Mobile healthcare management (indoor movement tracking)

• Main Author: Goh, Alvin Shen Guan.
• Other Authors: Chan Syin
• Format: Final Year Project
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering
• Online Access: http://hdl.handle.net/10356/52125
• Institution: Nanyang Technological University

Singapore is facing a rapid aging population with the elderly aged 65 and above expected to double and make up 14% of the population by the year 2020 [2]. An estimated 6% of the elderly people are suffering from mental impairment illnesses such as dementia [5]. These elderly people have a tendency to wander off into random areas of the indoor environment which might pose unforeseen risks due to their forgetfulness and lack of judgement. The fact that it is costly and unfeasible to provide one-to-one supervision has gave rise to the need to analyze the movement patterns of this significant group of elderly people with dementia in order to effectively take care of their well-being and mitigate potential dangers that they may face in their daily lives. Existing indoor localization systems that provide indoor tracking functionalities are generally costly and lack the ability to allow unrestricted integration with custom developed applications. In this project, an Indoor Movement Tracking System (IMTS) that can detect and record movements of the elderly or people in general within an indoor environment is developed. The Received Signal Strength Indicator (RSSI) fingerprinting technique based on the Wireless Fidelity (WiFi) technology is employed as the positioning methodology for the system. The Samsung Galaxy SIII Android device is used as the Mobile Station (MSTN) carried around by the tracked person while 4 WiFi Access Points (AP) positioned at different regions of the indoor location are deployed as Base Stations (BSTN). A test harness designed to verify the functionalities of the IMTS is implemented within the home unit of the author. Wandering detection algorithms developed by Mr. Vuong are integrated with the IMTS to perform analysis based on the position information obtained. The real-time locomotion data are examined and classified into different categories of wandering patterns. Alerts are sent to the caregivers to provide warning of movement anomalies exhibited by the elderly for timely interventions to be made. The integrated system provides a cost-effective, pervasive and scalable solution for wandering detection within an indoor setup.