International Journal of Science, Engineering and TechnologyAuthors: Shinde Sakshi Jaysing, Prof. S. R. Borhade, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: Falls are a leading cause of injury among older adults, and a person who falls while living alone may remain on the floor for a long time before help arrives. AI-based elderly care and fall detection systems monitor movement and alert caregivers when a fall is likely. This paper reviews such systems. It examines wearable approaches using accelerometers and gyroscopes with threshold and machine learning classifiers; ambient approaches using floor, radar and Wi-Fi channel sensing that require nothing to be worn; and vision approaches using cameras and human pose estimation. Public datasets of simulated falls and activities of daily living, deep learning models such as CNNs and LSTMs on motion sequences, and IoT alerting through mobile applications are discussed, along with activity monitoring and basic health tracking within ambient-assisted living. A five-layer architecture comprising sensing, edge inference, connectivity, care platform and caregiver application layers is proposed, together with research gaps, objectives, a comparison of sensing approaches and challenges such as false alarms, missed falls, battery life, connectivity, privacy and the gap between simulated and real-world falls. The review emphasizes that such systems support but do not replace medical professionals and emergency services, and concludes that combining sensing modalities with on-device AI can provide timely, reliable and acceptable support for independent living.
DOI: https://doi.org/10.5281/zenodo.24216390
