Sensors, Vol. 20, Pages 6486: A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects
Sensors, Vol. 20, Pages 6486: A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects
Sensors doi: 10.3390/s20226486
Authors:
Martin Khannouz
Tristan Glatard
This paper evaluates data stream classifiers from the perspective of connected devices, focusing on the use case of Human Activity Recognition. We measure both the classification performance and resource consumption (runtime, memory, and power) of five usual stream classification algorithms, implemented in a consistent library, and applied to two real human activity datasets and three synthetic datasets. Regarding classification performance, the results show the overall superiority of the Hoeffding Tree, the Mondrian forest, and the Naïve Bayes classifiers over the Feedforward Neural Network and the Micro Cluster Nearest Neighbor classifiers on four datasets out of six, including the real ones. In addition, the Hoeffding Tree and—to some extent—the Micro Cluster Nearest Neighbor, are the only classifiers that can recover from a concept drift. Overall, the three leading classifiers still perform substantially worse than an offline classifier on the real datasets. Regarding resource consumption, the Hoeffding Tree and the Mondrian forest are the most memory intensive and have the longest runtime; however, no difference in power consumption is found between classifiers. We conclude that stream learning for Human Activity Recognition on ...
Source: Sensors - Category: Biotechnology Authors: Martin Khannouz Tristan Glatard Tags: Article Source Type: research