Analyzing Human Muscle State with Flexible Sensors

Abstract

Analyzing human muscle states has attracted extensive attention. EMG (electromyography) pattern recognition methods based on these works have been proposed for many years. However, uncomfortable wearing and high prices make it inconvenient for motion tracking and muscle analysis by using robotic arms and inertial sensors in daily life. In this study, we propose to use smart clothes integrated with flexible sensors to collect arm motion data, estimate the kinematic information of continuous arm motion, and predict the EMG signal of each arm muscle. Firstly, the neural network regression model integrated with the LSTM (long short-term memory) module is used to continuously estimate the sensor resistances collected by the smart clothes and the angles collected by Kinect. Then, six types of shoulder and elbow movements’ angles and the corresponding EMG signals of 5 subjects are preprocessed and aligned. The stacked regression model based on extremely randomized trees (extra trees) is used for regression. Our experimental results show that the average estimation absolute error from the sensor resistances to the joint angle is 3.45 degrees, and the absolute percentage error from the joint angle to the EMG signal is only 1.82%.

Wearable sensors for monitoring vital signals in sports and health: progress and perspective

“…The sensor can be fitted on any irregular surface, enabling accurate detection of heart rate during exerciset such as weightlifting [Figure 2(b)] (Wang et al , 2021). Chen et al demonstrated the textile-based pressure sensor which is capable of monitoring epidermal pulses and shows great potential to advance the standardization and modernization of pulse palpation in traditional Chinese medicine, as shown in Figure 2(c) (Chen et al , 2022a, 2022b).…”

A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors

“…The system performs feature extraction of mean work frequency, median work frequency, peak work frequency, and mean power in EMG of biceps femoris and bilateral lower extremity lateral gastrocnemius muscles, followed by classification of stroke patients and healthy adults using neural network models in machine learning algorithms with an accuracy of 80%. However, EMG or motion sensors alone are difficult to use for muscle status monitoring [ 65 ]. Park et al [ 66 ] combined EMG signals with plantar pressure signals to obtain gait, an important marker of disability, injury, and gait symmetry.…”