  Human Activity Recognition (HAR) is one of the key applications of health
monitoring that requires continuous use of wearable devices to track daily
activities. State-of-the-art works using wearable devices have been following
fog/cloud computing architecture where the data is classified at the mobile
phones/remote servers. This kind of approach suffers from energy, latency, and
privacy issues. Therefore, we follow edge computing architecture where the
wearable device solutions provide adequate performance while being energy and
memory-efficient. This paper proposes an Adaptive CNN for energy-efficient HAR
(AHAR) suitable for low-power edge devices. AHAR uses a novel adaptive
architecture that selects a portion of the baseline architecture to use during
the inference phase. 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.
