Location Awareness By Analyzing Ambient WiFi Signals

• Main Authors: Mike Li-Chung Chen, 陳立中
• Other Authors: Polun Chang
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/67620419988351426095

碩士 === 國立陽明大學 === 生物醫學資訊研究所 === 102 === Activities of Daily Living (ADLs) is a clinical assessment tool commonly used in evaluating functional mobility and self-care ability in the elderly patients. This tool is limited by its form as a paper based self-respondent questionnaire – the questions may be interpreted differently by different respondents, those with cognitive deficit may be unable to answer, a proxy-respondent may introduce more bias. Furthermore, ADL does not reflect a person’s real-life behavior. The Life-Space Assessment (LSA) was designed to assess real-life functional ability indirectly by observing a person’s movement across different life-spaces. Nevertheless, paper-based LSA is still plagued by the same problems faced by ADL. Development of a smartphone based LSA tool may provide more objective observation. Unfortunately, current smartphones cannot easily achieve indoor location tracking. This study aims to see if room-level location tracking can be achieved simply by analyzing ambient WiFi signals, without installing extra hardwares. In our study, we were able to achieve crude room level location determination in most cases, but fluctuating accuracies were noted, possibly due to indoor environment without walls and the furniture placement. Accuracies higher than 95% were noted in most of the rooms, but dropped below 60% in some cases. Our current method may already suffice in designing a smartphone based LSA tool. Also, since our method does not require installation of additional hardware, it may complement other location determination methods in reducing hardware cost.