Novel Method for Predicting Dexterous Individual Finger Movements by Imaging Muscle Activity Using a Wearable Ultrasonic System

Recently there have been major advances in the electro-mechanical design of upper extremity prosthetics. However, the development of control strategies for such prosthetics has lagged significantly behind. Conventional noninvasive myoelectric control strategies rely on the amplitude of electromyography (EMG) signals from flexor and extensor muscles in the forearm. Surface EMG has limited specificity for deep contiguous muscles because of cross talk and cannot reliably differentiate between individual digit and joint motions. We present a novel ultrasound imaging based control strategy for upper arm prosthetics that can overcome many of the limitations of myoelectric control. Real time ultrasound images of the forearm muscles were obtained using a wearable mechanically scanned single element ultrasound system, and analyzed to create maps of muscle activity based on changes in the ultrasound echogenicity of the muscle during contraction. Individual digit movements were associated with unique maps of activity. These maps were correlated with previously acquired training data to classify individual digit movements. Preliminary results using ten healthy volunteers demonstrated this approach could provide robust classification of individual finger movements with 98% accuracy (precision 96%-100% and recall 97%-100% for individual finger flexions). The change in ultrasound echogenicity was found to be proportional to the digit flexion speed (R2=0.9), and thus our proposed strategy provided a proportional signal that can be used for fine control. We anticipate that ultrasound imaging based control strategies could be a significant improvement over conventional myoelectric control of prosthetics.