  Human Activity Recognition (HAR) is one of the key applications of health
monitoring that requires continuous use of wearable devices to track daily
activities. This paper proposes an Adaptive CNN for energy-efficient HAR (AHAR)
suitable for low-power edge devices. Unlike traditional early exit architecture
that makes the exit decision based on classification confidence, AHAR proposes
a novel adaptive architecture that uses an output block predictor to select a
portion of the baseline architecture to use during the inference phase.
Experimental results show that traditional early exit architectures suffer from
performance loss whereas our adaptive architecture provides similar or better
performance as the baseline one while being energy-efficient. We validate our
methodology in classifying locomotion activities from two datasets- Opportunity
and w-HAR. Compared to the fog/cloud computing approaches for the Opportunity
dataset, our baseline and adaptive architecture shows a comparable weighted F1
score of 91.79%, and 91.57%, respectively. For the w-HAR dataset, our baseline
and adaptive architecture outperforms the state-of-the-art works with a
weighted F1 score of 97.55%, and 97.64%, respectively. Evaluation on real
hardware shows that our baseline architecture is significantly energy-efficient
(422.38x less) and memory-efficient (14.29x less) compared to the works on the
Opportunity dataset. For the w-HAR dataset, our baseline architecture requires
2.04x less energy and 2.18x less memory compared to the state-of-the-art work.
Moreover, experimental results show that our adaptive architecture is 12.32%
(Opportunity) and 11.14% (w-HAR) energy-efficient than our baseline while
providing similar (Opportunity) or better (w-HAR) performance with no
significant memory overhead.
