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Register a new userMultimodal Sensing and Interaction for a Robotic Hand Orthosis
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Added by
Sangwoo Park
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Wearable robotic hand rehabilitation devices can allow greater freedom and flexibility than their workstation-like counterparts. However, the field is generally lacking effective methods by which the user can operate the device: such controls must be effective, intuitive, and robust to the wide range of possible impairment patterns. Even when focusing on a specific condition, such as stroke, the variety of encountered upper limb impairment patterns means that a single sensing modality, such as electromyography (EMG), might not be sufficient to enable controls for a broad range of users. To address this significant gap, we introduce a multimodal sensing and interaction paradigm for an active hand orthosis. In our proof-of-concept implementation, EMG is complemented by other sensing modalities, such as finger bend and contact pressure sensors. We propose multimodal interaction methods that utilize this sensory data as input, and show they can enable tasks for stroke survivors who exhibit different impairment patterns. We believe that robotic hand orthoses developed as multimodal sensory platforms with help address some of the key challenges in physical interaction with the user.
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In order to provide therapy in a functional context, controls for wearable orthoses need to be robust and intuitive. We have previously introduced an intuitive, user-driven, EMG based orthotic control, but the process of training a control which is robust to concept drift (changes in the input signal) places a substantial burden on the user. In this paper, we explore semi-supervised learning as a paradigm for wearable orthotic controls. We are the first to use semi-supervised learning for an orthotic application. We propose a K-means semi-supervision and a disagreement-based semi-supervision algorithm. This is an exploratory study designed to determine the feasibility of semi-supervised learning as a control paradigm for wearable orthotics. In offline experiments with stroke subjects, we show that these algorithms have the potential to reduce the training burden placed on the user, and that they merit further study.
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