones,, etc. The casing (160) can be, but not necessary, rectangular or circular in shape with the lengthiest measurement preferably less than 10cm, more preferably less than 6cm, such that the casing (160) can be entirely resting on and supported by the body part which the casing (160) attached on. Preferably, the casing (160) is dimensioned to about 35-60mm x 35-60mm x 10-25mm in size when it is in a cuboidal shape. The interior of the casing (160) may possess a number of lugs, threaded body, or ribs to secure positioning of the various modules within the defined hollow space with the help of other compatible fastening means like screw. The casing (160) may be carved with at least one outer slot for a power input interface such as type-C or micro-USB port to recharge the battery for several embodiment. Information and / or data about the therapeutic session can be download from the onboard database (190) to a computing device via the type-C or micro-USB port too. Other connection interfaces such as SD card slot or SIM card slot may be fabricated on the casing (160) as well.

[0061] According to some preferred embodiments, the disclosed system (100) further comprises a control interface in electrical connection with the primary controller module (130) forming at least part of the top of the casing (160) to display at least part of the training MMG readings collected, training voltage signals computed, the training RMS values and / or analysis data. More specifically, the control interface is touch enabled to receive user input including adjustment of the parameters of the training session besides presenting the training MMG readings collected, training voltage signals computed, training RMS values and analysis data. The primary controller module may run a full or modified version of the program found at the user terminal carrying the needed coding to decide the manner in which at least part of the training MMG readings, training voltage signals, training RMS values and / or analysis data being shown and to be interacted by the user thereto. For a number of the embodiments, a wearable strap (170) or pad capable of being detachably fastened onto the body part is preferably used in the disclosed system (100) for adherence or mounting of the electrode module (110) and the casing (160) along with the residing modules. Preferably, the strap (170) is made of, but not limited to, stretchy breathable fabric such as Lycra, Spandex or Elastane. Other fabric types may be used in addition of the stretchy fabric for comfortability. In some embodiments, the strap (170) comprises VELCRO patches or the like fasteners for adjustably wearing of the strap (170) onto the body part. Other extension fabric maybe employed in the some of the embodiments so that the strap (170) can be fitly placed on different body parts. Optionally, different size and shape of strap ( 170)s may be used interchangeably to fit different body parts in some embodiments. Preferably, the wearable strap (170) or pad is of a dimension around 80-200mm x 30-90mm.

[0062] To electrically connect the primary controller module (130) to the electrode module (110) across the strap (170), the disclosed system (100) uses a plurality of male mounting elements (173) disposed on the top surface of the strap (170) to detachably couple with a plurality of corresponding female mounting elements (174) positioned at the bottom of the casing (160). The male and female mounting elements (174) are conductive ly connecting to the electrode module (110) and the primary controller module (130) respectively. Both male and female mounting elements (174) are made of electric conductive material preferably imbued with good or strong magnetized characteristics that both are naturally attracted to one another when being brought to proximity. The magnetized properties facilitate the casing (160) and the residing modules to be detachably mounted atop of the planar top surface of the strap (170). As shown in Fig. 1, the male and female mounting elements (174) may be a plurality of metal discs, each being dimensioned into a circular form, and disposed in a substantially symmetric or equidistant fashion. Nonetheless, other fasteners able to realize strong yet removable mounting of the casing (160) may be utilized as well in other embodiments of the disclose system (100).

[0063] For better distinguishing the actual muscle contraction caused by the stimulation from the unintentional movement of the body part, it is important to get rid of the noise or artifacts present in the training MMG readings and / or training voltage signals derived thereof. The training MMG readings comprises the measured or detected acceleration of the body part around a X-axis, a Y-axis and a Z-axis within the three-dimensional environment. Prior to removing the noise and / or artifacts from the measurements in the training MMG readings, it is essential for the primary controller module (130) to convert the training MMG readings to corresponding training voltage signals using a mapping formula as mentioned above. Particularly, the conversion of the training MMG signals to a corresponding training voltage values comprises (i) computing a total acceleration of the body part expressed in g-force based on a vector calculation formula or Formula I as in:

[0064] R

[0065]

[0066] = √(Xacc)2+ (Yacc)2+ (Zacc)2

[0067] where R is a measurement of the total acceleration, Xaccis a measured acceleration value along the X-axis by the integrated sensor, Yaccis a measured acceleration value along the Y-axis by the integrated sensor, Zaccis a measured acceleration value along the Z-axis by the integrated sensor;

[0068] and (ii) converting the total acceleration into the corresponding training voltage values using the mapping formula or Formula II as in:

[0069] (R + 2g)

[0070] V = x 5F

[0071] 4#

[0072] where V is the training voltage value corresponding to R.

[0073] After the conversion, the disclosed system (100) has the primary controller module (130) to further process the obtained training voltage signals to filter noise and smooth out the voltage signals prior to computing the training RMS value as illustrated in the flowchart of Fig. 3. In more specific, the processing of the converted training voltage signals by the primary controller module comprises filtering the converted training voltage signals using a high-pass filter, filtering the converted training voltage signals using a Butterworth filter and performing a Fast Fourier Transform analysis towards the filtered training voltage signals. The high-pass filter is dedicated for removing low-frequency drifts and baseline artifacts while the Butterworth filter is directed to smooth out the voltage signals after passing through the high-pass filter. Preferably, the high-pass filter removes noises under 4 to 7Hz and the filter is a 4th-order Butterworth filter. The primary controller module (130) applies the FFT analysis to filtered voltage signals to convert it from a time domain data to a frequency domain data. This transformation helps to identify the dominant frequencies present in the muscle signals or MMG readings, which is preferably focusing solely on frequencies up to 150-200 Hz. A moving average is subsequently calculated over the frequency domain data to smooth short- term fluctuations. It is important to note that at least one of the high-pass filter and Butterworth filter may be additionally disposed immediately downstream of the integrated sensor to filter or clean noises in the MMG signal with respect to unintentional body motion or movement of the subject in some embodiments of the disclosed system.

[0074] With reference to Fig. 3, the primary controller module (130) subsequently advances to compute or calculate the training RMS values, which are critical for the disclosed system (100) to monitor contraction strength or overall energy level of the stimulated muscle during the training session. The primary controller module (130) computes the RMS values of the processed voltage signals using a Formula III as in

[0075] RMS =

[0076]

[0077] where Vi is the voltage value of each sampled information within a defined time window, and N is the number of samples over the time window. The computed training RMS values are indicator of the strength or overall energy level of the stimulated muscle during the training session. The changes of the training RMS values within the time window are crucial for the disclosed system (100) to detect early signs of muscle fatigue. In more specific, the primary controller is configured to constantly check the computed RMS value against a baseline RMS value and terminate the training session before end of the predetermined period upon detecting the computed RMS value drops below a first threshold of the baseline RMS value. Furthermore, the primary controller is configured to constantly check the computed RMS value against the baseline RMS value and reduce the predetermined amplitude and / or frequency of the training electrical stimulation upon detecting the computed RMS value drops below a second threshold of the baseline value. More preferably, the first threshold is greater than the second threshold.

[0078] It is clear that the primary controller module (130) uses the baseline RMS value as a reference point to gauge fatigue level of the stimulated muscle during the training session. For a number of embodiments, the primary controller module (130) is configured to establish the baseline RMS value prior to providing the training electrical stimulation. Preferably, the baseline RMS value is established by way of (i) applying an initial electrical stimulation using an initial NMES parameter to the muscle according to an initial parameter for an initial voltage, initial frequency, initial pulse duration of the initial electrical stimulation; (ii) deriving initial MMG readings from the integrated sensor for a predetermined duration; and (iii) computing the baseline RMS value using the initial MMG readings. To compute the baseline RMS value, the calculation or computing steps using Formula I to III, as described in the foregoing, shall be performed using the initial MMG readings instead of the training MMG readings. Sequentially, the primary controller module (130) adjustably increases or decreases the predetermined amplitude of the training electrical stimulation, the predetermined frequency of the training electrical stimulation, and / or the predetermined period of the training session based on the computed baseline RMS value with reference to the logic circuit embedded in the coding of the program, application or firmware driving its operation. Preferably, the first threshold is 50 to 70% of the baseline RMS value in several embodiments. Similarly, the second threshold is 30 to 60% of the baseline RMS value for more embodiments.

[0079] According to the flow of one embodiment of the training session of the disclosed system ( 100), the initial NMES parameter or the initial electrical stimulation is set for the electrode module (110) at an initial voltage of 35V with a frequency of 100Hz and pulse duration of 200ps at a current of 30mA for 40 seconds with rest time being 15 seconds between stimulation cycles. Meanwhile the initial baseline calculation time window is 3 seconds at a sampling rate of 1000Hz at the integrated sensor. After initial parameters have been set for the electrode module (110) and the integrated sensor, the disclosed system (100) begins the initial electrical stimulation towards the target muscle. During the initial electrical stimulation, the primary controller module (130) continuously obtains MMG readings using 3-axis accelerometers (linear motion), continuously monitor rotational movement using the gyroscope (angular motion), and calculate angular velocity in real-time for all three axes (yaw, pitch, roll). It is worthy to note that the contraction of the muscle triggered by the initial causes the rapid or pronounced angular movement, which the gyroscope registers as an increase in angular velocity and further translates into bulkiness of the stimulated muscle, as bulkier muscles typically generate more force during contraction and that force often translates to faster or more controlled rotational movement. The primary controller module (130) then computes baseline RMS value using the MMG readings of the first three seconds of the initial electrical stimulation. Additionally, the primary controller module (130) also computes the average gyroscope angular velocity during the first 3 seconds to establish a body part movement baseline. After completion of the initial electrical stimulation, the training session proceeds further to the application of the training electrical stimulation that the primary controller module (130) continuously measures the linear motion and angular velocity using the integrated sensor for acquiring the training MMG readings. The primary controller module (130) may apply adaptive filter based on accelerometer & gyroscope data. For example, the primary controller module (130) increases the motion filter cutoff frequency to suppress the movement artifacts when the linear motion or angular velocity reaches a predetermined limit. Optionally, the primary controller module (130) reduces the motion filter by allowing a full MMG signal capture when no significant movement were detected. To realize the real-time monitoring and dynamic adjustment of the training session, the primary controller module (130) is configured to compute a current training RMS value based on a current MMG reading at a rate of 1 second and check the current training RMS value against the first threshold and the second threshold respectively corresponding to 70% and 60% of the baseline RMS value. When the current training RMS value reaches the second threshold, the primary controller module (130) reduces the training electrical stimulation by 5 V and adjusts the pulse duration to 150 ps, In case the current training RMS value reaches the first threshold, the primary controller module (130) terminates the training session immediately.

[0080] The following example is intended to further illustrate the disclosure, without any intent for the disclosure to be limited to the specific embodiments described therein.

[0081] Example 1

[0082] A total of 4 samples with age >55 years old were being recruited for this pre-piloting phase after verbally consented for intervention and data collection. The samples were selected using the inclusion and exclusion criteria detailed in table 1. Basic health information include weight, height, smoking status, alcohol intake and medication were collected. Exercise status and nutritional status were being assessed using International Physical Activity Questionnaire (IPAQ) and mini nutritional assessment (MNA). Table 1 below lists the inclusion and exclusion criteria for sample recruitment. Inclusion

[0083] • No mobility Issue

[0084] • No severe muscle deterioration issue

[0085] Exclusion

[0086] • Using pacemaker or any electronics inside of the body

[0087] • Bedridden

[0088] • With memory or cognitive problems, affectingthe ability to receive and adhere to investigators’ instructions.

[0089] • Physical limitation preventing training (e.g., missing limb or having

[0090]

[0091] severe musculoskeletal disorder)

[0092] Table 1

[0093] The participants were asked to carry out self-monitored NMES session and exercises in alternate days where the participants will use NMES of the disclosed system without exercise for a day, the next day will be just exercise without NMES and the cycle repeat. For NMES sessions, it was performed at 2 sites (forearm and quadriceps muscle) for 15 minutes per sites. The exercise regimen is designed to target both upper body and lower body. For upper body, the exercises are hand-grip exercise and resistance band exercise while lower body exercises are leg raise and bridge, side-lying hip abduction, standing hip abduction and extension, squat with heel raise, sitting band exercise and sit to stand exercise. Each exercise was repeated for 15 times for 2 rounds.

[0094] Example 2

[0095] Heart rate and blood pressure were being measured using OMRON HEM-7156 blood pressure monitor. Hand grip strength was measured using a dynamometer (CAMRY) for average of three readings. Mechanomyogram readings using BIOPAC MMG MPU150 on four muscle point, namely vastus lateralis, vastus medialis, rectus femoris and extensor digitorium of dominant limb were measured with the setting of 1000 samples per second. The data were collected at 3 time points for the pre-intervention stage, mid-intervention staged postintervention stage with 2 weeks gap. During the measurement, the participants were asked to relax their muscle and fully contract their muscle for 4 seconds to obtain the baseline and fullcontraction signal. The raw data were collected and read using AcqKnowledge software (Fig.

[0096] 5). Upon acquisition, the data undergoes preprocessing to prepare it for analysis. This includes synchronizing time vectors to ensure consistent sampling rates across different datasets. A high-pass filter with a cutoff frequency of 5 Hz is applied to remove low-frequency noise and baseline drift also known as Artifact Movement, which may distort the analysis. The filter design utilizes a 4th-order Butterworth filter, implemented using the ‘butter’ and ‘filtfilt’ functions in MATLAB. This step cleans the data, allowing for more accurate subsequent analysis.

[0097] After cleaning, the data undergoes detailed analysis, starting with the calculation of RMS voltage value over time using a sliding window approach. This method, involving the definition of a specific window size and step size, captures variations in muscle activity levels. Calculating RMS is to reduce signal noises and quantify muscle activation. The Fast Fourier Transform (FFT) is then applied to the filtered data to convert it from the time domain to the frequency domain. This transformation helps identify the dominant frequencies present in the muscle signals, focusing on frequencies up to 150 Hz. A moving average is subsequently calculated over the frequency domain data to smooth short-term fluctuations and highlight underlying trends, aiding in understanding changes in muscle activity patterns over the course of the experiment.

[0098] A comprehensive statistical analysis was conducted to evaluate the presence of significant differences across various trial phases: Pre-Trial, During Trial, and Post-Trial. Initially, the Shapiro-Wilk test confirmed the normality of the data. Following this, paired t-tests were conducted to compare differences between post-intervention and pre-intervention phases, postintervention and mid-intervention phases, and mid-intervention and pre- intervention phases. The parameters analysed included hand grip strength, maximum absolute voltage, RMS voltage, and peak frequency. The comparison of maximum absolute voltage values in the filtered data aimed to assess changes in muscle response intensity. RMS voltage values were compared to evaluate changes in muscle activation and endurance over time, while the comparison of peak frequencies, obtained from frequency domain analysis, aimed to observe shifts in dominant frequency components, potentially indicating changes in muscle health or function. This approach provided a comprehensive evaluation of muscle response, activation, endurance, and frequency shifts across different trial phases. The statistical processing was done using inbuilt function in R version 4.4.1. Example 3

[0099] The demographic is summarized in Table 2. A total of four Chinese participants were recruited with age range from 57-63 years old where one of them is obese, another one is overweight, and the rest are in normal BMI range. None of the participants smoke and most of them does not take alcohol or low intake of alcohol. Two of them have moderate physical activity while another two have high activity assessed using IPAQ questionnaire and the MNA show all of them have normal nutritional status. Sample 1 and Sample 2 have slightly high BMI, which means they have slightly higher subcutaneous fat that may impede the current diffusion of NMES to the targeted muscle (Maffiuletti, 2010). Other than that, the alcohol intake in sample 2 may affect the muscle growth by reducing muscle protein synthesis (Murphy, Snape, Minett, Skein, & Duffield, 2013). However, these assessments are based on self-reported where results may not be fully representing the real status of the participants.

[0100] Name Sample I Sample 2 Sample 3 Sample 4 Race Chinese Chinese Chinese Chinese Age 57 62 61 63 Gender Female Male Male Male Weight (kg) 103.05 78.55 67.5 61.75 Height (m) 1.59 1.72 1.65 1.75 BMl (kgZm2) 40.76 26.55 24.79 20.16 Smoking Status X X X X Once a week (4 cans

[0101] Alcohol intake X of T 'er.256ml) X X Metformin

[0102] Carrcnt kidney medication, BP (Ghmiet DC 1, twice

[0103] meditation states medication day X X IPAQ Moderate* Moderate* High* High" n ri tonsil status

[0104] (M A) 13 (Normal) 13 (Normal) 13 (Normal) 12 (Normal) Table? t. r *rographic of four sublet recruited, The following are the link to ths raw data fcr JPAQra - H**:, FT -

[0105]

[0106] I:: j k. C * J ~

[0107] There is a positive mean difference between the after 4 weeks of intervention and the preintervention stage for both left and right hand (0.825 and 3.575 kg respectively). However, the results are statistically insignificant (Pleft = 0.6539 and Pright= 0.1054). A similar past study that also carried out for 4 weeks but with 20-30 minutes NMES session, has shown significant increase (p=0.04) in hand grip strength (Jang & Park, 2021). The smaller sample size in the present study may be the main contribution to the insignificant results. I.eft hand grip strength Right hand grip strength

[0108] (P-vai, mean difference} (P-val, mean difference)

[0109] After - Before 0.6539 0.1054

[0110] 0.825 kg 3.575 kg

[0111] Mi -Before 0.914? '’ 0.1729*

[0112] -0.15 kg 3.975 kg

[0113] After - Mid 0.2952'’ 0.8203*

[0114] 0.975 kg -0.4 g

[0115] Table 3: Tabla of mean diffsrence in hand grip strength tor both forearm between different time points.

[0116] The peak amplitude, RMS voltage and peak frequency reading were collected from ED to obtain a bigger picture in the effectiveness of NMES for this muscle part. The increase in right hand grip strength is coherent with the significant increase in peak amplitude and an insignificant increase in trend for RMS voltage. The increase in those reading may indicate increase in muscle strength as it has been described before that RMS voltage is reflective of force of muscular contraction (Fukuda et al., 2010).

[0117] Despite showing promising results, the peak frequency shows significant decrease after 4 weeks of training. Frequency is usually used in the assessing muscle fatigue and analyzing muscle unit recruitment (Phinyomark, Thongpanja, Hu, Phukpattaranont, & Limsakul, 2012). It is expected that the frequency will be increased due to more muscle unit being recruited after weeks of muscle training. One of the plausible reasons for such contradiction is likely associated to muscle fatigue as the result of having the regimen being completed on the same day, before the measurement. This shows the importance of participants preparation before the measurement.

[0118] Name Sample 1 Sample 2 Sample 3 Sample 4 Peak AmpHiude (¥)

[0119] Pre-Intervemion 2.50 4.54 6.01 2.31 Mid-lnterveiriien 3.56 8.94 4.97 4.75 Post-Intervention 4.00 8.62 9.74 9.60 RMS Voltage (V)

[0120] Pre-Intet ention 0.31 0.68 0.45 0.31 Mid-Interventian 0.36 1.02 0.39 0.73 Post-lnterveniien 0.54 1.90 0.80 1.09 Peak Frequency (Hz)

[0121] Pre-Intervention 36.30 38.40 50.00 35.40 Mid-Intervention 19.40 37.20 35.50 44.60 Post-fatenention 26.60 12.20 35.30 34.70 Table 4: Table of measurement of peak amplitude, R MS voltage and peak frequency at extensor digltorium (ED) tor alt

[0122]

[0123] samples. The MMG reading of quadriceps only show significant increase at VM for peak amplitude and RMS voltage. For peak amplitude there is a significant increase between the mid and preintervention while for RMS voltage show significant increase in both between mid and pre, and post and pre-intervention. However, for the other muscle points (VL and RF) does not shows a clear pattern as some samples has increase in value while other decrease. One of the reasons, that only VM show a clear increase in changes is because the placement of the NMES is closest or directly on VM but sparsely on to the other two muscle points. The spatial muscle unit recruitment by NMES is limited, such fixed recruitment diminishes proportionally with increasing distance from the electrode (Maffiuletti, 2010). The quadriceps muscle cross-sectional area activated by NMES also increase by intensity (Adams, Harris, Woodard, & Dudley, 1993), thus may explain the differences seen in different participants as different intensity was used by them to suit their level of pain tolerance. In addition, the variability in MMG frequencies across different samples indicates differences in muscle health and neuromuscular function among participants. This variability may be attributed to factors such as baseline muscle condition, individual physiological differences, and varying adherence to the NMES and exercise interventions (Islam et al., 2013).

[0124] To improve the outcome, there are few suggestions that can be done for a more even muscle unit recruitment. First, laiger coverage area of the pad can be used to ensure the taigeted area are well stimulated. The downside of this suggestion is the device will be larger and become less concealable for the users, not only that the cost for making the larger gel pad will also increase. Second option is that the protocol can be modified, where the gel pad can be moved after a series of contraction within the same NMES training period as suggested by (Maffiuletti, 2010). However, this method may dissatisfy or cause confusion to the user as they need to adjust the device frequently.

[0125] After being asked their feeling about wearable NMES like the disclosed system, all of them say that it was painful at the beginning but after few days using it, they have adapted to the device and does not feel so much pain after that. Sample 1 mentioned that, after using NMES, her leg feels lighter and easier to climb up the stairs. Not only that, they have also mentioned that the wearable NMES is time-saving and convenient as they can use it while doing their personal works. Overall, the comments and acceptance are positive. The present disclosure may be embodied in other specific forms without departing from its structures, methods, or other essential characteristics as broadly described above and claimed hereinafter. The described embodiments are to be considered in all respects only as illustrative, and not restrictive. The scope of the disclosure is, therefore, indicated by the appended claims, rather than by the foregoing description. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.

Claims

CLAIMS1. A muscle enhancement system (100) comprising:an electrode module (110) comprising an electrode assembly being configured to contact with a body part, underneath of which at least a portion of a muscle requiring enhancement located, to provide a training electrical stimulation of a predetermined amplitude or frequency towards the muscle prompting a series of muscle activities throughout a training session of a predetermined period;a sensor module (120) having an integrated sensor being positioned on the body part for deriving measurements associated to acceleration and angular velocity exerted on the integrated sensor in connection to the series of muscle activities within a three-dimensional environment at a given timeframe to generate corresponding MMG readings, the integrated sensor comprising an accelerometer coupled to a gyroscope and magnetometer to form a 9-axis inertial measurement unit (IMU) sensor;a primary controller module (130) being configured to retrieve the MMG readings, convert the MMG readings to corresponding voltage signals using a mapping formula, process the converted voltage signals, compute training root mean square (RMS) values of the processed voltage signals, perform analysis towards the training RMS values or generate data associated to the analysis;a communication module (140) operably connecting to the primary controller module (130) to retrieve the data, voltage signal or value from the controller module for relaying the data, voltage signal or value to a remotely located user terminal having a terminal user interface;a power module (150) comprising a battery electrically connecting to the various modules for powering operation of each module;a casing (160) having a top, a bottom and sidewalls to define a hollow space in which the sensor module (120), the primary controller module (130), the communication module (140) and the power module (1 0) are resided;a wearable strap (170) attachable to the body part of a subject covering at least a portion of the muscle, the strap (170) comprising a planar top surface and a planar bottom surface for respective mounting of the casing (160) and the electrode module (110);a plurality of male mounting elements (173) disposed on the top surface of the strap (170) to detachably couple with a plurality of corresponding female mounting elements (174) positioned at the bottom of the casing (160), coupling of the male and female mountingelements (174) enables electrical communication between the primary controller module and the electrode module (110), wherein the user terminal is communicating with the controller module wirelessly and able to adjust the predetermined amplitude or frequency of the training electrical stimulation throughout the training session.

2. The system (100) of claim 1 further comprising a database (190) connecting to the primary controller to store the information, signal, values or data of the training session.

3. The system (100) of claim 1 further comprising a control interface in electrical connection with the primary controller module (130) forming at least part of the top of the casing (160) to display at least one of the readings, signals, values and data.

4. The system (100) of claim 1, wherein the primary controller is configured to constantly check the computed training RMS value against a baseline RMS value and terminate the training session before end of the predetermined period upon detecting the computed training RMS value drops below a first threshold of the baseline RMS value.

5. The system ( 100) of claim 4, wherein the primary controller is configured to constantly check the computed training RMS value against the baseline RMS value and reduce the predetermined amplitude or frequency of the training electrical stimulation upon detecting the computed training RMS value drops below a second threshold of the baseline value.

6. The system (100) of claim 5, wherein the first threshold is greater than the second threshold.

7. The system (100) of claim 1, wherein the primary controller module (130) is further configured to:i. process the converted voltage signals using a high-pass filter, a Butterworth filter, and a Fast Fourier Transform analysis;ii. convert the MMG readings to corresponding voltage signals using a vector calculation formula to compute a total acceleration value as in:«= √(Xacc)2+ (Yacc)2+ (Zacc)2where R is a measurement of the total acceleration, Xaccis a measured acceleration value along the X-axis by the integrated sensor, YaCc is a measured acceleration value along the Y-axis by the integrated sensor, Zaccis a measured acceleration value along the Z-axis by the integrated sensor; anda mapping formula to convert the total acceleration value into the corresponding voltage value, as in:(R + 2g)V = - - — x 5T4#where V is the training voltage value corresponding to R; andiii. compute training root mean square (RMS) values of the processed voltage signals using a formula as in:RMS = √(1 / N Σ Vi2)J<=1where Vi is the voltage value of each sampled information within a defined time window, and N is the number of samples over the time window.

8. The system (100) of claim 1, wherein the MMG readings comprises measured acceleration of the body part around a X-axis, a Y-axis and a Z-axis within the three-dimensional environment.

9. The system (100) of claim 5, wherein the primary controller module (130) is configured to establish the baseline RMS value prior to providing the training electrical stimulation.

10. The system (100) of claim 9, wherein the primary controller module (130) adjustably increases or decreases the predetermined amplitude of the training electrical stimulation, predetermined frequency of the training electrical stimulation, or predetermined period of the training session based on the computed baseline RMS value.

11. The system (100) of claim 6, wherein the first threshold is 50 to 70% of the baseline RMS value.

12. The system (100) of claim 6, wherein the second threshold is 30 to 60% of the baseline RMS value.

13. A process for operating a muscle enhancement system, comprising steps of:initiating the muscle enhancement system for muscle contraction; establishing baseline root mean square (RMS) value prior to providing training electrical stimulation;providing the training electrical stimulation with a predetermined amplitude or frequency towards the muscle using an electrode module;deriving training mechanomyogram (MGM) readings associated to acceleration and angular velocity exerted on an integrated sensor by body movement, vibration or contraction of the stimulated muscle, throughout the training electrical stimulation; converting the training MMG readings to corresponding training voltage signals through a primary controller module;processing the obtained training voltage signals to filter noise and smooth out the voltage signals using the primary controller module;computing training RMS value of the processed voltage signals using a formula as in:RMS =where Vi is the voltage value of each sampled information within a defined time window, and N is the number of samples over the time window;constantly checking the computed training RMS value against the baseline RMS value using the primary controller module;reducing predetermined amplitude or frequency of the training electrical stimulation upon detecting the computed RMS value drops below a second threshold of the baseline RMS value; andterminating the training electrical stimulation before end of a predetermined period upon detecting the computed RMS value drops below a first threshold of the baseline RMS value, wherein the first threshold is greater than the second threshold.

14. The process of claim 13, wherein the step of establishing baseline root mean square (RMS) value prior to providing the training electrical stimulation further comprises method steps comprising:applying an initial electrical stimulation using an initial neuromuscular electrical stimulation (NMES) parameter to the muscle according to an initial parameter for an initial voltage, initial frequency, and initial pulse duration of the initial electrical stimulation;deriving initial MMG readings from the integrated sensor for a predetermined duration; andcomputing the baseline RMS value using the initial MMG readings.

15. The process of claim 13, wherein the step of converting the training MMG readings to corresponding training voltage signals through a primary controller module further comprises method steps comprising:computing a total acceleration of body parts expressed in g-forced based on a vector calculation formula as in:R= √(Xacc)2+ (Yacc)2+ (Zacc)2where R is a measurement of the total acceleration, Xaccis a measured acceleration value along the X-axis by the integrated sensor, YaCc is a measured acceleration value along the Y-axis by the integrated sensor, Zaccis a measured acceleration value along the Z-axis by the integrated sensor; andconverting the total acceleration into the corresponding voltage value using the mapping formula as in:(R + 2g)V = x 5T4#where V is the training voltage value corresponding to R.

16. The process of claim 13, wherein the step of processing the obtained training voltage signals to fdter noise and smooth out the voltage signals using the primary controller module further comprises method steps comprising:filtering the converted training voltage signals using a high-pass filter; filtering the converted training voltage signals using a Butterworth filter; andperforming a Fast Fourier Transform analysis towards the filtered training voltage signals.

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