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This study is an observational study that uses daily activity and environmental sensing techniques to establish behavioral models of early dementia patients and cognitive healthy function to assess their daily behavior and determine their activities. Specifically, the team will collect information on a number of wireless sensors for dementia, mild cognitive impairment and healthy elderly residents, and use special mathematical models to establish the behavior of the two groups of subjects Model. The model will be developed a reliable algorithm to assess health risk of the subjects.
With the help of intelligent home environment and Pervasive Computing, it is possible to bring information about the behavior of the patients to the caregivers and their relatives. In this study, non-invasive wireless sensors such as infrared motion sensor the switch sensor will be built in the home of the subjects. The wireless sensor data collected by the input to the team's "activity identification system" in order to assess the mathematical model of the subjects "capacity" and immediate judgment of their activities. In the identification of activities can be divided into two categories of behavior, risk behavior (harmful to the subjects) and protective behavior (beneficial to the subjects), real-time activity detection, can avoid dangerous behavior, such as water and gas off, roaming , Repetitive behavior (over-eating), forgetting to eat and day-night reversal (sleep disturbance), etc., the protection of behavior can be observed through a long time to help patients conduct beneficial behavior, such as regular quantitative diet.
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| Measure | Description | Time Frame |
|---|---|---|
| The relationship between moving pattern change and dementia. | Motion sensors will be installed on the ceiling. We will analyze multiple sensors to see how the participant move from one location to another. The data whether the participant is wandering or not and times of wandering will be measured. | 6 months |
| The relationship between daily routine change and dementia. | Motion sensors, switch sensors, and current sensors will be installed in the home of the participant. The room which the participant is in, the appliance which the participant is using will be measured. | 6 months |
| The relationship between sleeping time change and dementia. | Motion sensors will be installed in the bed or on the ceiling. The time when the participant goes to sleep, and the times the participant turns over during sleep will be measured. | 6 months |
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Inclusion criteria:
Exclusion criteria:
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The age of the subject is over 65. The education level is junior high school degree or above. The healthy elders will be selected by the MMSE score (over 24), AD-8 score (under 2) and the Mini-Cog (over 4).
The MMSE score of the subjects with cognitive impairment or dementia is 10 to 23.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Li-Chen Fu, Doctor | Contact | +886-2-3366-4888 | 533 | lichen@ntu.edu.tw |
| Name | Affiliation | Role |
|---|---|---|
| Li-Chen Fu, Doctor | National Taiwan University, Department of Computer Science & Information Engineering | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| National Taiwan University Hospital | Taipei | 10617 | Taiwan |
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| ID | Term |
|---|---|
| D060825 | Cognitive Dysfunction |
| D003704 | Dementia |
| ID | Term |
|---|---|
| D003072 | Cognition Disorders |
| D019965 | Neurocognitive Disorders |
| D001523 | Mental Disorders |
| D001927 | Brain Diseases |
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| D002493 |
| Central Nervous System Diseases |
| D009422 | Nervous System Diseases |