Using speech for mode selection in control of multifunctional myoelectric prostheses

Abstract

Electromyogram (EMG) recorded from residual muscles of limbs is considered as suitable control information for motorized prostheses. However, in case of high-level amputations, the residual muscles are usually limited, which may not provide enough EMG for flexible control of myoelectric prostheses with multiple degrees of freedom of movements. Here, we proposed a control strategy, where the speech signals were used as additional information and combined with the EMG signals to realize more flexible control of multifunctional prostheses. By replacing the traditional "sequential mode-switching (joint-switching)", the speech signals were used to select a mode (joint) of the prosthetic arm, and then the EMG signals were applied to determine a motion class involved in the selected joint and to execute the motion. Preliminary results from three able-bodied subjects and one transhumeral amputee demonstrated the proposed strategy could achieve a high mode-selection rate and enhance the operation efficiency, suggesting the strategy may improve the control performance of commercial myoelectric prostheses.

Efficient EMG Pattern Classification-based Control Interface for a Low-Cost Sensor Fusion Controlled Exoskeleton Glove

“…Research teams at various institutions are working on control strategies for assistive devices utilizing several different sensing technologies. Many of these strategies involve electromyography (EMG) [8][9][10][11][12][13], computer vision (CV) [8,10,13], and/or voice control [11,12].…”

“…In [11], the authors used voice control to select which joint would be controlled, and EMG to control the selected joint. The voice control would select between hand, wrist, and elbow joint modes.…”

“…Some of the devices in previous research require expensive equipment such as eye tracking sensors [8] and high-density EMG arrays [9] which can cost hundreds or thousands of dollars USD. Others are implemented on laptop or desktop computers, limiting the range of these devices [8][9][10][11]13]. Unfortunately, this causes these devices to still be inaccessible to low-income patients or unable to assist with some tasks of daily living.…”