A real-time fall detection system using a wearable gait analysis sensor and a Support Vector Machine (SVM) classifier

@article{Shibuya2015ARF,
  title={A real-time fall detection system using a wearable gait analysis sensor and a Support Vector Machine (SVM) classifier},
  author={Naohiro Shibuya and Bhargava Teja Nukala and Amanda Rodriguez and Jerry Tsay and Tam Q. Nguyen and Steven Zupancic and Donald Y. C. Lie},
  journal={2015 Eighth International Conference on Mobile Computing and Ubiquitous Networking (ICMU)},
  year={2015},
  pages={66-67}
}
In this study, we report a custom designed wireless gait analysis sensor (WGAS) system for real-time fall detection using a Support Vector Machine (SVM) classifier. Our WGAS includes a tri-axial accelerometer, 2 gyroscopes and a MSP430 micro-controller. It was worn by the subjects at either the T4 or at the waist level for various intentional falls, Activities of Daily Living (ADL) and the Dynamic Gait Index (DGI) test. The raw data of tri-axial acceleration and angular velocity is wirelessly… CONTINUE READING

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