Detect the daily activities and in-house locations using smartphone
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Publication Details
Author list: Sukreep S., Mongkolnam P., Nukoolkit C.
Publisher: Springer
Publication year: 2015
Journal: Advances in Intelligent Systems and Computing (2194-5357)
Volume number: 361
Start page: 215
End page: 225
Number of pages: 11
ISSN: 2194-5357
eISSN: 2194-5357
Languages: English-Great Britain (EN-GB)
Abstract
Falls are a key cause of significant health problems, especially for elderly people who live alone. Falls are a leading cause of accidental injury and death. To help assist the elderly, we propose a system to detect daily activities and in-house location of a user by means of a smartphone's sensor and Wi-Fi access points. We applied data mining techniques to classify activity detection (e.g., sitting, standing, lying down, walking, running, walking up/downstairs, and falling) and in-house location detection. Health risk level configurations (threshold model) are applied for unhealthy activity detection with an alarm sounding and also short messages sent to those who have responsibility such as a caregiver or a doctor. Moreover, we provide various forms of easy to understand visualization for monitoring and include health risk level summary, daily activity summary, and in-house location summary. ฉ Springer International Publishing Switzerland 2015.
Keywords
Access point, Activity of daily living (ADL), In-house location, Wireless signal
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