TY - CHAP
T1 - INDOOR ACTIVITY TRACKING FOR ELDERLY USING INTELLIGENT SENSORS
AU - Tsang, Nelson Wai-Hung
AU - Lam, Kam-Yiu
AU - Qureshi, Umair M.
AU - Ng, Joseph Kee-Yin
AU - Papavasileiou, Ioannis
AU - Han, Song
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
PY - 2018
Y1 - 2018
N2 - The world is experiencing an unprecedented, enduring, and pervasive aging process. More and more elderly will be staying alone at home, especially in the daytime, and need to handle daily living activities by themselves. It is important to have effective measures of their self-caring abilities and to minimize accident risks. For example, an efficient fall detection method for the elderly, tracking their daily activities, is desirable. In this chapter, we discuss how to apply the latest intelligent sensor technologies to track the common indoor activities performed by an elderly person in his or her living quarters, which could be used fall detection. Through the introduction of our system SmartMind, we first show how Kinect, a 3D depth camera, can be applied for effective activity tracking of the user within a predefined environment. In the design of SmartMind, in order to improve accuracy in activity detection, we adopt a context-based approach to model the activities. Since Kinect has a privacy concerns problem, in the second part of the chapter, we introduce another system, called ActiveLife, in which simple motion sensors are adopted to measure changes in motion for indoor activity estimation. To improve its accuracy in activity estimation, we need a good model the living environment of the user and his/her activities within the living environment. Experimental results have shown the effectiveness of using a machine learning method, support vector machines, for improving its accuracy in activity estimation.
AB - The world is experiencing an unprecedented, enduring, and pervasive aging process. More and more elderly will be staying alone at home, especially in the daytime, and need to handle daily living activities by themselves. It is important to have effective measures of their self-caring abilities and to minimize accident risks. For example, an efficient fall detection method for the elderly, tracking their daily activities, is desirable. In this chapter, we discuss how to apply the latest intelligent sensor technologies to track the common indoor activities performed by an elderly person in his or her living quarters, which could be used fall detection. Through the introduction of our system SmartMind, we first show how Kinect, a 3D depth camera, can be applied for effective activity tracking of the user within a predefined environment. In the design of SmartMind, in order to improve accuracy in activity detection, we adopt a context-based approach to model the activities. Since Kinect has a privacy concerns problem, in the second part of the chapter, we introduce another system, called ActiveLife, in which simple motion sensors are adopted to measure changes in motion for indoor activity estimation. To improve its accuracy in activity estimation, we need a good model the living environment of the user and his/her activities within the living environment. Experimental results have shown the effectiveness of using a machine learning method, support vector machines, for improving its accuracy in activity estimation.
KW - Activity tracking
KW - Kinect
KW - Machine learning methods
KW - Motion sensors
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U2 - 10.1016/B978-0-12-812130-6.00011-1
DO - 10.1016/B978-0-12-812130-6.00011-1
M3 - RGC 12 - Chapter in an edited book (Author)
SN - 9780128121306
SN - 9780128123201
T3 - Intelligent Data Centric Systems
SP - 197
EP - 222
BT - Intelligent Data Sensing and Processing for Health and Well-being Applications
A2 - Wister, Miguel
A2 - Pancardo, Pablo
A2 - Acosta, Francisco
PB - Elsevier
ER -