Force Myography Signals Research Articles

Development and evaluation of a novel force myography (FMG) based human-machine interface for transradial prosthetics: A comparative study with electromyography (EMG) techniques

Human-Machine Interfaces (HMIs) must bridge human skills with machine control, notably in active prosthesis. Electromyography (EMG) is the most popular prosthesis actuation method. However, researchers need new HMIs for active prosthesis development. This research introduces Forcemyography (FMG) based HMI using force- sensitive resistors (FSRs) to capture myoelectric data. A particularly constructed case with two integral parts—an internal and an exterior component—is FMG. This FMG enclosure adapts effortlessly to the user's limb and shape for best performance and comfort. FMG and sEMG were compared in the experimental evaluation. Stability, forecast accuracy, hair and sweat resistance, wearability, and cost-effectiveness were examined. FMG signals outperformed sEMG signals in this comprehensive examination. FMG outperformed sEMG in stability and prediction accuracy. FMG produced outstanding results right from capture without post-processing, a unique advantage. The specialised enclosure protected FSRs from outside interference and delivered precise signals. The Forcemyography (FMG)-based HMI is a breakthrough in active prosthesis technology. Its versatility, consistent signal acquisition, and user-centric design promise improved human-machine interactions. FMG leads the way to smooth, intuitive, and efficient humanmachine interface.

High-Performance Hydrogel Sensors Enabled Multimodal and Accurate Human-Machine Interaction System for Active Rehabilitation.

Human-machine interaction (HMI) technology shows an important application prospect in rehabilitation medicine, but it is greatly limited by the unsatisfactory recognition accuracy and wearing comfort. Here, this work develops a fully flexible, conformable, and functionalized multimodal HMI interface consisting of hydrogel-based sensors and a self-designed flexible printed circuit board. Thanks to the component regulation and structural design of the hydrogel, both electromyogram (EMG) and forcemyography (FMG) signals can be collected accurately and stably, so that they are later decoded with the assistance of artificial intelligence (AI). Compared with traditional multichannel EMG signals, the multimodal human-machine interaction method based on the combination of EMG and FMG signals significantly improves the efficiency of human-machine interaction by increasing the information entropy of the interaction signals. The decoding accuracy of the interaction signals from only two channels for different gestures reaches 91.28%. The resulting AI-powered active rehabilitation system can control a pneumatic robotic glove to assist stroke patients in completing movements according to the recognized human motion intention. Moreover, this HMI interface is further generalized and applied to other remote sensing platforms, such as manipulators, intelligent cars, and drones, paving the way for the design of future intelligent robot systems.

Dataset on Force Myography for Human–Robot Interactions

Force myography (FMG) is a contemporary, non-invasive, wearable technology that can read the underlying muscle volumetric changes during muscle contractions and expansions. The FMG technique can be used in recognizing human applied hand forces during physical human robot interactions (pHRI) via data-driven models. Several FMG-based pHRI studies were conducted in 1D, 2D and 3D during dynamic interactions between a human participant and a robot to realize human applied forces in intended directions during certain tasks. Raw FMG signals were collected via 16-channel (forearm) and 32-channel (forearm and upper arm) FMG bands while interacting with a biaxial stage (linear robot) and a serial manipulator (Kuka robot). In this paper, we present the datasets and their structures, the pHRI environments, and the collaborative tasks performed during the studies. We believe these datasets can be useful in future studies on FMG biosignal-based pHRI control design.

A Novel PPG-FMG-ACC Wristband for Hand Gesture Recognition

Wrist-based hand gesture recognition has the potential to unlock naturalistic human-computer interaction for a vast array of virtual and augmented reality applications. Photoplethysmography (PPG), force myography (FMG), and accelerometry (ACC) have generally been proposed as isolated single sensing modalities for gesture recognition, but any of these alone is inherently limited in the amount of biological information it can collect during finger and hand movements. We thus propose a novel, wrist-based, PPG-FMG-ACC combined sensing approach based on a multi-head attention mechanism fusion convolutional neural network (CNN-AF) for gesture recognition. Nine subjects performed twelve hand gestures involving various wrist and finger postures. Experimental results showed that multi-modal fusion improved classification performance significantly ( p 0.01) compared to any single sensing modality, and the F1-score of the combined PPG-FMG-ACC approach was 40.1% higher than PPG alone, 27.4% higher than ACC alone, and 11.9% higher than FMG alone. To the best of our knowledge, this paper is the first to combine wrist-based PPG, FMG, and ACC signals for hand gesture recognition. These results could serve to inform wrist-based gesture recognition design (e.g., via a smartwatch) and thus expand the capabilities of intuitive and ubiquitous human-machine interaction.

Wearable Iontronic FMG for Classification of Muscular Locomotion.

Human motion recognition with high accuracy and fast response speed has long been considered an essential component in human-machine interactive activities such as assistive robotics, medical prosthesis, and wearable electronics. The force myography (FMG) signal has been the focus of much investigation in the search for a reliable and efficient muscular locomotion recognition system. However, the effect of the sensing system on FMG-based locomotion classification accuracy has yet to be understood. This study proposed a novel FMG sensing strategy for human lower limb locomotion classification based on flexible supercapacitive iontronic sensors. Benefiting from the ultrahigh sensitivity (up to 1 nF/mmHg) and low activation pressure (less than 5 mmHg) of the supercapacitive iontronic pressure sensor, FMG signal can be acquired accurately from 5 iontronic sensors strapped to the thigh. In the experiment with 12 subjects, the real-time classification strategy based on sliding window and SVM model gave an average locomotion classification accuracy of 99% for seven categories, including sitting, standing, walking on level ground, ramp ascent, ramp descent, stair ascent, stair descent. Compared with traditional FSR sensors, the result showed that iontronic sensors improved the classification accuracy by up to 10 percentage points in the case of short time window. The implementation of the high sensitivity flexible iontronic sensors in the wearable system brings a valuable tool for detecting small human body pressure signals and has great potential to improve the performance of the human-machine interface in rehabilitation and medical applications.

Improving the Robustness of Human-Machine Interactive Control for Myoelectric Prosthetic Hand During Arm Position Changing.

Robust classification of natural hand grasp type based on electromyography (EMG) still has some shortcomings in the practical prosthetic hand control, owing to the influence of dynamic arm position changing during hand actions. This study provided a framework for robust hand grasp type classification during dynamic arm position changes, improving both the “hardware” and “algorithm” components. In the hardware aspect, co-located synchronous EMG and force myography (FMG) signals are adopted as the multi-modal strategy. In the algorithm aspect, a sequential decision algorithm is proposed by combining the RNN-based deep learning model with a knowledge-based post-processing model. Experimental results showed that the classification accuracy of multi-modal EMG-FMG signals was increased by more than 10% compared with the EMG-only signal. Moreover, the classification accuracy of the proposed sequential decision algorithm improved the accuracy by more than 4% compared with other baseline models when using both EMG and FMG signals.

Building Effective Machine Learning Models for Ankle Joint Power Estimation During Walking Using FMG Sensors.

Ankle joint power is usually determined by a complex process that involves heavy equipment and complex biomechanical models. Instead of using heavy equipment, we proposed effective machine learning (ML) and deep learning (DL) models to estimate the ankle joint power using force myography (FMG) sensors. In this study, FMG signals were collected from nine young, healthy participants. The task was to walk on a special treadmill for five different velocities with a respective duration of 1 min. FMG signals were collected from an FMG strap that consists of 8 force resisting sensor (FSR) sensors. The strap was positioned around the lower leg. The ground truth value for ankle joint power was determined with the help of a complex biomechanical model. At first, the predictors' value was preprocessed using a rolling mean filter. Following, three sets of features were formed where the first set includes raw FMG signals, and the other two sets contained time-domain and frequency-domain features extracted using the first set. Cat Boost Regressor (CBR), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) were trained and tested using these three features sets. The results presented in this study showed a correlation coefficient of R = 0.91 ± 0.07 for intrasubject testing and were found acceptable when compared to other similar studies. The CNN on raw features and the LSTM on time-domain features outperformed the other variations. Aside from that, a performance gap between the slowest and fastest walking distance was observed. The results from this study showed that it was possible to achieve an acceptable correlation coefficient in the prediction of ankle joint power using FMG sensors with an appropriate combination of feature set and ML model.

A Way of Bionic Control Based on EI, EMG, and FMG Signals.

Creating highly functional prosthetic, orthotic, and rehabilitation devices is a socially relevant scientific and engineering task. Currently, certain constraints hamper the development of such devices. The primary constraint is the lack of an intuitive and reliable control interface working between the organism and the actuator. The critical point in developing these devices and systems is determining the type and parameters of movements based on control signals recorded on an extremity. In the study, we investigate the simultaneous acquisition of electric impedance (EI), electromyography (EMG), and force myography (FMG) signals during basic wrist movements: grasping, flexion/extension, and rotation. For investigation, a laboratory instrumentation and software test setup were made for registering signals and collecting data. The analysis of the acquired signals revealed that the EI signals in conjunction with the analysis of EMG and FMG signals could potentially be highly informative in anthropomorphic control systems. The study results confirm that the comprehensive real-time analysis of EI, EMG, and FMG signals potentially allows implementing the method of anthropomorphic and proportional control with an acceptable delay.

Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition.

Force myography (FMG) is a method that uses pressure sensors to measure muscle contraction indirectly. Compared with the conventional approach utilizing myoelectric signals in hand gesture recognition, it is a valuable substitute. To achieve the aim of gesture recognition at minimum cost, it is necessary to study the minimum sampling frequency and the minimal number of channels. For purpose of investigating the effect of sampling frequency and the number of channels on the accuracy of gesture recognition, a hardware system that has 16 channels has been designed for capturing forearm FMG signals with a maximum sampling frequency of 1 kHz. Using this acquisition equipment, a force myography database containing 10 subjects’ data has been created. In this paper, gesture accuracies under different sampling frequencies and channel’s number are obtained. Under 1 kHz sampling rate and 16 channels, four of five tested classifiers reach an accuracy up to about 99%. Other experimental results indicate that: (1) the sampling frequency of the FMG signal can be as low as 5 Hz for the recognition of static movements; (2) the reduction of channel number has a large impact on the accuracy, and the suggested channel number for gesture recognition is eight; and (3) the distribution of the sensors on the forearm would affect the recognition accuracy, and it is possible to improve the accuracy via optimizing the sensor position.

A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance.

ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand gesture recognition performance. This paper proposes a novel signal processing pipeline employing a manifold learning method to produce a robust signal representation to boost hand gesture classifiers’ performance. We tested this approach on an FMG dataset collected from nine participants in 3 different data collection sessions with short delays between each. For each participant’s data, the proposed pipeline was applied, and then different classification algorithms were used to evaluate the effect of the pipeline compared to raw FMG signals in hand gesture classification. The results show that incorporating the proposed pipeline reduced variance within the same gesture data and notably maximized variance between different gestures, allowing improved robustness of hand gestures classification performance and consistency across time. On top of that, the pipeline improved the classification accuracy consistently regardless of different classifiers, gaining an average of 5% accuracy improvement.

Design, Development and Evaluation of Novel Force Myography Based 2-Degree of Freedom Transradial Prosthesis

Limb loss is a traumatic event as it has physical and psychological effects on an amputee. Recent advancements in mechatronics and biomedical engineering have resulted in development of dexterous myoelectric prostheses for rehabilitation of amputees. In addition, evolution in manufacturing and sensing technology presents ample room for improvement in mechanical design and control system of prostheses to enhance amputee experience while using prosthetic devices. The present study is focused on design of a novel and cost-effective externally powered two-degree-of freedom prosthesis for assisting amputees to switch from body-powered devices to externally powered prosthesis. The control system of the developed prosthesis is based on the muscles signals acquired through force myography (FMG) technique. For precise integration of force-sensitive resistor (FSR) inside the socket to measure muscle activity, a stand-alone housing for FSR was designed with the feature of mechanical adjustment to control sensitivity of FSR and auto-calibrate its threshold to meet the requirements of individual amputees. The housing was designed to handle the fabrication inconsistencies during socket shaping process and thus ensure that sensor is in-firm contact with the muscle to sense volumetric changes. The developed mechanical design and FMG based muscle acquisition technique was successfully tested on a transradial amputee and extensive experimentation was performed for characterization of the prosthesis. FMG signal for various gestures was successfully extracted from muscles of the amputee to control the prosthesis according to the developed control technique. The results suggested that integration of FSR in the socket has significantly reduced the effect of sweat and volumetric changes on the performance of the sensor. Due to its novel design, embedded features, and cost-effectiveness the developed prototype holds the promise to be successfully commercialized to assist transradial amputees in becoming active citizens for contributing towards socio-economic growth of their country.

An Ultra-Sensitive Modular Hybrid EMG-FMG Sensor with Floating Electrodes.

To improve the reliability and safety of myoelectric prosthetic control, many researchers tend to use multi-modal signals. The combination of electromyography (EMG) and forcemyography (FMG) has been proved to be a practical choice. However, an integrative and compact design of this hybrid sensor is lacking. This paper presents a novel modular EMG–FMG sensor; the sensing module has a novel design that consists of floating electrodes, which act as the sensing probe of both the EMG and FMG. This design improves the integration of the sensor. The whole system contains one data acquisition unit and eight identical sensor modules. Experiments were conducted to evaluate the performance of the sensor system. The results show that the EMG and FMG signals have good consistency under standard conditions; the FMG signal shows a better and more robust performance than the EMG. The average accuracy is 99.07% while using both the EMG and FMG signals for recognition of six hand gestures under standard conditions. Even with two layers of gauze isolated between the sensor and the skin, the average accuracy reaches 90.9% while using only the EMG signal; if we use both the EMG and FMG signals for classification, the average accuracy is 99.42%.

Force myography controlled multifunctional hand prosthesis for upper-limb amputees

Existing myoelectric prostheses can provide solutions to amputees for performing activities of daily livings. However, there are several issues with these prostheses (1) performance are affected mainly by sweat, electrode shift, motion artifact, interference, etc. (2) are expensive (3) have complex control schemes.This paper presents an affordable hand prosthesis solution for transradial amputees, which uses force myography (FMG) as a control signal in place of electromyography (EMG) signal. A novel FMG sensor was designed to detect mechanical muscle contractions from the residual forearm of amputees. Two distinct control strategies were formulated for translating the FMG signals from the sensor to the control commands for driving custom-made prosthetic hands.Prosthetic hand prototype 1 with a proportional control scheme was developed and verified on five amputees. Subjects wearing the hand were able to grasp various objects using their intent of muscular contractions.Fuzzy logic based classification scheme with an offline success rate above 97 % was established for identifying six different hand gestures from only single-channel FMG data. Further, the system, along with the threshold control, was implemented for the developed hand prototype 2 to obtain six distinct grip patterns. The real-time testing of hand on thirteen subjects (five amputees and eight intact) showed a success percentage above 95 % in performing dexterous grasping operations.As compared to the presently available hands, the hand prototype 2 displayed comparable features at such a low-cost price. However, with limited functionality, hand prototype 1 is an affordable, practical, intuitive, and faster version of the prosthetic hand.

Application of Forearm FMG signals in Closed Loop Modality-matched Sensory Feedback Stimulation

This study is aimed at exploring a technology that can use the human physiological information, such as Force Myography (FMG) signals to provide sensory feedback to prosthetic hand users. This is based on the principle that with the intent to move the prosthetic hand, the existing limbs in the arm recruit specific group of muscles. These muscles react with a change in the cross-sectional area; piezoelectric sensors placed on these muscles will generate a voltage (FMG signals), in response to the change in muscle volume. The correlation between the amplitude of the FMG signals and intensity of pressure on fingertips during grasping is then computed and a dynamic relation (model) is established through system identification in MATLAB. The estimated models generated a fitting accuracy of more than 80%. The model is then programmed into the Arduino microcontroller, so that a real-time and proportional force feedback is channeled to amputees through a micro actuator. Obtaining such percentages of accuracy in sensory feedback without relying on touch sensors on the prosthetic hand that could be affected by mechanical wear and other interaction factors is promising. Applying advanced signal processing and classification techniques may also refine the findings to better capture and correlate the force variations with the sensory feedback.

Multimodal sensor to measure the concurrent electrical and mechanical activity of muscles for controlling a hand prosthesis

A wearable sensor was fabricated to provide simultaneous electromyography (EMG) and force myography (FMG) information, from the same muscle location for controlling multi-functional hand prosthesis. The typical mechanical arrangement of the sensor constitutes silver electrodes and a force-sensitive resistor embedded inside the sensor chassis for detecting the electrical and mechanical activity of muscles. The sensor incorporates specific conditioning units for producing quantifiable EMG and FMG outputs from electrodes and force-sensitive resistor. The sensor exhibited excellent sensitivity and repeatability characteristics in recording force signals. The dry electrodes used in the sensor showed a comparable electrode-skin impedance with conventional Ag-AgCl electrodes. The EMG signals obtained with the sensor for ten subjects showed a good correlation and similar signal-to-noise ratio to signals with a traditional EMG sensor. Further, the nature of the measured EMG and FMG signals for the subjects by the sensor were analyzed by determining features such as signal stability and response time. The simultaneously recorded EMG-FMG signals from the sensor for different muscular contractions can be fused to generate a more reliable control for dexterous operation of hand prosthesis.

Estimating Exerted Hand Force via Force Myography to Interact with a Biaxial Stage in Real-Time by Learning Human Intentions: A Preliminary Investigation.

Force myography (FMG) signals can read volumetric changes of muscle movements, while a human participant interacts with the environment. For collaborative activities, FMG signals could potentially provide a viable solution to controlling manipulators. In this paper, a novel method to interact with a two-degree-of-freedom (DoF) system consisting of two perpendicular linear stages using FMG is investigated. The method consists in estimating exerted hand forces in dynamic arm motions of a participant using FMG signals to provide velocity commands to the biaxial stage during interactions. Five different arm motion patterns with increasing complexities, i.e., “x-direction”, “y-direction”, “diagonal”, “square”, and “diamond”, were considered as human intentions to manipulate the stage within its planar workspace. FMG-based force estimation was implemented and evaluated with a support vector regressor (SVR) and a kernel ridge regressor (KRR). Real-time assessments, where 10 healthy participants were asked to interact with the biaxial stage by exerted hand forces in the five intended arm motions mentioned above, were conducted. Both the SVR and the KRR obtained higher estimation accuracies of 90–94% during interactions with simple arm motions (x-direction and y-direction), while for complex arm motions (diagonal, square, and diamond) the notable accuracies of 82–89% supported the viability of the FMG-based interactive control.

Towards the investigation on the effect of the forearm rotation on the wrist FMG signal pattern using a high-density FMG sensing matrix

Force myography (FMG) is an emerging technique to predict extremity movement. The FMG signals captured near the wrist can be used to predict many hand gestures. However, the wrist FMG pattern of a ...

Estimation of User-Applied Isometric Force/Torque Using Upper Extremity Force Myography.

Hand force estimation is critical for applications that involve physical human-machine interactions for force monitoring and machine control. Force Myography (FMG) is a potential technique to be used for estimating hand force/torque. The FMG signals reflect the volumetric changes in the arm muscles due to muscle contraction or expansion. This paper investigates the feasibility of employing force-sensing resistors (FSRs) worn on the arm to measure the FMG signals for isometric force/torque estimation. Nine participants were recruited in this study and were asked to exert isometric force along three perpendicular axes, torque about the same three axes, and force and torque simultaneously. During the tests, the isometric force and torque were measured using a 6-degree-of-freedom (DoF) (i.e., force in three axes and torque around the same axes) load cell for ground truth labels whereas the FMG signals were recorded using a total number of 60 FSRs, which were embedded into four bands worn on the different locations of the arm. A two-stage regression strategy was employed to enhance the performance of the FMG bands, where three regression algorithms including general regression neural network (GRNN), support vector regression (SVR), and random forest regression (RF) models were employed, respectively, in the first stage and GRNN was used in the second stage. Two cases were considered to explore the performance of the FMG bands in estimating: (1) 3-DoF force and 3-DoF torque at once and (2) 6-DoF force and torque. In addition, the impact of sensor placement and the spatial coverage of FMG measurements were studied. This preliminary investigation demonstrates promising potential of FMG to estimate multi-DoF isometric force/torque. Specifically, R2 accuracies of 0.83 for the 3-DoF force, 0.84 for 3-DoF torque, and 0.77 for the combination of force and torque (6-DoF) regressions were obtained using the four bands on the arm in cross-trial evaluation.

Novel force myography sensor to measure muscle contractions for controlling hand prostheses

This paper presents a dual-channel, noninvasive force myography (FMG) sensor to extract muscle contraction information for controlling hand prostheses. The sensor was prepared using a pair of force-sensitive resistors (FSRs) mounted inside a rigid base for sensing the force exerted by contracting muscles through polydimethylsiloxane (PDMS) couplers. The device employs a dedicated signal conditioning circuitry for producing an output voltage proportional to the muscular contractile force. The static and dynamic characteristics of the sensor (i.e., sensitivity, drift, precision, hysteresis, and frequency response) were determined and analyzed using the recorded measurements to demonstrate its effectiveness. The frequency response of the designed sensor was sufficiently large to detect the rapidly varying FMG signals. The output assessment for the simultaneous acquisition of electromyography (EMG) and FMG from flexor muscles of subjects was performed using a two-tailed paired t-test, which showed a high correlation coefficient (r > 0.87) with a p-value less than 0.0001. Furthermore, a successful trial of the FMG sensor was made on five subjects to control a prosthetic hand in real-time, employing the proportional strategy. These experiments revealed that the designed sensor may provide an alternative to the EMG device.

An Investigation on the Sampling Frequency of the Upper-Limb Force Myographic Signals.

Force myography (FMG) is an emerging method to register muscle activity of a limb using force sensors for human–machine interface and movement monitoring applications. Despite its newly gained popularity among researchers, many of its fundamental characteristics remain to be investigated. The aim of this study is to identify the minimum sampling frequency needed for recording upper-limb FMG signals without sacrificing signal integrity. Twelve healthy volunteers participated in an experiment in which they were instructed to perform rapid hand actions with FMG signals being recorded from the wrist and the bulk region of the forearm. The FMG signals were sampled at 1 kHz with a 16-bit resolution data acquisition device. We downsampled the signals with frequencies ranging from 1 Hz to 500 Hz to examine the discrepancies between the original signals and the downsampled ones. Based on the results, we suggest that FMG signals from the forearm and wrist should be collected with minimum sampling frequencies of 54 Hz and 58 Hz for deciphering isometric actions, and 70 Hz and 84 Hz for deciphering dynamic actions. This fundamental work provides insight into minimum requirements for sampling FMG signals such that the data content of such signals is not compromised.