  This paper evaluates data stream classifiers from the perspective of
connected devices, focusing on the use case of HAR. We measure both
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 to
three synthetic datasets. Regarding classification performance, results show an
overall superiority of the HT, the MF, and the NB classifiers over the FNN and
the Micro Cluster Nearest Neighbor (MCNN) classifiers on 4 datasets out of 6,
including the real ones. In addition, the HT, and to some extent MCNN, are the
only classifiers that can recover from a concept drift. Overall, the three
leading classifiers still perform substantially lower than an offline
classifier on the real datasets. Regarding resource consumption, the HT and the
MF 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 HAR on connected objects is challenged by two factors which
could lead to interesting future work: a high memory consumption and low F1
scores overall.
