An IoT and Machine Learning Approach to In-Home Elderly Care

The recent increase in the global aging population has resulted in many social and economic challenges for elderly people at the familial level. The inability to provide continuous and urgent care in domestic environments has resulted in a lack of independence among the elderly. This has further increased the burden on family members and caregivers. To address these challenges, this paper proposes an IoT and machine learning-based solution to monitor multiple health parameters of the aged individuals. The proposed system is designed implemented using a wearable sensing device integrated with in-style data visualization and analysis. The device allows medical practitioners and caregivers to access real-time and historical data on a patient for general monitoring and to provide obligatory care. With further use of machine learning techniques, the system is able to predict heart conditions with 65% accuracy in an elderly patient.