Comparative Study of Different Types of Sensors ‎of Smart Home for Elderly

Abstract

This paper describes an evaluation framework for a smart home project that utilizes sensor technologies to detect activity levels and monitor older adults. An independent retirement community is designed to follow the aging model in place. Technologies used include a stove sensor, a bed sensor, a gait monitor, and a video sensor network. The evaluation framework includes focus groups with end users (older adults and health care providers) and observations. Preliminary findings indicate an overall positive attitude of older adults towards smart home sensors and a valid and reliable performance. End users must be included in all stages of smart home development (design, implementation, and testing) and actively participate in a formative evaluation of the technology. A rapidly aging population requires support systems that enable it to preserve dwellers' independence without compromising their safety. Smart homes for the elderly have the potential to offer hidden health and wellness monitoring. The aim is to provide a safe, independent living environment that can identify and predict problems by monitoring the Activities of Daily Livings (ADLs) of the inhabitants. For this, a system able to handle continuous data streams is required. Such a system can extract information using appropriate classification and learning algorithms, thus allowing the remote monitoring of health and well-being at a high level. The implementation requires appropriate sensing technologies, identification of ADLs, data preprocessing techniques, and machine learning algorithms. It is challenging due to individual differences. Such a system must be able to personalize individual needs. Our contribution was designing and implementing a platform to smartly monitor the health condition of the elderly using sensor data from a smart home through an interactive user interface that is user-friendly and multi-platform. This proof-of-concept used offline data, with the view to extend to real-time data collection in the future, which could then be used to inform support providers remotely.