Fall detection algorithm based on random forest
Da Luo · Journal of Computer Applications · 2015
To handle the over fitting and inadaptability problem of current fall detection algorithms caused by lack of real fall samples of elderly people and the use of small size of fall samples collected by young people, a fall detection algorithm based on random forest was proposed. By adopting sliding window mechanism, the sequentially collected acceleration data within the window were firstly processed to extract feature parameters of time domain and frequency domain, and then the Bootstrap approach was employed to randomly select partial samples with the same number from the whole training sample collection, after that random selection of features was performed to construct a collection of basic SVM(Support Vector Machine) classifiers with best feature partition. On the online fall detection stage, the final classification result was obtained with vote of results by multiple basic SVM classifiers according to the majority criteria. The experimental results demonstrate that the proposed algorithm outperforms the SVM and BP(Back Propagation) neural network algorithms with 95. 2% accuracy,90. 6% sensitivity and 93. 5% specificity, and reflects that the fall detection algorithm based on random forest can accurately recognize the fall activity, and has strong generalization ability and robustness.