Automatic Pose Recognition for Monitoring Dangerous Situations in Ambient-Assisted Living

• Published in: Frontiers in bioengineering and biotechnology 2020-05, Vol.8, p.415-415
• Main Authors: Guerra, Bruna Maria Vittoria, Ramat, Stefano, Beltrami, Giorgio, Schmid, Micaela
• Format: Article
• Language: English
• Subjects: Ambient-Assisted Living; Bioengineering and Biotechnology; geometric features; machine learning; pose recognition; skeleton tracking; vision-based activity recognition
• ISSN: 2296-4185; 2296-4185
• DOI: 10.3389/fbioe.2020.00415

Continuous monitoring of frail individuals for detecting dangerous situations during their daily living at home can be a powerful tool toward their inclusion in the society by allowing living independently while safely. To this goal we developed a pose recognition system tailored to disabled students living in college dorms and based on skeleton tracking through four Kinect One devices independently recording the inhabitant with different viewpoints, while preserving the individual’s privacy. The system is intended to classify each data frame and provide the classification result to a further decision-making algorithm, which may trigger an alarm based on the classified pose and the location of the subject with respect to the furniture in the room. An extensive dataset was recorded on 12 individuals moving in a mockup room and undertaking four poses to be recognized: standing, sitting, lying down, and “dangerous sitting.” The latter consists of the subject slumped in a chair with his/her head lying forward or backward as if unconscious. Each skeleton frame was labeled and represented using 10 discriminative features: three skeletal joint vertical coordinates and seven relative and absolute angles describing articular joint positions and body segment orientation. In order to classify the pose of the subject in each skeleton frame we built a two hidden layers multi-layer perceptron neural network with a “SoftMax” output layer, which we trained on the data from 10 of the 12 subjects (495,728 frames), with the data from the two remaining subjects representing the test set (106,802 frames). The system achieved very promising results, with an average accuracy of 83.9% (ranging 82.7 and 94.3% in each of the four classes). Our work proves the usefulness of human pose recognition based on machine learning in the field of safety monitoring in assisted living conditions.