A Human Activity Recognition System Using Skeleton Data from RGBD Sensors

• Published in: Computational Intelligence and Neuroscience Vol. 2016; no. 2016; pp. 872 - 885-073
• Main Authors: Spinsante, Susanna, Gambi, Ennio, Gasparrini, Samuele, Cippitelli, Enea
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01.01.2016; Hindawi Publishing Corporation; Hindawi Limited
• Subjects: Accelerometry - methods; Aged; Algorithms; Computation; Extraction; Female; Human Activities; Human motion; Humans; Intelligence; Joints - physiology; Machine Learning; Male; Monitors; Older people; Pattern Recognition, Automated - methods; Privacy; Recognition; Sensors; Skeleton - physiology; Support Vector Machine; Support vector machines
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2016/4351435

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.