| dc.contributor | 資科系 | |
| dc.creator (作者) | 廖峻鋒 | |
| dc.creator (作者) | Liao, Chun-Feng | |
| dc.creator (作者) | Li, Ting-Ying;Chien, Yi-Wei;Chou, Chi-Chun;Cheah, Wen-Ting;Fu, Li-Chen;Chen, Chery Chia-Hui;Chou, Chun-Chen;Chen, I-An | |
| dc.date (日期) | 2019-11 | |
| dc.date.accessioned | 2-Sep-2020 09:16:08 (UTC+8) | - |
| dc.date.available | 2-Sep-2020 09:16:08 (UTC+8) | - |
| dc.date.issued (上傳時間) | 2-Sep-2020 09:16:08 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/131413 | - |
| dc.description.abstract (摘要) | Because of the worldwide aging population, more and more elders suffer from dementia problem. Nowadays, it is an inconvenient and time-consuming process for medical doctors to diagnose elders who live independently with possible dementia because the process imposes a large quantity of diagnostic questions from a checklist that needs to be answered by elders themselves or their caregivers either directly or after a long-term observation. In order to help doctors to make this diagnostic process easier, this article proposes a supporting system that can quickly estimate the likelihood for an elder of having dementia based on 2 to 4 hours monitoring of a behavioral test done by the elder. During the test, the elder only needs to perform certain activities selected from the so-called instrumental activities of daily living (IADL) in a smart home environment, and their movement trajectories will be extracted from motion sensors deployed in the smart home environment and be analyzed to find a potential correlation with the indoor wandering patterns. A machine learning algorithm is selected to carry out the classification task, namely, into dementia and nondementia groups, based on our proposed features of the aforementioned wandering patterns. Two data sets are employed for performance evaluation, where the first one is 232 elders including seven dementia, whereas the second one is collected by ourselves from a senior center, which is 30 elders including nine dementia. It turns out that the average precision and recall for the first data set are both up to 98.3% with area under the ROC curve (AUC-ROC) being 0.846, and those for the second data set are 89.9% and 90.0% with AUC-ROC being 0.921. Note to Practitioners-We proposed a supporting system which can classify the elders as either dementia or nondementia with high accuracy. The trajectories of the elders will be extracted from motion sensors that deployed in the smart home environment. The indoor wandering patterns according to repetitive movements are analyzed and classified using the machine learning technique. The proposed system used ambient sensors instead of wearable sensors or cameras to let the elders feel more comfortable when they are being monitored. In addition, the proposed system only required a short period of time to screen the elders and easier for medical doctors to diagnose the elders without wasting time for asking the large quantity of diagnostic questions from a checklist that needs to be answered by the elders themselves or their caregivers. | |
| dc.format.extent | 2819603 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | IEEE Transactions on Automation Science and Engineering, 17:2, 771-783 | |
| dc.subject (關鍵詞) | Dementia ; indoor wandering pattern ; machine learning ; motion sensors ; quickly monitor ; smart home | |
| dc.title (題名) | A Fast and Low Cost Repetitive Movement Pattern Indicator for Massive Dementia Screening | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1109/TASE.2019.2942386 | |
| dc.doi.uri (DOI) | https://doi.org/10.1109/TASE.2019.2942386 | |
