Proposed Article Title
Many tetraplegics must wear wrist braces to support paralyzed wrists and hands. However, current wrist orthoses have limited functionality to assist a person’s ability to perform typical activities of daily living other than a small pocket to hold utensils. To enhance the functionality of wrist orthoses, gesture recognition technology can be applied to control mechatronic tools attached to a novel fabricated wrist brace. Gesture recognition is a growing technology for providing touchless human-computer interaction that can be particularly useful for tetraplegics with limited upper-extremity mobility. In this study, three gesture recognition models were compared—two dynamic time-warping models and a hidden Markov model—in terms of their classification accuracy of gestures from a gesture lexicon known to be accessible to tetraplegics. Gesture data from participants with and without spinal cord injuries was collected using a prototype wrist orthosis. Leave-one-subject-out cross-validation was used to develop a user-independent gesture recognition library. The trained models were then tested using a combination of data from both populations and data separated by population. The classification accuracy and classification time were computed and compared to determine the optimal gesture recognition model.
"Comparison of Machine Learning Models: Gesture Recognition Using a Multimodal Wrist Orthosis for Tetraplegics,"
The Journal of Purdue Undergraduate Research:
Vol. 10, Article 14.
Available at: https://docs.lib.purdue.edu/jpur/vol10/iss1/14