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While walking is a basic exercise, falls are a serious health concern for older persons. With an emphasis on lightweight, user-friendly, and precise sensor integration, the prototype offers real-time data on tilt angles and pressure distribution. The initiative attempts to anticipate the risk of falls by working with a physiotherapist and using machine learning algorithms, providing useful information for families and medical professionals. The suggested method, which has implications in physiology and orthopedics, shows promise for an aging population. By adding sensors to a conventional walking stick to monitor the research offers a novel way to address the major health risk of falls among the elderly. Real-time data on tilt angles and pressure distribution are provided by the created prototype, which places a high priority on accurate sensor integration and lightweight, user-friendly design. The program uses machine learning algorithms with physiotherapists to forecast fall risks, providing families and medical professionals with important information. The suggested method shows potential for applications in neurology, physiology, and orthopedics in addition to fall prevention, demonstrating a comprehensive approach to improving an aging population’s general safety and well-being. The device is designed to improve walking safety for the elderly by incorporating accurate, lightweight, and user-friendly sensors into a traditional walking stick. The real-time data gathering on tilt angles and pressure distribution made possible by this connection provides insightful information about the user’s motions. Through proactive fall risk prediction, the program works with physiotherapists and uses machine learning algorithms to provide a tailored and efficient fall prevention strategy. The dedication to a user-friendly and ergonomic design encourages regular usage without posing any inconvenience. Beyond preventing falls, the prototype shows potential uses in neurology, physiology, and orthopedics, highlighting its adaptability to many healthcare domains.

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Predictive Fall Prevention Using Sensor-Integrated Walking Sticks

  • 会议论文

  • 发表日期：

    2025年

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  • Hriday Boriawala,
  • Rahi Shah,
  • Reetu Jain

摘要

While walking is a basic exercise, falls are a serious health concern for older persons. With an emphasis on lightweight, user-friendly, and precise sensor integration, the prototype offers real-time data on tilt angles and pressure distribution. The initiative attempts to anticipate the risk of falls by working with a physiotherapist and using machine learning algorithms, providing useful information for families and medical professionals. The suggested method, which has implications in physiology and orthopedics, shows promise for an aging population. By adding sensors to a conventional walking stick to monitor the research offers a novel way to address the major health risk of falls among the elderly. Real-time data on tilt angles and pressure distribution are provided by the created prototype, which places a high priority on accurate sensor integration and lightweight, user-friendly design. The program uses machine learning algorithms with physiotherapists to forecast fall risks, providing families and medical professionals with important information. The suggested method shows potential for applications in neurology, physiology, and orthopedics in addition to fall prevention, demonstrating a comprehensive approach to improving an aging population’s general safety and well-being. The device is designed to improve walking safety for the elderly by incorporating accurate, lightweight, and user-friendly sensors into a traditional walking stick. The real-time data gathering on tilt angles and pressure distribution made possible by this connection provides insightful information about the user’s motions. Through proactive fall risk prediction, the program works with physiotherapists and uses machine learning algorithms to provide a tailored and efficient fall prevention strategy. The dedication to a user-friendly and ergonomic design encourages regular usage without posing any inconvenience. Beyond preventing falls, the prototype shows potential uses in neurology, physiology, and orthopedics, highlighting its adaptability to many healthcare domains.