Power and accuracy trade-offs in sound-based context recognition systems
Pervasive and Mobile Computing
Towards implicit interaction by using wearable interaction device sensors for more than one task
Mobility '06 Proceedings of the 3rd international conference on Mobile technology, applications & systems
Energy efficient cooperative multimodal ambient monitoring
EuroSSC'10 Proceedings of the 5th European conference on Smart sensing and context
Proceedings of the 2nd Augmented Human International Conference
Balancing energy, latency and accuracy for mobile sensor data classification
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
ACM Transactions on Embedded Computing Systems (TECS)
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In this paper, we evaluate how the performance of a wearable context recognition system is affected by the sampling frequency and the resolution of the sensor signals used for the classification. We introduce our method for this evaluation and present the results for a widely studied activity recognition task: the classification of human modes of locomotion using body-worn acceleration sensors. With this example we show that both the sampling frequency and the resolution can be significantly reduced without much impact on the recognition performance. While many of the published approaches in this domain rely on higher sampling frequencies and signal resolutions, we show that good recognition performance can already be achieved with 20Hz and 2 bit resolution.