A context-aware smart seat

• Published in: 2011 4th IEEE International Workshop on Advances in Sensors and Interfaces pp. 104 - 109
• Main Authors: Benocci, M., Farella, E., Benini, L.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2011
• Subjects: Electromyography; Electrooculography; Hidden Markov models; Magnetic resonance imaging; Magnetometers; Microphones; Ultrasonic imaging
• ISBN: 9781457706233; 1457706237
• DOI: 10.1109/IWASI.2011.6004697

This paper reports the characterization and test of an embedded implementation of the k-Nearest Neighbor (kNN) classifier in a resource constrained device applied to a seat to capture user postures and combine them with contextual information about the user. The embedded platform is a wearable multi-sensor device based on the 32 bit ARM Cortex M3 architecture, capable of data processing (sampling, windowing, filtering, Fast Fourier Transform) from 9 different sensors. The system, applied to the seat, identifies 6 different user postures - adopted while she/he is working on the desk - and fuses the result with the information available from other sensors worn by the user, collecting information about her/his activities and physiological state. The kNN classifier is evaluated in terms of required computational power and latency. 7 users have been monitored along 3 days. The posture recognition accuracy reaches 93.7%, it requires 9KB of memory and introduces a latency of 950usec, satisfying strict real-time requirements.