A Method for the Control of Multigrasp Myoelectric Prosthetic Hands

Abstract

This paper presents the design and preliminary experimental validation of a multigrasp myoelectric controller. The described method enables direct and proportional control of multigrasp prosthetic hand motion among nine characteristic postures using two surface electromyography electrodes. To assess the efficacy of the control method, five nonamputee subjects utilized the multigrasp myoelectric controller to command the motion of a virtual prosthesis between random sequences of target hand postures in a series of experimental trials. For comparison, the same subjects also utilized a data glove, worn on their native hand, to command the motion of the virtual prosthesis for similar sequences of target postures during each trial. The time required to transition from posture to posture and the percentage of correctly completed transitions were evaluated to characterize the ability to control the virtual prosthesis using each method. The average overall transition times across all subjects were found to be 1.49 and 0.81 s for the multigrasp myoelectric controller and the native hand, respectively. The average transition completion rates for both were found to be the same (99.2%). Supplemental videos demonstrate the virtual prosthesis experiments, as well as a preliminary hardware implementation.

EMG-based decoding of grasp gestures in reaching-to-grasping motions

“…Previous studies [17,19,26,36], presented different approaches for mapping EMG signals to reaching and grasping motions according to object's features and locations and during static or dynamic gestures. In these approaches, the system is trained as the subject is asked to perform a grasp type, with or without holding an object, and stay there for a few seconds.…”

“…This multidimensional reduction may be accomplished through the use of postural synergies [16] , corresponding to a number of hand postures that humans combine when grasping everyday life objects. Several works propose to exploit a mapping between upper limb EMG signals and hand postures [17][18][19][20], as strategy to control the large number of the hand's degrees of freedom. However, in these approaches only the grasping phase, i.e.…”

Design and Validation of a Morphing Myoelectric Hand Posture Controller Based on Principal Component Analysis of Human Grasping

“…All the metrics were based on the postural envelope. The postural envelope was defined as 25% of the total range of motion of each joint [23]. If a commanded joint angle was within the postural envelope, then that joint was considered to be controlled accurately.…”

Local Band Spectral Entropy Based on Wavelet Packet Applied to Surface EMG Signals Analysis

“…Surface electromyogram (EMG) activities have been reported that can represent the inherent characteristics and movement intention in motor-control systems, which are widely used in the field of prosthesis control [1,2], rehabilitation [3,4], muscle fatigue analysis [5,6] and clinical diagnosis [7,8]. An accurate and efficient analytical method, therefore, is of great significance to differentiate the motor patterns and explore the potential mechanism for motor control and function evaluation.…”