A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN

Abstract

Recent developments in implantable technology, such as high-density recordings, wireless transmission of signals to a prosthetic hand, may pave the way for intramuscular electromyography (iEMG)-based myoelectric control in the future. This study aimed to investigate the real-time control performance of iEMG over time. A novel protocol was developed to quantify the robustness of the real-time performance parameters. Intramuscular wires were used to record EMG signals, which were kept inside the muscles for five consecutive days. Tests were performed on multiple days using Fitts’ law. Throughput, completion rate, path efficiency and overshoot were evaluated as performance metrics using three train/test strategies. Each train/test scheme was categorized on the basis of data quantity and the time difference between training and testing data. An artificial neural network (ANN) classifier was trained and tested on (i) data from the same day (WDT), (ii) data collected from the previous day and tested on present-day (BDT) and (iii) trained on all previous days including the present day and tested on present-day (CDT). It was found that the completion rate (91.6 ± 3.6%) of CDT was significantly better (p < 0.01) than BDT (74.02 ± 5.8%) and WDT (88.16 ± 3.6%). For BDT, on average, the first session of each day was significantly better (p < 0.01) than the second and third sessions for completion rate (77.9 ± 14.0%) and path efficiency (88.9 ± 16.9%). Subjects demonstrated the ability to achieve targets successfully with wire electrodes. Results also suggest that time variations in the iEMG signal can be catered by concatenating the data over several days. This scheme can be helpful in attaining stable and robust performance.

Intramuscular EMG feature extraction and evaluation at different arm positions and hand postures based on a statistical criterion method

“…Following filtering, the process pertaining to windowing was done in order to eliminate the spectral leakages. Previous studies including Asghar et al 32 and Waris et al 31 have used 200 ms window for eliminating spectral leakages. Hence, we empirically selected 200 ms as the window length as per the literature.…”

“…An iEMG signal classification technique implemented over multiple days was proposed in Waris et al 31 The iEMG signals were classified with the aid of ANN with three performance metrics namely, completion rate, path efficiency, and overshoot being evaluated for determining the robustness of the proposed classification technique. 31 The ANN classifier was trained and tested on data collected over a span of 5 days. The results of Waris et al 31 exhibited that the proposed classification method can be utilized for multiday real-time iEMG signal classification.…”

“…The iEMG wire electrodes were made from Teflon-coated stainless steel with a diameter of 50 µm and were injected into each muscle with a sterilized 25-gauge hypodermic needle. 31 Three millimeter of wire was left exposed from the tip, while the rest of it was insulated. A moist wrist band was utilized as a reference.…”

“…Intramuscular EMG. In order to acquire the iEMG signals, custom made wire electrode pair by OT Bioelectronica 31 was inserted around 10-15 mm below the muscle fascia of forearm in the extensor and flexor muscles. Hence, using the pair of wire electrodes, iEMG was recorded from two muscles including the extensor and flexor muscles, with the extensor muscle on channel 1 and flexor muscle on channel 2.…”

Evaluation of windowing techniques for intramuscular EMG-based diagnostic, rehabilitative and assistive devices

“…Windowing is a process that divides recorded EMG signals into smaller parts to analyze fine characteristics of the signal. The windowing process is used in decomposition of EMG signals, and also to design myoelectric control schemes for rehabilitative and assistive devices [5][6][7]. Classification and identification of action potentials by virtue of individual motor units through analyzing interference pattern is called EMG signal decomposition.…”