Adaptive Grasp Control through Multi-Modal Interactions for Assistive Prosthetic Devices
This addresses the problem of improving assistive prosthetic devices for people with transradial amputations or wrist disarticulation, but it appears incremental as it builds on existing multi-modal sensing approaches without claiming a breakthrough.
The paper tackled the challenge of creating adaptive prosthetic hands that mimic human activities by developing algorithms that allow the device to adjust its behavior based on user preferences through multi-modal interactions, resulting in a platform evaluated with experiments to quantify performance, though no concrete numbers are provided in the abstract.
The hand is one of the most complex and important parts of the human body. The dexterity provided by its multiple degrees of freedom enables us to perform many of the tasks of daily living which involve grasping and manipulating objects of interest. Contemporary prosthetic devices for people with transradial amputations or wrist disarticulation vary in complexity, from passive prosthetics to complex devices that are body or electrically driven. One of the important challenges in developing smart prosthetic hands is to create devices which are able to mimic all activities that a person might perform and address the needs of a wide variety of users. The approach explored here is to develop algorithms that permit a device to adapt its behavior to the preferences of the operator through interactions with the wearer. This device uses multiple sensing modalities including muscle activity from a myoelectric armband, visual information from an on-board camera, tactile input through a touchscreen interface, and speech input from an embedded microphone. Presented within this paper are the design, software and controls of a platform used to evaluate this architecture as well as results from experiments deigned to quantify the performance.