A Study on Hyperparameters Configurations for an Efficient Human Activity Recognition System

TL;DR

This paper investigates how hyperparameter tuning in a kNN-based Human Activity Recognition system affects accuracy, energy efficiency, and user adaptation, highlighting the importance of automatic hyperparameter adjustment for personalized and resource-efficient HAR applications.

Contribution

It provides an extensive analysis of hyperparameters in a kNN HAR system and demonstrates the benefits of automatic hyperparameter adaptation for improved performance and efficiency.

Findings

Hyperparameter configurations significantly influence accuracy and energy consumption.

Adaptive hyperparameter tuning improves user and activity recognition performance.

System can be optimized for different device constraints and user needs.

Abstract

Human Activity Recognition (HAR) has been a popular research field due to the widespread of devices with sensors and computational power (e.g., smartphones and smartwatches). Applications for HAR systems have been extensively researched in recent literature, mainly due to the benefits of improving quality of life in areas like health and fitness monitoring. However, since persons have different motion patterns when performing physical activities, a HAR system must adapt to user characteristics to maintain or improve accuracy. Mobile devices, such as smartphones, used to implement HAR systems, have limited resources (e.g., battery life). They also have difficulty adapting to the device's constraints to work efficiently for long periods. In this work, we present a kNN-based HAR system and an extensive study of the influence of hyperparameters (window size, overlap, distance function, and the value of k) and parameters (sampling frequency) on the system accuracy, energy consumption, and inference time. We also study how hyperparameter configurations affect the model's user and activity performance. Experimental results show that adapting the hyperparameters makes it possible to adjust the system's behavior to the user, the device, and the target service. These results motivate the development of a HAR system capable of automatically adapting the hyperparameters for the user, the device, and the service.