Sock-Type Wearable Sensor for Estimating Lower Leg Muscle Activity Using Distal EMG Signals

Preliminary Study on Wearable Smart Socks with Hydrogel Electrodes for Surface Electromyography-Based Muscle Activity Assessment

Surface electromyography (sEMG) is increasingly important for prevention, diagnosis, and rehabilitation in healthcare. The continuous monitoring of muscle electrical activity enables the detection of abnormal events, but existing sEMG systems often rely on disposable pre-gelled electrodes that can cause skin irritation and require precise placement by trained personnel. Wearable sEMG systems integrating textile electrodes have been proposed to improve usability; however, they often suffer from poor skin-electrode coupling, leading to higher impedance, motion artifacts, and reduced signal quality. To address these limitations, we propose a preliminary model of smart socks, integrating biocompatible hybrid polymer electrodes positioned over the target muscles. Compared with commercial Ag/AgCl electrodes, these hybrid electrodes ensure lower the skin-electrode impedance, enhancing signal acquisition (19.2 ± 3.1 kΩ vs. 27.8 ± 4.5 kΩ for Ag/AgCl electrodes). Moreover, to the best of our knowledge, this is the first wearable system incorporating hydrogel-based electrodes in a sock specifically designed for the analysis of lower limb muscles, which are crucial for evaluating conditions such as sarcopenia, fall risk, and gait anomalies. The system incorporates a lightweight, wireless commercial module for data pre-processing and transmission. sEMG signals from the Gastrocnemius and Tibialis muscles were analyzed, demonstrating a strong correlation (R = 0.87) between signals acquired with the smart socks and those obtained using commercial Ag/AgCl electrodes. Future studies will further validate its long-term performance under real-world conditions and with a larger dataset.

Longitudinal analysis of lower limb muscle activity and ankle tendon biosignals using structural equation modeling

We collected biosignals from 63 participants and extracted the features corresponding to each level of exerted muscle force. Data were classified into typical and atypical patterns. Data analysis was performed using the Linear Latent Curve Model (LCM) and the Conditional Linear LCM. The typical patterns demonstrated a high degree of fit. Factors, such as ankle circumference and muscle mass, influenced the model intercept. A larger ankle circumference indicated attenuation of signal transmission from the tendon to the skin surface, leading to lower biosignal values.  These results indicate that biosignals from the tendons near the ankle can be captured using piezoelectric film sensors. There are studies that define biosignals originating from tendons as mechanotendography. It has been demonstrated that the relationship between biosignals originating from tendons and the exerted muscle force can be explained linearly. Insights from this study may facilitate individualized approaches in the fields of motion control and rehabilitation. Physiological studies to elucidate the mechanisms underlying biosignal generation are necessary.

Predicting Grip Aperture using Forearm Muscle Activation Data

The performance of activities of daily living (ADLs) is directly related to recovery of motor function after stroke. Because the recovery process occurs primarily in the home, there is a need for tools sensitive to this process that can be used in ambient settings. The goal of the current approach is to use surface electromyography (sEMG) acquired from wearable sensors to capture relevant ADL performance. Our specific focus is on detecting thumb-forefinger aperture. This aperture, which occurs during reach-to-grasp (RTG) movements, is an indicator of potential success of interacting with the environment. Our results suggest that sEMG data can be used to determine increasing thumb-forefinger aperture in a population of non-disabled individuals. We find a statistically significant effect of increased aperture on peak sEMG values (p < 0.001).

Towards standardisation of surface electromyography measurements in the horse: Bipolar electrode location

The use of surface electromyography in the field of animal locomotion has increased considerably over the past decade. However, no consensus exists on the methodology for data collection in horses. This study aimed to start the development of recommendations for bipolar electrode locations to collect surface electromyographic data from horses during dynamic tasks. Data were collected from 21 superficial muscles of three horses during trot on a treadmill using linear electrode arrays. The data were assessed both quantitatively (signal-to-noise ratio (SNR) and coefficient of variation (CoV)) and qualitatively (presence of crosstalk and activation patterns) to compare and select electrode locations for each muscle. For most muscles and horses, the highest SNR values were detected near or cranial/proximal to the central region of the muscle. Concerning the CoV, there were larger differences between muscles and horses than within muscles. Qualitatively, crosstalk was suspected to be present in the signals of twelve muscles but not in all locations in the arrays. With this study, a first attempt is made to develop recommendations for bipolar electrode locations for muscle activity measurements during dynamic contractions in horses. The results may help to improve the reliability and reproducibility of study results in equine biomechanics.

Novel Muscle Sensing by Radiomyography (RMG) and Its Application to Hand Gesture Recognition

Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable or touchless, capturing both superficial and deep muscle groups. We verified RMG experimentally by a wearable forearm sensor for hand gesture recognition (HGR). We first converted the sensor outputs to the time-frequency spectrogram, and then employed the vision transformer (ViT) deep learning network as the classification model, which can recognize 23 gestures with an average accuracy up to 99% on 8 subjects. By transfer learning, high adaptivity to user difference and sensor variation were achieved at an average accuracy up to 97%. We further extended RMG to monitor eye and leg muscles and achieved high accuracy for eye movement and body posture tracking. RMG can be used with synchronous EMG to derive stimulation-actuation waveforms for many potential applications in kinesiology, physiotherapy, rehabilitation, and human-machine interface.

A Focused Review on the Flexible Wearable Sensors for Sports: From Kinematics to Physiologies

As an important branch of wearable electronics, highly flexible and wearable sensors are gaining huge attention due to their emerging applications. In recent years, the participation of wearable devices in sports has revolutionized the way to capture the kinematical and physiological status of athletes. This review focuses on the rapid development of flexible and wearable sensor technologies for sports. We identify and discuss the indicators that reveal the performance and physical condition of players. The kinematical indicators are mentioned according to the relevant body parts, and the physiological indicators are classified into vital signs and metabolisms. Additionally, the available wearable devices and their significant applications in monitoring these kinematical and physiological parameters are described with emphasis. The potential challenges and prospects for the future developments of wearable sensors in sports are discussed comprehensively. This review paper will assist both athletic individuals and researchers to have a comprehensive glimpse of the wearable techniques applied in different sports.

Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges

Wearable health monitoring devices allow for measuring physiological parameters without restricting individuals' daily activities, providing information that is reflective of an individual's health and well-being. However, these systems need to be accurate, power-efficient, unobtrusive and simple to use to enable a reliable, convenient, automatic and ubiquitous means of long-term health monitoring. One such system can be embedded in an insole to obtain physiological data from the plantar aspect of the foot that can be analyzed to gain insight into an individual's health. This manuscript provides a comprehensive review of insole-based sensor systems that measure a variety of parameters useful for overall health monitoring, with a focus on insole-based PPD measurement systems developed in recent years. Existing solutions are reviewed, and several open issues are presented and discussed. The concept of a fully integrated insole-based health monitoring system and considerations for future work are described. By developing a system that is capable of measuring parameters such as PPD, gait characteristics, foot temperature and heart rate, a holistic understanding of an individual's health and well-being can be obtained without interrupting day-to-day activities. The proposed device can have a multitude of applications, such as for pathology detection, tracking medical conditions and analyzing gait characteristics.

A standalone computing system to classify human foot movements using machine learning techniques for ankle-foot prosthesis control

This paper presents different machine learning techniques to classify the following foot movements: (i) dorsiflexion, (ii) plantarflexion, (iii) inversion, (iv) eversion, (v) medial rotation, and (vi) lateral rotation. The purpose is to design a real-time standalone computing system to predict the foot movements in the sagittal plane, useful for ankle-foot prosthesis control. Electromyography (EMG) and forcemyography (FMG) signals were acquired from the leg's tibialis anterior, medial gastrocnemius, lateral gastrocnemius, and peroneus longus muscles. First, Raspberry Pi was used to acquire EMG/FMG signals and to classify foot movements in real-time using different machine learning techniques. Later, an Arduino Nano 33 BLE controller was employed to implement the TinyML algorithm to classify these foot movements in the Arduino environment. The results showed that Raspberry Pi-based classification provided more than 99.5% accuracy for the EMG signals using LDA, LR, KNN, and SVC classifiers for offline prediction. However, for the classification of real-time signals, the performance of LDA is exceptionally well in predicting all classes. For Arduino Nano 33 BLE controller, the TinyML algorithm performed the classification task in real-time (8.5msec) without any misclassification. Further, the classification accuracy using EMG signal is much better than FMG based classification. Finally, the TinyML algorithm is applied on a transtibial amputee, and it is found that all three classes were classified correctly. Our finding suggests that a TinyML based Arduino Nano 33 BLE microcontroller is comparatively faster to predict and control, and it is smaller in size, thus advantageous for real-time prosthetic leg control applications.

Correction of Electrode ID Configuration based on Distribution of Surface EMG Features

Surface EMG (sEMG) signals are useful for estimating the motion or exercise of users. Wireless-type sensor electrodes, which are placed on multiple parts of the body and send the measured signals to a server, have recently become commercially available. With many estimation algorithms, the relationships between the sensor IDs and the body parts they are placed on (ID configuration) are expected to be fixed between the calibration and estimation phases. If the ID configuration is changed after the calibration phase, the estimation accuracy tends to dramatically decrease. Since it is inconvenient for users to check the ID configuration every time, we developed a method to correct the electrode ID configuration on the basis of the distribution of sEMG features. Using open data, we investigated the feasibility of our method by shuffling the order of sEMG signals. The results showed that the method was able to correct the ID configuration and restore the estimation accuracy to close to that of the calibration.

A review of wearable and unobtrusive sensing technologies for chronic disease management

With the rapidly increasing number of patients with chronic disease, numerous recent studies have put great efforts into achieving long-term health monitoring and patient management. Specifically, chronic diseases including cardiovascular disease, chronic respiratory disease and brain disease can threaten patients' health conditions over a long period of time, thus effecting their daily lives. Vital health parameters, such as heart rate, respiratory rate, SpO2 and blood pressure, are closely associated with patients’ conditions. Wearable devices and unobtrusive sensing technologies can detect such parameters in a convenient way and provide timely predictions on health condition deterioration by tracking these biomedical signals and health parameters. In this paper, we review current advancements in wearable devices and unobtrusive sensing technologies that can provides possible tools and technological supports for chronic disease management. Current challenges and future directions of related techniques are addressed accordingly.

Recent Advances in Wearable Devices for Non-Invasive Sensing

The development of wearable sensors is aimed at enabling continuous real-time health monitoring, which leads to timely and precise diagnosis anytime and anywhere. Unlike conventional wearable sensors that are somewhat bulky, rigid, and planar, research for next-generation wearable sensors has been focused on establishing fully-wearable systems. To attain such excellent wearability while providing accurate and reliable measurements, fabrication strategies should include (1) proper choices of materials and structural designs, (2) constructing efficient wireless power and data transmission systems, and (3) developing highly-integrated sensing systems. Herein, we discuss recent advances in wearable devices for non-invasive sensing, with focuses on materials design, nano/microfabrication, sensors, wireless technologies, and the integration of those.

Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications

The present paper reviews, for the first time, to the best of our knowledge, the most recent advances in research concerning two popular devices used for foot motion analysis and health monitoring: smart socks and in-shoe systems. The first one is representative of textile-based systems, whereas the second one is one of the most used pressure sensitive insole (PSI) systems that is used as an alternative to smart socks. The proposed methods are reviewed for smart sock use in special medical applications, for gait and foot pressure analysis. The Pedar system is also shown, together with studies of validation and repeatability for Pedar and other in-shoe systems. Then, the applications of Pedar are presented, mainly in medicine and sports. Our purpose was to offer the researchers in this field a useful means to overview and select relevant information. Moreover, our review can be a starting point for new, relevant research towards improving the design and functionality of the systems, as well as extending the research towards other areas of applications using sensors in smart textiles and in-shoe systems.

An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise

In recent years, wearable monitoring devices have been very popular in the health care field and are being used to avoid sport injuries during exercise. They are usually worn on the wrist, the same as sport watches, or on the chest, like an electrocardiogram patch. Common functions of these wearable devices are that they use real time to display the state of health of the body, and they are all small sized. The electromyogram (EMG) signal is usually used to show muscle activity. Thus, the EMG signal could be used to determine the muscle-fatigue conditions. In this study, the goal is to develop an EMG patch which could be worn on the lower leg, the gastrocnemius muscle, to detect real-time muscle fatigue while exercising. A micro controller unit (MCU) in the EMG patch is part of an ARM Cortex-M4 processor, which is used to measure the median frequency (MF) of an EMG signal in real time. When the muscle starts showing tiredness, the median frequency will shift to a low frequency. In order to delete the noise of the isotonic EMG signal, the EMG patch has to run the empirical mode decomposition algorithm. A two-electrode circuit was designed to measure the EMG signal. The maximum power consumption of the EMG patch was about 39.5 mAh. In order to verify that the real-time MF values measured by the EMG patch were close to the off-line MF values measured by the computer system, we used the root-mean-square value to estimate the difference in the real-time MF values and the off-line MF values. There were 20 participants that rode an exercise bicycle at different speeds. Their EMG signals were recorded with an EMG patch and a physiological measurement system at the same time. Every participant rode the exercise bicycle twice. The averaged root-mean-square values were 2.86 ± 0.86 Hz and 2.56 ± 0.47 Hz for the first and second time, respectively. Moreover, we also developed an application program implemented on a smart phone to display the participants' muscle-fatigue conditions and information while exercising. Therefore, the EMG patch designed in this study could monitor the muscle-fatigue conditions to avoid sport injuries while exercising.