A study of the adaptive gesture interface for the severely physically handicapped
In order to devise new systems of input for users with various motor disabilities, Yoda and his team first had to gather data on the sorts of gestures they could create. To document these accurately, Yoda's team monitored a pool of disabled volunteers to collect movement data for classification and analysis. Yoda chose participants that represented a wide range of potential disabilities. This meant many gestures of different types and from different parts of the body could be acquired and analysed. These data were classified according to body part and used to develop a modulised gesture recognition engine.
In addition to gathering important data, Yoda is also trying to optimise the research process using relatively cheap motion detection cameras. It is essential that any system eventually employed is at least accessible to the majority of the target disabled users. Yoda and his team have previously developed an interface based on head gestures for individuals with severe cerebral palsy to circumvent their inability to operate their motorised wheelchairs.
Keywords: CEREBRAL PALSY; GESTURES; HEAD GESTURES; KINECT; MODULISED GESTURE RECOGNITION ENGINE; MOTION DETECTION CAMERAS; MOTION SENSORS; MOTION-SENSING DEVICES; MOTOR DISABILITIES; MOTOR DYSFUNCTION; MOTORISED WHEELCHAIRS; MOVEMENT DATA; PHYSICAL DEVICE; WIDER RANGE OF DETECTION
Document Type: Research Article
Publication date: June 1, 2018
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