Recent developments in cognitive technical systems (CTS), which offer organic and effective operating principles, reveal a development in human-computer interaction (HCI). A CTS must rely on data from several sensors, which must then be processed and merged by fusion algorithms, to do this. To put the observations made into the proper context, additional knowledge sources must also be integrated. This research propose novel technique in cognitive human computer interaction based body sensor data analytics using machine learning technique. here the body sensor based monitoring data has been collected and transmitted by cloud networks for cognitive human computer interaction. then this data has been processed and trained using Boltzmann perceptron basis encoder neural network. Various body sensor-based monitored datasets are subjected to experimental analysis for accuracy, precision, recall, F-1 score, RMSE, normalised square error (NSE), and mean average precision. Proposed technique obtained 93% accuracy, 79% precision, 72% of recall, 64% f-1 score, 51% of RMSE, 56% NSE and 48% MAP.

The advancement and innovations in the field of science and technology paved way for various advanced treatments in the field of medicine. They are implemented using sensors, and computer-aided designs with artificial intelligence techniques. This helps in the detection of serious health constraints at an earlier stage with appropriate treatments using decision-making techniques. One of the important health concerns that are increasing rapidly is cardiovascular disorders. This includes Arrhythmia and Myocardial Infarction. Earlier prediction and classification can protect them from serious constraints. They are diagnosed using the Electrocardiogram (ECG). To obtain accurate results, artificial intelligence techniques are implemented to extract the optimum output. The proposed system includes the detection and classification using deep learning techniques with the Internet of Things (IoT). The existing heartbeat detection system is overcome using a deep convolutional neural network. This helps in the implementation of automatic heartbeat detection and identification of abnormalities. The ECG signals are pre-processed with segmentation and feature extraction techniques. The classification and identification of constraints in the functioning of the heart are identified using optimization algorithms. The proposed system is trained, tested, and evaluated using the MIT-BIH arrhythmia database. The accuracy and efficiency of the proposed system are 99.98% using the MIT-BIH dataset.