TY - GEN
T1 - Tracking indoor activities of patients with mild cognitive impairment using motion sensors
AU - Nelson, Tsang Wai Hung
AU - Qureshi, Umair Mujtaba
AU - Kam-Yiu, Lam
AU - NG, Joseph K Y
AU - Song, Han
AU - Ioannis, Papavasileiou
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/5/5
Y1 - 2017/5/5
N2 - In order to maintain a healthy living both physiologically and psychologically, it is important for patients with mild cognitive impairment (MCI) to maintain active in daily life. In this paper, we demonstrate how to use simple motion sensors, e.g. accelerometers, gyroscopes and magnetometers, to design and develop a system, called ActiveLife, for effective tracking of the daily living activities of MCI patients within their living rooms. In order to simplify the activity detection process, in ActiveLife, we adopt the context-based approach to model the common activities performed by the user within a day. Since the accelerometer and gyroscope are tri-axial sensors, the sensor data for different axes can be used to predict the current posture of the user while he is performing an activity. Combining with the heading direction of the posture obtained from the magnetometer and distance traveled during the transition of activities, we can estimate the current activity of the user. To further improve the estimation accuracy, we have designed an algorithm using the machine-learning technique, i.e. support vector machines (SVM), for activity classification.
AB - In order to maintain a healthy living both physiologically and psychologically, it is important for patients with mild cognitive impairment (MCI) to maintain active in daily life. In this paper, we demonstrate how to use simple motion sensors, e.g. accelerometers, gyroscopes and magnetometers, to design and develop a system, called ActiveLife, for effective tracking of the daily living activities of MCI patients within their living rooms. In order to simplify the activity detection process, in ActiveLife, we adopt the context-based approach to model the common activities performed by the user within a day. Since the accelerometer and gyroscope are tri-axial sensors, the sensor data for different axes can be used to predict the current posture of the user while he is performing an activity. Combining with the heading direction of the posture obtained from the magnetometer and distance traveled during the transition of activities, we can estimate the current activity of the user. To further improve the estimation accuracy, we have designed an algorithm using the machine-learning technique, i.e. support vector machines (SVM), for activity classification.
KW - Activity Tracking
KW - Context-based Approach
KW - Dementia
KW - Motion Sensors
KW - Support Vector Machines
UR - http://www.scopus.com/inward/record.url?scp=85019707343&partnerID=8YFLogxK
U2 - 10.1109/AINA.2017.23
DO - 10.1109/AINA.2017.23
M3 - Conference proceeding
AN - SCOPUS:85019707343
T3 - Proceedings - International Conference on Advanced Information Networking and Applications, AINA
SP - 431
EP - 438
BT - Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017
A2 - Enokido, Tomoya
A2 - Hsu, Hui-Huang
A2 - Lin, Chi-Yi
A2 - Takizawa, Makoto
A2 - Barolli, Leonard
PB - IEEE
T2 - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017
Y2 - 27 March 2017 through 29 March 2017
ER -