The human-in-the-loop cyber-physical system provides numerous solutions for the challenges faced by the doctors or medical practitioners. There is a linear trend of advancement and automation in the medical field for the early diagnosis of several diseases. One of the critical and challenging diseases in the medical field is coma. In the medical research field, currently, the prediction of these diseases is performed only using the data gathered from the devices only; however, the human’s input is much essential to accurately understand their health condition to take appropriate decision on time. Therefore, we have proposed a healthcare framework involving the concept of artificial intelligence in the human-in- the-loop cyber-physical system. This model works via a response loop in which the human’s intention is concluded by gathering biological signals and context data, and then, the decision is interpreted to a system action that is recognizable to the human in the physical environment, thereby completing the loop. In this paper, we have designed a model for early prognosis of coma using the electroencephalogram dataset. In the proposed approach, we have achieved the best results using a statistical learning algorithm called autoregressive integrated moving average in comparison to artificial neural networks and long short-term memory models. In order to measure the efficiency of our model, we have used the root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) value to evaluate the linear models as it gives the difference between the measured value and true or correct value. We have achieved the least possible error value for our dataset. To conduct this experiment, we used the dataset available in the phsyionet opensource community.
Elderly people activity recognition has become a vital necessity in many countries, because most of the elderly people live alone and are vulnerable. Thus, more research to advance in the monitoring systems used to recognize the activities of elderly people is required. Many researchers have proposed different monitoring systems for activity recognition using wired and wireless wearable sensing devices. However, the activity classification accuracy achieved so far should be improved to meet the challenges of more precise activity monitoring. Our study proposes a smart Human Activity Recognition system architecture utilizing an open source dataset generated by wireless, batteryless sensors used by 14 healthy aged persons and unsupervised and supervised machine learning algorithms. In this paper, we also propose using a smart grid for checking regularly the wearable sensing device operational status to address the well-known reliability challenges of these devices, such as wireless charging and data trustworthiness. As the data from the sensing device is very noisy, we employ the K-means++ clustering to identify outliers and use advanced ensemble classification techniques, such as the stacking classifier for which a meta model built using the random forest algorithm gave better results than all other base models considered. We also employ a bagging classifier, which is an ensemble meta-estimator fitting the prediction outputs of the base classifiers and aggregating them to produce the ensemble output. The best classification accuracy of 99.81 was achieved by the stacking classifier in training and 99.78% in testing, respectively. Comparisons for finding the best model were conducted using the recall, F1 score, and precision values.
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