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

Abstract

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.

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

“…Li et al have proposed a lower-limb motion classification method for the evaluation of hemiparetic patients using IMU and EMG signals, demonstrating their potential in predicting the lower limb Brunnstrom stage for hemiparetic patients [ 82 ]. Meanwhile, Isezaki et al have designed a sock-type wearable sensor for estimating lower leg muscle activity using distal EMG signals [ 83 ]: the sock-type EMG measurement systems can collect the target muscle electrical activity widely distributed around the shank.…”

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

“…Smart socks are not restricted to pressure sensor technology, though. Other types of smart socks comprise EMG sensors to determine the activity of leg muscles around the ankle [18], in order to assess the risk of accidents or the health condition, while others are equipped with more types of sensors for determining the overall score of wellbeing [19] or health disorders [20]. Smart socks are easy to wear, minimizing the discomfort for the subjects that use them [17].…”

Advances and current challenges in non‐invasive wearable sensors and wearable biosensors—A mini‐review

“…(f) WBS for ECG measurements (Lee, Ha, et al, 2018) (reproduced under the terms of the CC BY-NC licence (Lee, Ha, et al, 2018) Copyright © 2018, Springer Nature). (g) Tension biosensors for subtle human movements like swallowing (reproduced with permission (Sun et al, 2019) (Isezaki et al, 2019) Copyright 2019, the authors, sensors, MDPI). (m) Images of the CNT-based glove biosensors reproduced under the terms of the CC BY-NC licence (Luo et al, 2018) Copyright 2018, the authors, sensors, MDPI).…”

“…(k) Gold electrodes inspired by the skin Images of the device under mechanical distortion (reproduced with permission (Ashley et al, 2019) © 2019 Elsevier B.V. All rights reserved). (l) sock‐type wearable sensor for estimating lower leg muscle activity with ten conductive‐fabric electrodes around the ankle (reproduced under the terms of the CC BY‐NC licence (Isezaki et al, 2019) Copyright 2019, the authors, sensors, MDPI). (m) Images of the CNT‐based glove biosensors reproduced under the terms of the CC BY‐NC licence (Luo et al, 2018) Copyright 2018, the authors, sensors, MDPI).…”