A Bio-Inspired Elderly Action Recognition System for Ambient Assisted Living

As the global population continues to age, the demand for effective and personalized care for elderly individuals becomes increasingly vital. Assisted living systems have emerged as promising technological solutions to address the challenges associated with aging populations. Numerous studies have demonstrated the positive impact of ambient assisted living (AAL) systems on seniors' lives, including improved health outcomes, increased independence, enhanced quality of life and reduced healthcare costs. However, not all AAL systems are commercially available, primarily due to their energy-costly computational demands and expensive equipment. The paper at hand introduces an ambient low-cost solution exploiting only a single RGB sensor to monitor and recognize the activity of senior subjects. For this purpose, the utilization of bio-inspired networks, particularly the hierarchical temporal memory (HTM) model, based on Hebbian learning and implemented using the Nupic framework, as well as the spiking neural networks (SNNs) developed through the Nengo library is assessed. Both solutions are compared against the Support Vector Machine (SVM) classifier for elderly action recognition in the context of energy-efficient systems for AAL scenarios. The obtained results of the HTM showcase promising success rates and enhanced time efficiency, outperforming the rest methods in both terms. The above findings lead us to valuable insights for developing accurate and practical action recognition systems tailored to AAL scenarios.