A deep genetic algorithm for human activity recognition leveraging fog computing frameworks

With modern e-healthcare developments, ambulatory healthcare has become a prominent requirement for physical or mental ailed, elderly, childhood people. One of the major challenges in such applications is timing and precision. A potential solution to this problem is the fog-assisted cloud computing architecture. The activity recognition task is performed with the hybrid advantages of deep learning and genetic algorithms. The video frames captured from vision cameras are subjected to the genetic change detection algorithm, which detects changes in activities of subsequent frames. Consequently, the deep learning algorithm recognizes the activity of the changed frame. This hybrid algorithm is run on top of fog-assisted cloud framework, fogbus and the performance measures including latency, execution time, arbitration time and jitter are observed. Empirical evaluations of the proposed model against three activity data sets shows that the proposed deep genetic algorithm exhibits higher accuracy in inferring human activities as compared to the state-of-the-art algorithms.