Energy Efficient Smartphone-Based Users Activity Classification

Abstract

Nowadays most people carry a smartphone with built-in sensors (e.g., accelerometers, gyroscopes) capable of providing useful data for Human Activity Recognition (HAR). Machine learning classification methods have been intensively researched and developed for HAR systems, each with different accuracy and performance levels. However, acquiring sensor data and executing machine learning classifiers require computational power and consume energy. As such, a number of factors, such as inadequate preprocessing, can have a negative impact on the overall HAR performance, even on high-end handheld devices. While high accuracy can be extremely important in some applications, the device’s battery life can be highly critical to the end-user. This paper is focused on the k-nearest neighbors’ algorithm (kNN), one of the most used algorithms in HAR systems, and research and develop energy-efficient implementations for mobile devices. We focus on a kNN implementation based on Locality-Sensitive Hashing (LSH) with a significant positive impact on the device’s battery life, fully integrated into a mobile HAR Android application able to classify human activities in real-time. The proposed kNN implementation was able to achieve execution time reductions of 50% over other versions of kNN with average accuracy of 96.55% when considering 8 human activities.