Impact of Steady – State Genetic Algorithm and Internet – of – Things (IOT) on the Enhancement of Fall Detection System and Rehabilitation Gaming Exercises for Elderly People
Keywords:SSGA, IOT, 2×2 confusion matrix, 3×3 confusion matrix, Binary classification method, MIRA, RGB
Old age is inevitable. Fall in elders is one of the important crises which leads them to severe injuries and also causes mortality sometimes to them. There is an urgent need for the development of fall detection system every - were throughout the world. There is tremendous growth in the medical field to save the elders from these disasters. Presently, IOT occupies an important place in the life of elderly people. In order to prevent the problem of fall detection in elders, we use Kinect sensor and IOT devices. Our approach in this work from different angles includes data collection, data transmission and data analysis. The performance of the rehabilitants is estimated through the ability of the performer to achieve the rehabilitation goal of the individual. While playing rehabilitation gaming exercises with the help of Kinect an IOT device, we get parametric values test 1, test 2 and test 3 of each patient through which we can calculate certain classifiers which is used for comparing the F1 – weighted average of steady state genetic algorithm with that of some of the supervised machine learning algorithms like KNN, logistic regression and MP – CNN. The proposed algorithm is applied on an UT – Kinect dataset to check its performance. We can make use of UT – Kinect dataset to recognized depth sequence. Single stationary Kinect Xbox 360 is utilized to captured videos. Each patient is asked to perform the action three times and thereby three channels are recorded. The three channels are RGB, depth and skeleton joints and are synchronized. The rate of frame is 30 f/s. In this paper, we analyze Logistic Regression and Steady – State Genetic Algorithm (SSGA). We prove in this paper, the F1 – weighted average of SSGA (84.2) is higher than other supervised machine learning algorithms like KNN (69.9), Logistic Regression (75) and MP – CNN. (78.6)
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