Ensuring Healthcare Services Provision: An Integrated Approach of Resident Contexts Extraction and Analysis via Smart Objects

Abstract

Advances in healthcare applications benefit the scenario of medical service provision for both care staff and medical institution than ever. Following such success, a healthcare system, named RCTC (Resident Classification by Types of Care), which grades individuals in terms of the severity of five functional status assessment aspects: mental, movement, eating, toilet (urination and defecation functions), and medical treatment, was developed in this study. This system is designed based on a conceptual model that follows the assessment/classification/placement sequence for long-term care institutions. It is implemented to deliver appropriate services according to individual needs based on its preprocessing of classification and further reduces the costs of manpower and loading of care staff through the analysis of assessment logs. With this systematic appraisal, this system can not only help care staff determine the needs of residents but also produce personalized health plans (i.e., weekly schedule towards comprehensive assessment and personalized care services). Results of implemented (and in use as well) system have demonstrated the feasibility that it can enhance the quality of care services to residents, working load of care staff, and efficiency of care-related information management for medical institution.

Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement

“…Thus, patients would prefer non-image UHC devices because of privacy, convenience, and cost of equipment [9]. Requirements of UHC in relation to rehabilitation are expanded by the concept of homecare personal area networks that provide ambulatory monitoring and supporting hospital functions by using ad hoc or body area networks [10,11,12,13,14,15,16,17,18]. In the past decade, wireless sensor networks (WSNs) have been gradually deployed and applied to detect various physiological signals because of micro-electro-mechanical system (MEMS), integrated circuits (IC), and rapid advances in radio frequency (RF) [19,20].…”