| 研究実績の概要 |
Wearable computing is an important technology in health care. We developed a natural and light weight finger-worn interface to identify user gestures and activities for long-term life support.Three key issues are solved as follows.
Segmentation of gesture sequence is a precondition in wearable computing. We proposed an adaptive threshold-based method based on Bayes Decision Theory and designed an adaptive mechanism to segment short-duration, ambiguous, and individually different gestures. The method can obtain high segmentation precision/recall and user-dependent adaptability with low computation complexity.
Recognition is the resolution of wearable computing. We proposed a pre-classification HMM method that can reduce recognition complexity by dividing gesture vocabularies into groups, maintain or even improve recognition accuracy by the adaptive adjustment of HMMs for different groups.
Application is the final goal of wearable computing. We employed only one finger-worn device to detect ten daily activities. To reflect realistic life aspects, a weight sequence alignment approach is proposed to analyze the detected activity sequences and attributes of each activity. The method can provide more detailed and realistic information of users' living for discover of potential health problem.
In summary, our finger-worn interface can detect finger/hand gestures and identify users’ daily activities. It has many potential applications like appliance control and health monitoring, which is especially useful for health care of elderly person in the aging society.
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