A myoelectric prosthetic hand with muscle synergy–based motion determination and impedance model–based biomimetic control

Abstract

Prosthetic hands are prescribed to patients who have suffered an amputation of the upper limb due to an accident or a disease. This is done to allow patients to regain functionality of their lost hands. Myoelectric prosthetic hands were found to have the possibility of implementing intuitive controls based on operator’s electromyogram (EMG) signals. These controls have been extensively studied and developed. In recent years, development costs and maintainability of prosthetic hands have been improved through three-dimensional (3D) printing technology. However, no previous studies have realized the advantages of EMG-based classification of multiple finger movements in conjunction with the introduction of advanced control mechanisms based on human motion. This paper proposes a 3D-printed myoelectric prosthetic hand and an accompanying control system. The muscle synergy–based motion-determination method and biomimetic impedance control are introduced in the proposed system, enabling the classification of unlearned combined motions and smooth and intuitive finger movements of the prosthetic hand. We evaluate the proposed system through operational experiments performed on six healthy participants and an upper-limb amputee participant. The experimental results demonstrate that our prosthetic hand system can successfully classify both learned single motions and unlearned combined motions from EMG signals with a high degree of accuracy. Furthermore, applications to real-world uses of prosthetic hands are demonstrated through control tasks conducted by the amputee participant.

Real-Time Hand Gesture Recognition Using Temporal Muscle Activation Maps of Multi-Channel Semg Signals

“…The CNN models were evaluated in a real-time study containing a random sequence of 150 hand gestures, ensuring that each gesture type would have an equal number of repetitions. Table 1 reports the classification accuracies obtained from the proposed real-time gesture recognition algorithm and compare them with state-of-the-art methods described by Crepin et al [9] and Furui et al [10].…”

“…A number of studies have been conducted to accurately classify hand gestures from the pre-recorded sEMG signals [5,7,8]. However, only a few studies [9,10,11] have focused on real-time hand gesture recognition using sEMG signals from the forearm. Most methods [7,9,10,12] rely on binning of the sEMG signals, computing a set of features (mean absolute value, waveform length, etc.)…”

Muscle Synergy and Musculoskeletal Model-Based Continuous Multi-Dimensional Estimation of Wrist and Hand Motions

“…In SLRM2, it also increased other synergy coefficients albeit, relatively, by a small amount when a strong grip was assumed. is result could support extracting a unique synergy for each motion [31]. e FDS and FDP are flexor muscles of the fingers from an anatomical point of view.…”

Progress in wearable electronics/photonics—Moving toward the era of artificial intelligence and internet of things

“…Wearable electronics/photonics are rapidly emerging in diversified research areas for their promising future in broad applications, for example, robotics, 62‐64 medical devices, 65,66 environmental monitoring, 67 human‐machine interfaces (HMIs), 51,68 healthcare, 69 and so on. This section summarizes the various aspects of general wearable electronics and photonics in terms of materials, transducing mechanisms, and applications.…”