In-home hierarchical posture classification with a time-of-flight 3D sensor

- A novel posture recognition system was presented and validated in four scenarios.
- The use of TOF sensors allowed to overcome well-known passive vision problems.
- TOF images were unable to reveal subject's identity thus preserving privacy.
- Two discrimination approaches allowed to satisfy complementary requirements.
- Classification rates greater than 97% were achieved in all considered scenarios.

A non-invasive technique for posture classification suitable to be used in several in-home scenarios is proposed and preliminary validation results are presented. 3D point cloud sequences were acquired using a single time-of-flight sensor working in a privacy preserving modality and they were processed with a low power embedded PC. In order to satisfy different application requirements (e.g. covered distance range, processing speed and discrimination capabilities), a twofold discrimination approach was investigated in which features were hierarchically arranged from coarse to fine by exploiting both topological and volumetric representations. The topological representation encoded the intrinsic topology of the body's shape using a skeleton-based structure, thus guaranteeing invariance to scale, rotations and postural changes and achieving a high level of detail with a moderate computational cost. On the other hand, using the volumetric representation features were described in terms of 3D cylindrical histograms working within a wider range of distances in a faster way and also guaranteeing good invariance properties. The discrimination capabilities were evaluated in four different real-home scenarios related with the fields of ambient assisted living and homecare, namely “dangerous event detection”, “anomalous behaviour detection”, “activities recognition” and “natural human-ambient interaction”. For each mentioned scenario, the discrimination capabilities were evaluated in terms of invariance to viewpoint changes, representation capabilities and classification performance, achieving promising results. The two feature representation approaches exhibited complementary characteristics showing high reliability with classification rates greater than 97%.