Projects:2016s2-240 Electromyographic Signal Processing for Controlling an Exoskeleton

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Introduction

Background

The exoskeleton is a wearable device which can reduce physiotherapist intervention but ensure the efficacy of rehabilitation in the medical and health area, which can also help soldiers fight better because it can help them get better protection and carry more weapons and equipment in the military area. Recent years, researchers from around the world are working hard so that the exoskeleton can be widely used in practice.

EMG signal is a bio-medical signal which generated from muscle activities. EMG signal originates from motor neurons in the spinal cord, which is the part of the central nervous system. Motor neurons are in the cell body; their axons extend to the muscle fiber and couples fiber at the end plate area. Muscle movement is controlled by awareness, when the brain is excited and downward conduction, the somatosensory and dendrites of the motor neurons in the central nervous system generate electrical impulses under the stimulation from synapses. These electrical impulses are along the neurons of the axon conduction to the peripheral nerve and muscle contact. When the motor nerve contacts the muscle, the axon branches to many fibers, and each branch ends in the muscle fibers to form synapses, which are called motor end plate. The generated electrical impulses are broadcasting along muscle fibers, which cause a serious change inside muscle fibers, and then produce a contraction of muscle fibers. Therefore, a large number of muscle fibers produce muscle contraction. It can be seen that the spread of action potential of muscle fibers leads to the muscle contraction, while the spread of electrical signals in human soft tissue results in the current field, and the potential difference is represented between the detection electrodes, which is EMG signal [1]. Usually, the amplitude of EMG signal is between 50μV and 5mV. The bandwidth is of EMG signal is from 0—500Hz.

SEMG signal is a bioelectric signal released from the neuromuscular activity recorded by the electrode from the surface of the human skeletal muscle. Different muscle movement patterns are caused by different muscle contractions, and their accompanying SEMG signals are different. It can be used to research or detect the muscle bioelectric activity to determine muscle system skills as well as morphological changes and contribute to neuromuscular system research or provide diagnosis [2]. The frequency range of the SEMG signal is from 0 to 2000Hz, but from the perspective of intensity, SEMG signals are mainly distributed between 20 and 500Hz.

Aim

This project was based on signal processing and focused on developing algorithms for feature extraction and classification to control lower limbs of an exoskeleton. The feature extracted from the recorded EMG signal will be compared to the previous training recordings to identify what kind of movement the muscles are trying to implement.

The project built up a system that has following contents: 1) Surface EMG signals recording. 2) Surface EMG signal denoising. 3) Surface EMG feature extraction. 4) Feature comparing and recognizing with the help of previous data.

The outcome of the project is a control system that extracts the feature of the input EMG signal and then delivers the muscle state as an output to the motor control of the exoskeleton. The system is accurate and fast in real-time processing.

Figure 2 System breakdown

Approach

In this project, three algorithms are used to analyze the EMG signal.

Team members

Hanzhi Wang & Junyao Wang

Supervisiors

A. Prof. Mathias Baumert

Dr. Tien-fu Lu