Toward an Unsupervised Approach for Daily Gesture Recognition in Assisted Living Applications

Activity Recognition is important in assisted living applications to monitor people at home. Over the past, inertial sensors have been used to recognize different activities, spanning from physical activities to eating ones. Over the last years, supervised methods have been widely used, but they require an extensive labeled dataset to train the algorithms and this may represent a limitation of concrete approaches. This paper presents a comparison of unsupervised and supervised methods in recognizing nine gestures by means of two inertial sensors placed on the index finger and on the wrist. Three supervised classification techniques, namely Random Forest, Support Vector Machine, and Multilayer Perceptron, as well as three unsupervised classification techniques, namely k-Means, Hierarchical Clustering, and Self-Organized Maps, were compared in the recognition of gestures made by 20 subjects. The obtained results show that the Support Vector Machine classifier provided the best performances (0.94 accuracy) compared to the other supervised algorithms. However, the outcomes show that even in an unsupervised context, the system is able to recognize the gestures with an average accuracy of ~0.81. The proposed system may be therefore involved in future telecare services that could monitor the activities of daily living, allowing an unsupervised approach that does not require labelled data