Aiming at the shortcomings of particle swarm optimization(PSO) such as serious convergence, slow convergence and easy to fall into local minimum, this paper introduced the pattern search algorithm into PSO to optimize the parameters of support vector machine(SVM) and applied to fall detection. Firstly, it used a wearable device to collect the fall to the detection data set, and filtered the initial data to eliminate the influence of noise. Secondly, it extracted the filtered data feature and reduced the dimensionality of the extracted multi-dimensional data by singular value decomposition algorithm(SVD). Finally, it used the dimensionality-reduced data to test the PSO pattern search algorithm. Compared with SVM and SVM plus PSO, it improved the specificity and sensitivity of PSO in fall detection.

[2020-04-05] Printed Article /doc/2018.09.0747/v2