A multistage collaborative filtering method for fall detection
Tao Xie, Yiqiang Chen, Lisha Hu, Chenlong Gao, Chunyu Hu, Jianfei Shen · 2017
Falls threaten the health and life of the elders heavily because they lead to injuries or even death. Therefore, a reliable monitoring and alarm mechanism is desperately in need to guarantee the quality of elders' life. In this paper, we propose a multistage machine learning method to perform fall detection and solve the false alarm and missing alarm problem in traditional fall detection methods. Our proposed method consists of three stages: 1) threshold filtering, 2) ELM classifier, and 3) orientation-based filtering. Our method utilizes a high-precision triaxial accelerometer to collect the relevant information. After filtered by our three-stage method, the signal can be determined whether it is a fall or not. Experimental results demonstrate that: different from the traditional state-of-art methods with a single machine learning classifier, our method can greatly reduce the missing alarm and false alarm rate on the premise of high accuracy for all detection.