Exploring combinations of missing data complement for fault tolerant activity recognition
Ren Ohmura, Ryoma Uchida · 2014
Disrupting the transmission of sensor data due to sensor failure or connection loss significantly decrease accuracy in existing activity recognition techniques. We introduce an approach towards managing missing sensor data which operates at each step of the standard activity recognition, beginning with raw sensor data, feature calculation, classification, and result, as well as their combination methods. Our evaluation showed that the F1-score increased from 0.61 in the case of sensor data loss to 0.68 with the combination of all methods. Moreover, by selecting the combination of methods according to the failed sensor position, the F1-score increased to 0.69.