Abstract
Dexterous hand motion is critical for object manipulation. Electrophysiological studies of the hand are key to understanding its underlying mechanisms. High-density electromyography (HD-EMG) provides spatio-temporal information about the underlying electrical activity of muscles, which can be used in neurophysiological research, rehabilitation and control applications. However, existing EMG electrodes platforms are not muscle-specific, which makes the assessment of intrinsic hand muscles difficult. Muscle-specific flexible HD-EMG electrode arrays were developed to capture intrinsic hand muscle myoelectric activity during manipulation tasks. The arrays consist of 60 individual electrodes targeting 10 intrinsic hand muscles. Myoelectric activity was displayed as spatio-temporal amplitude maps to visualize muscle activation. Time-domain and temporal-spatial HD-EMG features were extracted to train cubic support vector machine machine-learning classifiers to classify the intended user motion. Experimental data was collected from 5 subjects performing a range of 10 common hand motions. Spatio-temporal EMG maps showed distinct activation areas correlated to the muscles recruited during each movement. The thenar muscle fiber conduction velocity (CV) was estimated to be at 4.7±0.3 m/s for all subjects. Hand motions were successfully classified and average accuracy for all subjects was directly related to spatial resolution based on the number of channels used as inputs; ranging from 74±4% when using only 5 channels and up to 92±2% when using 41 channels. Temporal-spatial features were shown to provide increased motion-specific accuracy when similar muscles were recruited for different gestures. Muscle-specific electrodes were capable of accurately recording HD-EMG signals from intrinsic hand muscles and accurately predicting motion. The muscle-specific electrode arrays could improve electrophysiological research studies using EMG decomposition techniques to assess motor unit activity and in applications involving the analysis of dexterous hand motions.
Highlights
T HE human hand is comprised of dozens of muscles, bones and nerves
Extrinsic muscles are not capable of coordinating a motion that permits a subject to grasp and hold objects, since the muscles are only capable of curling the fingers into flexion from the distal phalanges towards the palm [2]
This paper only focused on the activity of the thenar muscles to illustrate the ability to estimate conduction velocity (CV)
Summary
T HE human hand is comprised of dozens of muscles, bones and nerves. Coordinated motor function is essential for interacting with the world and performing complex manipulation tasks. The intrinsic and extrinsic muscles balance finger movements to create fluid movements [1]. Extrinsic muscles are not capable of coordinating a motion that permits a subject to grasp and hold objects, since the muscles are only capable of curling the fingers into flexion from the distal phalanges towards the palm [2]. Intrinsic hand muscles include the lumbrical, interossei (palmar and dorsal), thenar and hypothenar muscles, which work in conjunction with the extrinsic muscles to achieve normal position, guide stable motion and provide strength to the hand. Their exact contribution and activation during the manipulation of objects like pinching and gripping is not fully understood
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