Action Recognition with Spatiotemporal Analysis and Support Vector Machine for Elderly Monitoring System
Mahaputra Ilham Awal, Luqmanul Hakim Iksan, Rizky Zull Fhamy, Dwi Kurnia Basuki, Sritrusta Sukaridhoto, Kazuyoshi Wada · 2021
Nowadays, action recognition is widely used in various aspects of life, one of which is for monitoring purposes. One of the challenges in this problem is the process of detecting and recognizing the actions of the elderly. In this research, an integrated system is proposed to detect and monitor the actions of the elderly based on Computer Vision. This system using joint skeleton coordinate data as a system input. The system can detect actions taken by the elderly based on the dataset we created. There are seven actions: standing, sitting, walking, falling, drinking, sleeping, and eating. This system uses the OpenPose library to detect skeletons from the elderly. Then for feature extraction using spatiotemporal to extract the joint position data of the elderly every 15 frames. For classification using the Support Vector Machine in this system. Then, the recognized action data will be sent by the system to the backend of the platform. The test shows that the system can detect seven actions of the elderly with an accuracy value of 87.81% on the training model used.