Abstract-This paper proposes the use of posterioradapted class-based weighted decision fusion to effectively combine multiple accelerometer data for improving physical activity recognition. The cutting-edge performance of this method is benchmarked against model-based weighted fusion and class-based weighted fusion without posterior adaptation, based on two publicly available datasets, namely PAMAP2 and MHEALTH. Experimental results show that: 1) posterior-adapted class-based weighted fusion outperformed model-based and class-based weighted fusion; 2) decision fusion with two accelerometers showed statistically significant improvement in average performance compared to the use of a single accelerometer; 3) generally, decision fusion from three accelerometers did not show further improvement from the best combination of two accelerometers; and 4) a combination of ankle and wrist located accelerometers showed the best overall performance compared to any combination of two or three accelerometers.
In REF the authors combine the results of accelerometers placed at different body locations with model-based and class-based weighted decision fusion techniques and also propose a posterior adaptation of the class-based scheme.
Physical Activity Recognition Using Posterior-Adapted Class-Based Fusion of Multiaccelerometer Data
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics
Medicine Computer Science Engineering