Network-Level Power-Performance Trade-Off in Wearable Activity Recognition

Abstract

Wearable gesture recognition enables context aware applications and unobtrusive HCI. It is realized by applying machine learning techniques to data from on-body sensor nodes. We present an gesture recognition system minimizing power while maintaining a run-time application defined performance target through dynamic sensor selection. Compared to the non managed approach optimized for recognition accuracy (95% accuracy), our technique can extend network lifetime by 4 times with accuracy >90% and by 9 times with accuracy >70%. We characterize the approach and outline its applicability to other scenarios.