Application of electromyography (EMG) and electrical impedance myography (EIM) incontrolling a bionic hand prosthesis: A comparison of efficiency and precision EMG andEIM in algorithms for controlling bionic prostheses

<b>Aim:</b> The study aimed to develop an innovative algorithm for controlling a bionic hand prosthesis based on simultaneous analysis of electromyography (EMG) and electrical impedance myography (EIM) signals using continuous wavelet transform (CWT). The efficiency of both methods in motion classification was analyzed and compared, and the impact of electrode placement on classification accuracy was evaluated. <br><b>Methods:</b> The BIOPAC MP36R system was employed to acquire EMG and EIM signals during six predefined hand movements from two distinct electrode placements. A total of 600 measurements were collected for each type of signal. Data were subjected to preliminary, time-domain, frequency-domain, and time-frequency analyses to extract key features for each movement. The developed classification algorithm utilized a k-nearest neighbors (k-NN) approach based on extracted signal features. The algorithm was trained on data obtained during the first measurement session and validated on an independent dataset. <br><b>Results:</b> The results demonstrated that EIM signals outperformed EMG in motion classification accuracy, allowing for the differentiation of flexion and extension at the preliminary analysis stage. EIM achieved 100% accuracy in 9 out of 12 analyzed cases, with a minimum accuracy of 84%. In contrast, EMG signals enabled only basic movement detection without distinguishing between flexion and extension during the preliminary analysis phase. Electrode placement significantly affected classification performance, with differences exceeding 82% depending on placement. <br><b>Conclusions:</b> The findings indicate the considerable advantage of EIM over traditional EMG in controlling bionic hand prostheses, opening new possibilities for more precise and intuitive control. The study also highlights the importance of optimal electrode placement to enhance the functionality of future bionic prostheses.