  For lower arm amputees, prosthetic hands promise to restore most of physical
interaction capabilities. This requires to accurately predict hand gestures
capable of grabbing varying objects and execute them timely as intended by the
user. Current approaches often rely on physiological signal inputs such as
Electromyography (EMG) signal from residual limb muscles to infer the intended
motion. However, limited signal quality, user diversity and high variability
adversely affect the system robustness. Instead of solely relying on EMG
signals, our work enables augmenting EMG intent inference with physical state
probability through machine learning and computer vision method. To this end,
we: (1) study state-of-the-art deep neural network architectures to select a
performant source of knowledge transfer for the prosthetic hand, (2) use a
dataset containing object images and probability distribution of grasp types as
a new form of labeling where instead of using absolute values of zero and one
as the conventional classification labels, our labels are a set of
probabilities whose sum is 1. The proposed method generates probabilistic
predictions which could be fused with EMG prediction of probabilities over
grasps by using the visual information from the palm camera of a prosthetic
hand. Our results demonstrate that InceptionV3 achieves highest accuracy with
0.95 angular similarity followed by 1.4 MobileNetV2 with 0.93 at ~20% the
amount of operations.
