Real-time myoelectric decoding of individual finger movements for a virtual target task

Abstract

This study presents the development of a myoelectric decoding algorithm capable of continuous online decoding of finger movements with the intended eventual application for use in prostheses for transradial amputees. The effectiveness of the algorithm was evaluated through controlling a multi-fingered hand in a virtual environment. Two intact limbed adult subjects were able to use myoelectric signals collected from 8 bipolar electrodes to control four fingers in real-time to touch and maintain contact with targets appearing at various points in the flexion space of the hand. In these tasks, subjects achieved accuracies of 94% when target regions extended +/- 11.5 degrees about a target angle and 81% when the target region extended only +/- 5.75 degrees about the target angle. The real-time virtual system provides a practical and economic way to develop and train algorithms and amputee subjects using dexterous prostheses.

Prosthetic Myoelectric Control Strategies: A Clinical Perspective

“…This has allowed increased movement patterns to be classified beyond just grip, to include wrist flexion and extension, forearm pronation and supination [29,30], humeral rotation [31] and ulnar and radial deviation at the wrist [32], with the current trend moving towards identifying muscle patterns to control individual fingers [33][34][35][36]. However, it is important to note that pattern recognition is only capable of classifying these movements in sequence and not simultaneously (Fig.…”

Using Augmented Reality Techniques to Simulate Myoelectric Upper Limb Prostheses

“…Smith et al [15], proposed the combination of continuous decoding of finger position, based on EMG signals, with a virtual prosthetic to evaluate controllability, in an online setting. In particular, it was investigated whether or not intact limbed subjects could exert control over individual fingers of this virtual prosthesis in target touching tasks that require both active movement as well as sustained contractions, at various locations in the flexion space of the fingers (Figure 4).…”

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

“…The number of EMG channels placed in a narrow uniform band/ring, which is proposed in the literature, is seven sensors (Du et al, 2006(Du et al, , 2010Mogk & Keir, 2003;Shyu et al, 2002) and eight sensors (Andrews et al, 2009;Khushaba & Kodagoda, 2012;Khushaba et al, 2013;Saponas et al, 2008;Smith et al, 2009). When multiple sets of sensors were placed around the forearm, reasonable EMG signals with useful information could be acquired from the armband even the sensors are only approximately placed (Saponas et al, 2008).…”

“…one pair across the ulnar border (Du et al, 2006(Du et al, , 2010Mogk & Keir, 2003;Shyu et al, 2002;Smith et al, 2008Smith et al, , 2009. In this scheme, however, there is no guarantee that electrodes are placed over the same muscles in all users.…”

“…(1) Lower forearm positions, i.e., around the wrist Tang, Liu, Lv, & Sun, 2012;You, Rhee, & Shin, 2010, 2011; (2) Upper forearm positions, i.e., below the elbow (Andews, Morin, & McLean, 2009;Benko et al, 2009;Du, Lin, Shyu, & Chen, 2010;Du, Shyu, & Hu, 2006;Khushaba & Kodagoda, 2012;Khushaba et al, 2013;Mogk & Keir, 2003;Saponas et al, 2008;Shyu, Chen, Tatn, & Hu, 2002;Smith, Huberdeau, Tenore, & Thakor, 2009;Smith et al, 2008;Tenore et al, 2007Tenore et al, , 2009). …”