Low latency and low powered fall detector for epilepsy

Registration ID: IJNRD_220002

Published ID: IJNRD2405299

DOI: Click Here to Get

Dhanalakshmi Stridarane , Durga Devi.S , Gangaswetha.K

Fall detector, Epilepsy, IoT (Internet of Things), Edge computing. Smart healthcare,Biomedical applications, Real-time monitoring ,MEMS sensor,, Edge-based fall detection.

Epilepsy, a pandemic disorder affecting 50 million people (about twice the population of Texas), drives the urgent search for a reliable and quick detection tool. Abstract: This paper introduces a comprehensive fall detection system that integrates state-of-the-art MEMS (Micro-Electro-Mechanical Systems) sensors, including accelerometers and gyroscopes, with cutting-edge edge computing, heart rate sensors, and temperature sensors. Falling incidents pose a significant risk, especially among the elderly and individuals with mobility impairments, necessitating effective and timely detection mechanisms to ensure prompt assistance and mitigate potential injuries. The proposed system addresses this imperative by harnessing the collective capabilities of MEMS sensors, which continuously monitor and analyze the user's movements in real-time. These sensors are adept at capturing even subtle changes in acceleration and rotational motion, enabling the system to discern between regular activities and fall events accurately. Incorporating sophisticated algorithms and leveraging the computational power of edge computing infrastructure, the system processes sensor data autonomously, swiftly identifying patterns indicative of a fall. By executing the fall detection algorithm directly on the device, edge computing circumvents the limitations of traditional cloud-based solutions, ensuring minimal latency and enabling rapid response even in environments with limited network connectivity. Moreover, the integration of heart rate sensors enhances the system's capabilities by providing valuable insights into the user's physiological parameters. Variations in heart rate patterns can signify distress or physical exertion, aiding in the accurate identification of fall incidents and facilitating appropriate interventions. Additionally, temperature sensors offer contextual information about the user's surroundings, enabling the system to assess environmental conditions and factor them into the fall risk assessment process. Through the fusion of data from multiple sensors and the dynamic adjustment of fusion parameters using machine learning algorithms, the system achieves robust performance across diverse scenarios. The proposed system undergoes rigorous validation through extensive experimentation and real-world testing, demonstrating its efficacy in significantly enhancing fall detection accuracy and response efficiency.

Published Paper   E-Certificate

Dhanalakshmi Stridarane, Durga Devi.S, & Gangaswetha.K (May-2024). Low latency and low powered fall detector for epilepsy . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), c939-c946. https://ijnrd.org/papers/IJNRD2405299.pdf

Volume 9 Issue 5, May-2024

Pages : c939-c946

Paper Reg. ID: IJNRD_220002

Published Paper Id: IJNRD2405299

Downloads: 000121996

Research Area: Engineering

Country: Puducherry, Puducherry, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405299.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405299

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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