Healthcare delivery in recent years has gained massive improvement. Information Technology has been used to improve the way medical practitioners administer healthcare which has further impacted on the lives of the general populace. This study present an automated system in health monitoring of the elderly. Human Activities Recognition (HAR), heart rate, body temperature, stress level and blood pressure sensor dataset has been used in implementation and a mobile software was developed to simulate the activity, health monitoring and response of the medical personnel to the elderly. The HAR dataset contains 77 sensors in subject 1 and 84 sensors in subject 2. The Agile software development methodology was used in the process of development. The mobile software is used to get the readings of the various wearable and home stationary sensors from which the medical personnel can receive notification of the health information and activity of the patient. The MATLAB scientific programming language has been used to analyze and demonstrate the HAR dataset with some unique activity spikes shown on the graphical illustrations. A mobile application was developed to simulate the readings gotten from the wearable sensors and the activity triggers in the home. The R programming language was used to train and test the wearable sensor performance of the artificial neural model. The body sensor data has been evaluated and analyzed and the performance accuracies are thus: for Heart rate, the performance accuracy recorded for training is 70.4% with a misclassification of 29.6% and testing performance accuracy was 57.4% with a misclassification of 42.4%, Body temperature recorded 65.7% with misclassification of 34.3% for training and 66.7% with misclassification of 33.3% for testing. The performance for Stress level in the training was 65.7% with misclassification of 34.3 and 64.1% with misclassification of 35.9% for testing performance and the performance of blood pressure was 73.1% with a misclassification of 26.9% for training and in testing, 70.4% with misclassification of 29.6% was recorded. The mobile application has performed well during simulation, presenting the readings of the smart wearable devices from the patient and the activities of the patient at home.

Health Monitoring System, Internet of Things, Patient Monitoring, Human Activity Recognition

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