In recent years, researchers have begun to focus on wearable computer and sensor interfaces. One major benefit provided by wearable intelligent devices is that they are in close proximity to the users so that human data such as motion and physiological information can be obtained and analyzed anywhere at anytime conveniently. One area for wearable devices has, however, remained relatively unexplored-namely, the design and implementation of sensor and computer-equipped intelligent shoes. Therefore, we propose the development of just such a system.
The on-going miniaturization revolution in electronics, sensor, and battery technologies, driven largely by the cell phone and hand-held device markets, has made possible an intelligent-shoe implementation. Along with these hardware advances, progress in human data modeling and machine learning algorithms have also made possible the analysis and interpretation of complex, multi-channel sensor data.
The intelligent shoe consists of a microcontroller, a suite of sensors for acquiring physiological, motion and force information, and a wireless transmitter-receiver set. The data gathered from the intelligent shoe-integrated platform will be further analyzed in order to meet three primary goals:
(1) real-time health and gait monitoring;
(2) real-time motion (activity) identification;
(3) real-time user localization.
We will propose various intelligent learning algorithms to achieve the modeling of human motion and physiological data. The proposed research opens up tremendous new human-computer interface possibilities, resulting in rich academic research contents and potential product lines in consumer electronics and multimedia industries.
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