Self-Powered Gesture Recognition with Ambient Light

We present a self-powered module for gesture recognition that utilizes small, low-cost photodiodes for both energy harvesting and gesture sensing. Operating in the photovoltaic mode, photodiodes harvest energy from ambient light. In the meantime, the instantaneously harvested power from individual photodiodes is monitored and exploited as a clue for sensing finger gestures in proximity. Harvested power from all photodiodes are aggregated to drive the whole gesture-recognition module including a micro-controller running the recognition algorithm. We design robust, lightweight algorithm to recognize finger gestures in the presence of ambient light fluctuations. We fabricate two prototypes to facilitate user’s interaction with smart glasses and smart watches. Results show 99.7%/98.3% overall precision/recall in recognizing five gestures on glasses and 99.2%/97.5% precision/recall in recognizing seven gestures on the watch. The system consumes 34.6 µW/74.3 µW for the glasses/watch and thus can be powered by the energy harvested from ambient light. We also test system’s robustness under various light intensities, light directions, and ambient light fluctuations. The system maintains high recognition accuracy (\u3e 96%) in all tested settings

Human behavioural analysis with self-organizing map for ambient assisted living

This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints