One walk a year to 1000 within a year: Continuous in-home unobtrusive gait assessment of older adults

Kaye, Jeffrey; Mattek, Nora; Dodge, Hiroko H.; Buracchio, Teresa; Austin, Daniel; Hagler, Stuart; Pavel, M.; Hayes, Tamara L.

doi:10.1016/j.gaitpost.2011.09.006

Cited by 107 publications

(131 citation statements)

References 29 publications

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“…An alternative to this approach is to deploy unobtrusive passive monitoring systems in people's homes, providing continuous assessment of daily activity and behaviors of interest. This kind of pervasive computing model has been established by the Intelligent Systems for Assessing Aging Change (ISAAC) study, 5,6 …”

Section: Methodsmentioning

confidence: 99%

“…Details of the study protocol are published elsewhere. 5,6 In brief, entry criteria for the study included 1) age 70 or older, 2) independent living in a larger than one-room apartment, 3) Mini-Mental State Examination (MMSE) score Ͼ24 and Clinical Dementia Rating scale 8 score Յ0.5, and 4) average health for age: Medical illnesses that would limit physical participation (e.g., wheelchair bound) or likely lead to untimely death (e.g., certain cancers) were exclusions. Enrollment began in March 2007 and continued on a rolling basis until September 2009 with a total of 265 participants.…”

mentioning

confidence: 99%

“…The assessment of walking speed and its data validation process are described in detail elsewhere. 6,23 In brief, motion sensors with a restricted field of view were installed along a hallway so they would detect only when a person passed directly under them. Data from all sensors were wirelessly received by a dedicated research computer placed in the subject's home and then were time-stamped and stored in a structured query language database.…”

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confidence: 99%

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In-home walking speeds and variability trajectories associated with mild cognitive impairment

Dodge¹,

Mattek²,

Austin³

et al. 2012

Neurology

197

163

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Objective: To determine whether unobtrusive long-term in-home assessment of walking speed and its variability can distinguish those with mild cognitive impairment (MCI) from those with intact cognition. Methods:Walking speed was assessed using passive infrared sensors fixed in series on the ceiling of the homes of elderly individuals participating in the Intelligent Systems for Assessing Aging Change (ISAAC) cohort study. Latent trajectory models were used to analyze weekly mean speed and walking speed variability (coefficient of variation [COV]).Results: ISAAC participants living alone included 54 participants with intact cognition, 31 participants with nonamnestic MCI (naMCI), and 8 participants with amnestic MCI at baseline, with a mean follow-up of 2.6 Ϯ 1.0 years. Trajectory models identified 3 distinct trajectories (fast, moderate, and slow) of mean weekly walking speed. Participants with naMCI were more likely to be in the slow speed group than in the fast (p ϭ 0.01) or moderate (p ϭ 0.04) speed groups. For COV, 4 distinct trajectories were identified: group 1, the highest baseline and increasing COV followed by a sharply declining COV; groups 2 and 3, relatively stable COV; and group 4, the lowest baseline and decreasing COV. Participants with naMCI were more likely to be members of either highest or lowest baseline COV groups (groups 1 or 4), possibly representing the trajectory of walking speed variability for early-and late-stage MCI, respectively. It is of substantial importance to detect dementia at its earliest phases to sustain independence, to optimize treatment, to understand preclinical biology, and to ultimately develop prevention strategies. Past studies have found that slower walking speed and poorer motor function are associated with mild cognitive impairment (MCI) and are predictors of progression to frank dementia. [1][2][3][4] However, it is difficult to identify changes in these functions because changes evolve slowly over time and change measures have high test-to-test variability. Further, current methods for assessing this change rely largely on sparsely spaced or annual assessments that provide too few data points to discern subtle changes. An alternative to this approach is to deploy unobtrusive passive monitoring systems in people's homes, providing continuous assessment of daily activity and behaviors of interest. This kind of pervasive computing model has been established by the Intelligent Systems for Assessing Aging Change (ISAAC) study, 5,6 Conclusion:

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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In-home walking speeds and variability trajectories associated with mild cognitive impairment

Dodge¹,

Mattek²,

Austin³

et al. 2012

Neurology

197

163

View full text Add to dashboard Cite

show abstract

“…Decreased costs associated with formal healthcare and assisted living placement are additional benefits of in-home monitoring and intervention technologies. Ubiquitous sensor networks that provide unobtrusive in-home monitoring of physical activity, 9 complex activity recognition, 10 context-based IADL reminder systems, 11,12 home telecare, 13 and physically or socially assistive robots 14,15 are examples of smart environment assistive technologies. Given the detrimental effects of social isolation on older adults, assistive technologies that promote older adults' interaction with others have gained recent interest.…”

Section: Introductionmentioning

confidence: 99%

Reactions to a Remote-Controlled Video-Communication Robot in Seniors' Homes: A Pilot Study of Feasibility and Acceptance

Seelye

Wild

Larimer

et al. 2012

Telemedicine and e-Health

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Objective: Remote telepresence provided by tele-operated robotics represents a new means for obtaining important health information, improving older adults' social and daily functioning and providing peace of mind to family members and caregivers who live remotely. In this study we tested the feasibility of use and acceptance of a remotely controlled robot with video-communication capability in independently living, cognitively intact older adults. Materials and Methods: A mobile remotely controlled robot with video-communication ability was placed in the homes of eight seniors. The attitudes and preferences of these volunteers and those of family or friends who communicated with them remotely via the device were assessed through survey instruments. Results: Overall experiences were consistently positive, with the exception of one user who subsequently progressed to a diagnosis of mild cognitive impairment. Responses from our participants indicated that in general they appreciated the potential of this technology to enhance their physical health and well-being, social connectedness, and ability to live independently at home. Remote users, who were friends or adult children of the participants, were more likely to test the mobility features and had several suggestions for additional useful applications. Conclusions: Results from the present study showed that a small sample of independently living, cognitively intact older adults and their remote collaterals responded positively to a remote controlled robot with video-communication capabilities. Research is needed to further explore the feasibility and acceptance of this type of technology with a variety of patients and their care contacts.

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“…By monitoring gait in the home it is possible to collect some metrics relevant to ADL such as walking speed and ambulatory time regularly and with much higher frequency than afforded by the typical annual assessment [8].…”

Section: The Link Between Gait and Healthmentioning

confidence: 99%

An IR and RF Based System for Functional Gait Analysis in a Multi-Resident Smart-Home

Schafermeyer¹

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Changes in the gait characteristics, such as walking speed and stride length, of a person living at home can be used to presage cognitive decline, predict fall potential, monitor long-term changes in cognitive impairment, test drug regimens, and more. This thesis presents a novel approach to gait analysis in a smart-home environment by leveraging new advances in inexpensive sensors and embedded systems to create novel solutions for in-home gait analysis. Using a simple, non-invasive hardware system consisting entirely of wall-mounted infrared and radio frequency sensor arrays, data is collected on the gait of subjects as they pass by. This data is then analyzed and sent to a clinician for further study. The system is non-invasive in that it does not use cameras and could be built into the molding of a home so that it would be nearly invisible. In a finished prototype version, the system presented in this thesis could be used to analyze the gait characteristics of one or more subjects living in a home environment while ignoring the data of visitors and other non-subject cohabitants. The ability to constantly collect data from a home environment could provide thousands of observations per year for clinical analysis. Providing such a robust data set may allow people with gait impairment to live at home longer and more safely before transitioning to a care facility, have a reduced fall risk due to better prediction, and live a healthier life in old age.ii To Mary Beth Llorens and Howard Newman, my parents, who have always encouraged and supported me in finding my own path. Thank you.iii

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One walk a year to 1000 within a year: Continuous in-home unobtrusive gait assessment of older adults

Cited by 107 publications

References 29 publications

In-home walking speeds and variability trajectories associated with mild cognitive impairment

In-home walking speeds and variability trajectories associated with mild cognitive impairment

Reactions to a Remote-Controlled Video-Communication Robot in Seniors' Homes: A Pilot Study of Feasibility and Acceptance

An IR and RF Based System for Functional Gait Analysis in a Multi-Resident Smart-Home

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