TY - GEN
T1 - Deep convolutional neural network classifier for travel patterns using binary sensors
AU - Gochoo, Munkhjargal
AU - Liu, Shing Hong
AU - Bayanduuren, Damdinsuren
AU - Tan, Tan Hsu
AU - Velusamy, Vijayalakshmi
AU - Liu, Tsung Yu
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - The early detection of dementia is crucial in independent life style of elderly people. Main intention of this study is to propose device-free non-privacy invasive Deep Convolutional Neural Network classifier (DCNN) for Martino-Saltzman's (MS) travel patterns of elderly people living alone using open dataset collected by binary (passive infrared) sensors. Travel patterns are classified as direct, pacing, lapping, or random according to MS model. MS travel pattern is highly related with person's cognitive state, thus can be used to detect early stage of dementia. The dataset was collected by monitoring a cognitively normal elderly resident by wireless passive infrared sensors for 21 months. First, over 70000 travel episodes are extracted from the dataset and classified by MS travel pattern classifier algorithm for the ground truth. Later, 12000 episodes (3000 for each pattern) were randomly selected from the total episodes to compose training and testing dataset. Finally, DCNN performance was compared with three other classical machine-learning classifiers. The Random Forest and DCNN yielded the best classification accuracies of 94.48% and 97.84%, respectively. Thus, the proposed DCNN classifier can be used to infer dementia through travel pattern matching.
AB - The early detection of dementia is crucial in independent life style of elderly people. Main intention of this study is to propose device-free non-privacy invasive Deep Convolutional Neural Network classifier (DCNN) for Martino-Saltzman's (MS) travel patterns of elderly people living alone using open dataset collected by binary (passive infrared) sensors. Travel patterns are classified as direct, pacing, lapping, or random according to MS model. MS travel pattern is highly related with person's cognitive state, thus can be used to detect early stage of dementia. The dataset was collected by monitoring a cognitively normal elderly resident by wireless passive infrared sensors for 21 months. First, over 70000 travel episodes are extracted from the dataset and classified by MS travel pattern classifier algorithm for the ground truth. Later, 12000 episodes (3000 for each pattern) were randomly selected from the total episodes to compose training and testing dataset. Finally, DCNN performance was compared with three other classical machine-learning classifiers. The Random Forest and DCNN yielded the best classification accuracies of 94.48% and 97.84%, respectively. Thus, the proposed DCNN classifier can be used to infer dementia through travel pattern matching.
KW - assistive technology
KW - deep learning
KW - device-free
KW - elder care
KW - non-invasive
KW - smart house
KW - travel pattern
UR - http://www.scopus.com/inward/record.url?scp=85050688144&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050688144&partnerID=8YFLogxK
U2 - 10.1109/ICAwST.2017.8256432
DO - 10.1109/ICAwST.2017.8256432
M3 - Conference contribution
AN - SCOPUS:85050688144
T3 - Proceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017
SP - 132
EP - 137
BT - Proceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Awareness Science and Technology, iCAST 2017
Y2 - 8 November 2017 through 10 November 2017
ER -