High accuracy human activity recognition using machine learning and wearable devices’ raw signals

Abstract

Human activity recognition (HAR) is vital in a wide range of real-life applications such as health monitoring of olderly people, abnormal behaviour detection and smart home management. HAR systems can employ smart human-computer interfaces and be parts of active, intelligent surveillance systems. The increasing use of high-tech mobile and wearable devices, such as smart phones, smart watches and smart bands, can be the key elements in building high accuracy models, as they can provide a tremendous number of signals. This research aims to develop and test a machine learning (ML) model, which can successfully recognize a performed activity using raw signals obtained by wearable devices. Photoplethysmography – Daily Life Activities (PPG-DaLiA) dataset contains data related to 15 individuals wearing physiological and motion sensors. PPG-DaLiA was used as an input to a custom data segmentation model to obtain the respective training and testing dataset. Overall, 23 ML well-established models were employed. The weighted and the fine k-nearest neighbours, the fine Gaussian support vector machines and the bagged trees were the algorithms that achieved the best performance with a very high accuracy level.

Keywords

• human activity recognition
• machine learning
• multi-class classification
• wearables
• data segmentation