A Hybrid Accuracy- and Energy-Aware Human Activity Recognition Model in IoT Environment

Personalised health and fitness provide users with information regarding their wellbeing and an opportunity to inform healthcare services for better patient outcomes. Underpinning this industry sector is the need to establish human activity recognition (HAR) in a ubiquitous manner. For example, through the use of smartwatches and/or mobile phones gathering information such as heart rates, movement, and steps of a user. The engineering challenge is providing accurate, informative, and timely data without rapidly depleting the mobile device's battery life. This problem is compounded as a number of algorithms used to process such data require substantial, cloud-based resources, to achieve higher accuracy. Therefore, a balance is required between battery depletion, accuracy of data, and timely delivery of results through a mixture of cloud and local algorithmic execution. In this article, we propose AE-HAR (Accuracy and Energy Aware-HAR) model that delivers engineered solutions which approach optimal combinations in the consideration of energy consumption, accuracy, and timeliness of results. AE-HAR introduces a light-weight machine learning on-device component identifying the probabilistic accuracy of data together with energy consumption identification requirements. A heuristic is then adopted to determine if cloud-enabled calculations are required while including possible performance costs related to the analysis of networking infrastructures. Our model is validated in a real-world environment through experimentation that demonstrates accuracy in excess of 93% and energy consumption savings in excess of 94%.

Automated summary

Personalised health and fitness provide users with information regarding their wellbeing and an opportunity to inform healthcare services for better patient outcomes. AE-HAR is a model that balances battery depletion, data accuracy, and timely delivery of results in human activity recognition (HAR). It incorporates a lightweight machine learning component and cloud-based calculations to achieve high accuracy and energy consumption savings.