The design of an activity recognition and monitoring system based on the eWatch, multi-sensor platform worn on different body positions, is presented in this paper. The system identifies the user's activity in realtime using multiple sensors and records the classification results during a day. We compare multiple time domain feature sets and sampling rates, and analyze the tradeoff between recognition accuracy and computational complexity. The classification accuracy on different body positions used for wearing electronic devices was evaluated.

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Advanced human-machine interfaces require improved embedded sensors that can seamlessly interact with the user. Voice-based communication has emerged as a promising interface for next generation mobile, automotive and hands-free devices. Presented here is such an audio front-end with Voice Activity Detection (VAD) hardware targeted for low-power embedded SoCs, featuring a 512 pt FFT, programmable filters, noise floor estimator and a decision engine which has been fabricated in 32 nm CMOS. The dual-, dual-frequency design allows the core datapath to scale to near-threshold voltage (NTV), where power consumption is less than 50 uW. At peak energy efficiency, the core can process audio data at 2.3 nJ/frame-a 9.4X improvement over nominal voltage conditions. Index Terms-Low-power sensor front-end, near operation, voice activity detection.