A Human Activity Recognition System Using Skeleton Data from RGBD Sensors

TLDR

Active and Assisted Living aims to enable ageing in place, and human activity recognition with cost‑effective RGBD sensors can monitor elderly people in home environments. This work proposes an activity‑recognition algorithm that exploits skeleton data extracted from RGBD sensors. The system extracts key poses to build a feature vector and classifies them with a multiclass Support Vector Machine, computing key poses via clustering without a learning algorithm. Evaluated on five public datasets, the approach achieves promising results, particularly for AAL‑related actions, and its applicability in real‑world AAL scenarios is discussed.

Abstract

The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.