Intelligent cloud agent based action recognition detection using machine learning

On the lap of this present-day epoch, Human Activity Recognition (HAR) has been of considerable assistance in case of health monitoring and recovery. The utilization of machine learning approach integration with intelligent agent in the area of health informatics collected via Human Activity Recognition enhances the decision making quality and significance. Its specific homogenization into the Smart Healthcare Monitoring permits gathering, examining and learning from Internet of Things (IoT) wearable devices, undoubtedly achieving knowledge and making analysis on the patient’s state. Despite several research works conducted on Smart Healthcare Monitoring, there remains certain amount of pitfalls, like, time, overhead involved in analysis and also the falsification of analysis. To focus on these issues, a Statistical Partial Regression and Support Vector Intelligent Agent Learning (SPR-SVIAL) for Smart Healthcare Monitoring is proposed. The SPR-SVIAL method is split into two stages. First, data pre-processing along with the dimensionality reduced features are extracted by employing Statistical Partial Regression Feature Extraction model. Here, with the ceaseless thump to-pulsate heart information, triaxial accelerometer information, rest quality, actual work and mental attributes obtained from the input dataset acquired from IoT wearable devices, Partial Least Square is applied to extract the dimensionality reduced features, therefore contributing to Smart Healthcare Monitoring time and accuracy. Next, with these resultant features, Support Vector Intelligent Agent Learning is proposed for Smart Healthcare Monitoring that with the aid of Machine Learning and Intelligent Agent not only reduces the falsification of analysis but also reduces the overhead incurred. The SPR-SVIAL method is tested on simulators and the obtained results indicated better performance upon comparison with the other methods. The results show that we can reduce the time, overhead, false positive rate for healthcare monitoring and achieve a high accuracy rate by performing feature extraction for each of the data recording.