Novel wearable EMG sensors based on nanowire technology

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

Lower leg muscle activity contributes to body control; thus, monitoring lower leg muscle activity is beneficial to understand the body condition and prevent accidents such as falls. Amplitude features such as the mean absolute values of electromyography (EMG) are used widely for monitoring muscle activity. Garment-type EMG measurement systems use electrodes and they enable us to monitor muscle activity in daily life without any specific knowledge and the installation for electrode placement. However, garment-type measurement systems require a high compression area around the electrodes to prevent electrode displacement. This makes it difficult for users to wear such measurement systems. A less restraining wearable system, wherein the electrodes are placed around the ankle, is realized for target muscles widely distributed around the shank. The signals obtained from around the ankle are propagated biosignals from several muscles, and are referred to as distal EMG signals. Our objective is to develop a sock-type wearable sensor for estimating lower leg muscle activity using distal EMG signals. We propose a signal processing method based on multiple bandpass filters from the perspectives of noise separation and feature augmentation. We conducted an experiment for designing the hardware configuration, and three other experiments for evaluating the estimation accuracy and dependability of muscle activity analysis. Compared to the baseline based on a 20-500 Hz bandpass filter, the results indicated that the proposed system estimates muscle activity with higher accuracy. Experimental results suggest that lower leg muscle activity can be estimated using distal EMG signals.

Non-Invasive Flexible and Stretchable Wearable Sensors With Nano-Based Enhancement for Chronic Disease Care

Advances in flexible and stretchable electronics, functional nanomaterials, and micro/nano manufacturing have been made in recent years. These advances have accelerated the development of wearable sensors. Wearable sensors, with excellent flexibility, stretchability, durability, and sensitivity, have attractive application prospects in the next generation of personal devices for chronic disease care. Flexible and stretchable wearable sensors play an important role in endowing chronic disease care systems with the capability of long-term and real-time tracking of biomedical signals. These signals are closely associated with human body chronic conditions, such as heart rate, wrist/neck pulse, blood pressure, body temperature, and biofluids information. Monitoring these signals with wearable sensors provides a convenient and non-invasive way for chronic disease diagnoses and health monitoring. In this review, the applications of wearable sensors in chronic disease care are introduced. In addition, this review exploits a comprehensive investigation of requirements for flexibility and stretchability, and methods of nano-based enhancement. Furthermore, recent progress in wearable sensors-including pressure, strain, electrophysiological, electrochemical, temperature, and multifunctional sensors-is presented. Finally, opening research challenges and future directions of flexible and stretchable sensors are discussed.

Estimating the lower leg muscle activity from distal biosignals around the ankles

Electromyogram signals (EMG) can be used not only to measure motions, but also to control devices such as exoskeleton robots. Sensor electrodes need to be placed on each muscle based on kinematics and anatomical characteristics. Wearable EMG measurement approach is also investigated in recent years. Electrodes are fixed to the clothes. In this paper, we propose a motion measurement method based on propagation characteristics of biopotential signal. An experiment with walking and plantar flexion motion as tasks. The results showed that the signals calculated from proposed method were comparable with that of conventional method. We confirmed that there were few individual differences for calculating the signals of tibialis anterior, gastrocnemius and peroneal muscles.

Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors

As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short calculation time (65.586 ms), making it a possible choice for pre-impact fall detection. The thorough quantitative comparison of the features and classifiers in this study supports the feasibility of a wireless, wearable sEMG sensor system for automatic activity monitoring and fall detection.