Automated Precision Tuning in Activity Classification Systems: A Case Study

Abstract

The greater availability and reduction in production cost make wearable IoT platforms perfect candidates to continuously monitor people at risk, like elderly people. In particular these platforms, along with the use of artifical intelligence algorithms, can be exploited to detect and monitor people's activities, in particular potentially harmful situations, such as falling. However, wearable devices have limited computational power and battery life. We optimize a situation-recognition application via the well-known precision tuning practice using a dedicated state-of-the-art toolchain. After the optimization we evaluate how the reduced-precision version better fits the use case of limited-resources platforms, such as wearable devices. In particular, we achieve over 500% of speedup in execution time, and consume about 6 times less energy to carry out the classification.