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Examples of two home layouts with coverage of sensors indicated. Red boxes (S): locations of passive infrared motion detectors; green rectangles (D): contact sensors on exit/entry doors and refrigerator doors; blue boxes (W): sensor lines for measuring walking speed; HC: home computer location. See text for details of how sensor locations were chosen. 

Examples of two home layouts with coverage of sensors indicated. Red boxes (S): locations of passive infrared motion detectors; green rectangles (D): contact sensors on exit/entry doors and refrigerator doors; blue boxes (W): sensor lines for measuring walking speed; HC: home computer location. See text for details of how sensor locations were chosen. 

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To describe a longitudinal community cohort study, Intelligent Systems for Assessing Aging Changes, that has deployed an unobtrusive home-based assessment platform in many seniors homes in the existing community. Several types of sensors have been installed in the homes of 265 elderly persons for an average of 33 months. Metrics assessed by the sen...

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Context 1
... assessed by the sensors include total daily activity, time out of home, and walking speed. Floor plans of each residence were drawn to provide a map of sensors placed throughout the home (Figure 1). ...
Context 2
... sensors were used to assess general activity by location. Walking speed was estimated as described previously ( Hagler et al., 2010) using data from sensors positioned sequentially on the ceiling approximately 61 cm apart in areas such as a hallway or other corridor (light blue boxes; Figure 1). These sensors were modified so that they had a restricted field view of ±4°, only firing when someone passed directly within their path. ...
Context 3
... nighttime activity was estimated by the mean number of sensor firings between 9 p.m.-6 a.m. The walking speed was estimated as the median walking speed per day as derived from the firing times of the walking line sensors (Hagler et al., 2010). The number of walks per day was determined using the same walking data. ...

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... The AI-ML system can collect complex streams of data in real-time, longitudina l ly track movements, analyze the input, and identify any unusual changes that may suggest the onset of cognitive or functional decline. Using predictive reasoning, the automated analytic system can then intervene by sounding an alert or offering behavioural suggestions in response to what may be subtle declines in function, which a human may otherwise miss 128,134 . As a result, these systems enable a better understanding of the dynamic nature of an older adult's disease progression or functioning 128,135 and can potentially facilitate timely access to appropriate healthcare and prevent serious injuries 136,137 . ...
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... Our overall approach builds on earlier work [12,14,34], all being real-world implementations. The data collection process, sensor setup, the method used for pattern identification, and the behavior of older persons, however, differ. ...
... Interviews and motion sensors were used to identify residents' sleep and daily movements. Another longitudinal study [34] evaluated the usage of unobtrusive technologies in detecting a change in activities and cognitive decline by statistically analyzing 200 days of data on the daytime and nighttime activities of 233 senior participants with a mean age of 83 years. ...
... Our proposed approach to ADL analysis used routine data (data collected from the interviews) of older participants to identify anomalies in ADLs. This approach is similar to that used by other studies [12,14,34]. Our approach, however, differs from [13,16,32], which used annotated activity data. ...
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... One hundred and twenty-eight ambulatory community-dwelling volunteers (mean age 85.2 ± 7.7 years, 100 female) were included in this study. All participants provided written informed consent and were enrolled in one of two ongoing studies of inhome monitoring: the ORCATECH Life Laboratory study and the Intelligent Systems for Assessing Aging Changes (ISAAC) study (21). The study protocols were approved by the Oregon Health and Science University Institutional Review Board (Life Laboratory, IRB #2765; ISAAC IRB #2353). ...
... Homes included in the ORCATECH study range from one-bedroom apartments to homes with up to five bedrooms. Frequently visited rooms (bedrooms, bathrooms, kitchens, and living rooms), were outfitted with passive sensors (21). The sensors fire when presence activity is detected and a timestamp of each sensor firing is sent wirelessly to a transceiver. ...
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Introduction: Computer-use behaviours can provide useful information about an individual's cognitive and functional abilities. However, little research has evaluated unaided and non-directed home computer-use. In this proof of principle study, we explored whether computer-use behaviours recorded during routine home computer-use i) could discriminate between individuals with subjective cognitive decline (SCD) and individuals with mild cognitive impairment (MCI); ii) were associated with cognitive and functional scores; and iii) changed over time. Methods: Thirty-two participants with SCD (n=18) or MCI (n=14) (mean age = 72.53 years; female n = 19) participated in a longitudinal study in which their in-home computer-use behaviour was passively recorded over 7-9 months. Cognitive and functional assessments were completed at three time points: baseline; mid-point (4.5 months); and end point (month 7 to 9). Results: Individuals with MCI had significantly slower keystroke speed and spent less time on the computer than individuals with SCD. More time spent on the computer was associated with better task switching abilities. Faster keystroke speed was associated with better visual attention, recall, recognition, task inhibition, and task switching. No significant change in computer-use behaviour was detected over the study period. Discussion/Conclusion: Passive monitoring of computer-use behaviour shows potential as an indicator of cognitive abilities, and can differentiate between people with SCD and MCI. Future studies should attempt to monitor computer-use behaviours over a longer time period to capture the onset of cognitive decline, and thus could inform timely therapeutic interventions.
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... The difficulties in BADL reportedly increase for each stage as dementia progresses [25]. To improve the ADL assessment with a more objective approach, several studies have directly observed the subjects in a home environment or in a laboratory, quantified the ADL, and measured the different levels of ADL performance [26][27][28][29]. Studies that investigated the relationship between MCI and dementia based on ADL data collected through direct observation can be categorized into research that observed the daily lives of study subjects in a smart home environment and research that assigned ADL tasks to study the subjects' performance in a laboratory environment with sensors and compared the results with those of the normal control group. ...
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With the global trend toward an aging population, the increasing number of dementia patients and elderly living alone has emerged as a serious social issue in South Korea. The assessment of activities of daily living (ADL) is essential for diagnosing dementia. However, since the assessment is based on the ADL questionnaire, it relies on subjective judgment and lacks objectivity. Seven healthy seniors and six with early-stage dementia participated in the study to obtain ADL data. The derived ADL features were generated by smart home sensors. Statistical methods and machine learning techniques were employed to develop a model for auto-classifying the normal controls and early-stage dementia patients. The proposed approach verified the developed model as an objective ADL evaluation tool for the diagnosis of dementia. A random forest algorithm was used to compare a personalized model and a non-personalized model. The comparison result verified that the accuracy (91.20%) of the personalized model was higher than that (84.54%) of the non-personalized model. This indicates that the cognitive ability-based personalization showed encouraging performance in the classification of normal control and early-stage dementia and it is expected that the findings of this study will serve as important basic data for the objective diagnosis of dementia.