International Journal of Science, Engineering and TechnologyAuthors: Bombale Sukanya Vikas, Dr.Anand.A.Khatri, Professor Bangar Abhishek Popat, Professor S.B.Bhosale
Abstract: A personal alarm is useful only if it can send a message at the moment help is needed. A person who has fallen may be unable to reach a button, mobile coverage may be missing in remote or enclosed places, and a cloud service may be out of reach. Progress in embedded machine learning, inertial sensing and low-power long-range radio makes it realistic to build a wearable that screens body movement locally and relays a small alert through neighbouring radio nodes. This paper presents a focused narrative review of eight studies on LoRa mesh routing, wearable fall detection with edge inference, LoRaWAN-based wearable networks, radio localisation and multi-sensor emergency alerting. The studies are discussed under three themes: sensing and on-device classification, multi-hop alert transport, and system-level evaluation. Reported fall-detection accuracies of about 95.6–95.9% and an inference time below 37.84 ms show that local classification is feasible, yet these values come from individual datasets and prototypes and cannot be transferred to a different wearer, sensor or outdoor setting. None of the reviewed work evaluates the complete chain from body-worn motion sensing, through mesh relaying, to notification of a trusted contact. To address this gap, the paper outlines a conceptual seven-layer architecture that combines an IMU-based event score, a manual SOS path that bypasses classification, compact authenticated packets, duplicate-controlled relaying and explicit delivery-state reporting, together with evaluation metrics and a four-stage test plan. The main risks (false alarms, missed events, radio congestion, battery drain, location error, privacy loss and spoofed alerts) are examined. The proposed system is an assistive alert aid, not a guaranteed rescue service or a diagnostic device.
