  Sonomyography (SMG) is a novel human-machine interface that controls
upper-limb prostheses by monitoring forearm muscle activity using ultrasonic
imaging. SMG has been investigated for controlling upper-limb prostheses during
the last two decades. The results show that this method, in combination with
artificial intelligence, can classify different hand gestures with an accuracy
of more than 90%, making it a great alternative control system compared to
electromyography (EMG). However, up to now there are few reports of a system
integrating SMG together with a prosthesis for testing on amputee subjects to
demonstrate its capability in relation to daily activities. In this study, we
developed ProRuka, a novel low-cost 6-degree-of-freedom prosthetic hand
integrated with the control provided by a SMG system with a wearable ultrasound
imaging probe. The classification of hand gestures using different machine
learning classification/regression algorithms including KNN, nearest neighbor
regression, random forest, decision tree classifier, decision tree regression,
support vector regression and support vector machine in combination with a
transfer learning model (VGG16) was first evaluated off-line to determine its
reliability and precision. Additionally, the developed controlling system were
evaluated on two amputees, in real-time experiments using a variety of hand
function test kits. The results from an off-line study including ten healthy
participants indicated that nine different hand motions can be classified with
a success rate of 100%. In addition, the hand function test in real time (using
4 different hand gestures) confirmed that the designed prosthesis with the SMG
controlling system can assist amputees to perform a variety of hand movements
needed in daily activities.
