Fall Monitoring System Based on Wearable Device and Improved KNN

Abstract

For the elderly, falls can be extremely fatal. However, due to the physical decline of the elderly, it is difficult to avoid falls. Therefore, to the greatest extent feasible lessen the harm that falls on the elderly inflict, so that they can be found in the first time of falls, this study based on wearable devices, proposed a fall monitoring system using an improved K-nearest neighbor algorithm. The improved fuzzy K-nearest neighbor algorithm combined with support vector machine algorithm is applied to improve the efficiency and accuracy of the algorithm, and reduce the false positive rate and false negative rate as much as possible. The suggested model’s average precision in the simulation experiment is 97.5%. The specificity was 97.6%. The sensitivity was 97.5%. The convergence performance is also good, 24 iterations can reach the optimal. In the actual experiment, the average accuracy reached 98.7%; The false alarm rate is only 0.7%; The negative rate was 2.5%; Its performance is superior to other two algorithms. This shows that the proposed method has excellent accuracy, false positive rate and false negative rate in practical application, which has important significance for the health and safety of the elderly.