Paper Title
Low latency and low powered fall detector for epilepsy
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Registration ID: IJNRD_220002
Published ID: IJNRD2405299
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Keywords
Fall detector, Epilepsy, IoT (Internet of Things), Edge computing. Smart healthcare,Biomedical applications, Real-time monitoring ,MEMS sensor,, Edge-based fall detection.
Abstract
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.
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How To Cite (APA)
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
Issue
Volume 9 Issue 5, May-2024
Pages : c939-c946
Other Publication Details
Paper Reg. ID: IJNRD_220002
Published Paper Id: IJNRD2405299
Research Area: Engineering
Author Type: Indian Author
Country: Puducherry, Puducherry, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2405299.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405299
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