使用深度学习的腕带跌落检测系统

Kulwarun Warunsin, Thongchai Phairoh
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引用次数: 3

摘要

跌倒是威胁老年人的重大问题之一。环境辅助生活(AAL)是支持老年人独立和安全生活的设备和过程。AAL包括老年人跌倒检测。老年人在跌倒后的安全期内,如果有救援,生命就会有保障。跌落检测是保障生命安全的必要手段。微控制器具有体积小、计算性能高、功耗低等特点,发展迅速。此外,对于机器学习的软件也引起了广泛的兴趣和发展。本研究采用加速度计作为传感器,深度学习算法作为跌倒模式识别,开发了一个跌倒检测系统。我们使用ESP32微控制器来确定用户的活动模式。如果ESP32检测到跌倒,它将通过LINE应用程序的Wi-Fi向供应商发送跌倒警报。本研究的跌倒检测模型与测试数据的准确率为96.55%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wristband Fall Detection System Using Deep Learning
Fall is one of the significant problems threatening older people. Ambient Assisted Living (AAL) is equipment and process for supporting older people’s independent and safe living. AAL includes elderly fall detection. The life of older people will be safe if rescue comes to help in the safety period after fall. Then fall detection is needed for life safety. The development of microcontrollers is very fast with tiny in size, high calculation performance, and low power consumption. In addition, the software for machine learning is extensive interest and developed. This study developed a fall detection system by applying an accelerometer as a sensor and a deep learning algorithm as a fall pattern recognition. We used an ESP32 microcontroller to determine the pattern of the user’s activity. If ESP32 detects the fall, it will send a fall alert to the provider via Wi-Fi with the LINE application. The fall detection model of this study has an accuracy of 96.55% with the testing data.
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