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Behera CK, Condell J, Dora S, Gibson DS, Leavey G. State-of-the-Art Sensors for Remote Care of People with Dementia during a Pandemic: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:4688. [PMID: 34300428 PMCID: PMC8309480 DOI: 10.3390/s21144688] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 05/31/2021] [Accepted: 07/02/2021] [Indexed: 01/10/2023]
Abstract
In the last decade, there has been a significant increase in the number of people diagnosed with dementia. With diminishing public health and social care resources, there is substantial need for assistive technology-based devices that support independent living. However, existing devices may not fully meet these needs due to fears and uncertainties about their use, educational support, and finances. Further challenges have been created by COVID-19 and the need for improved safety and security. We have performed a systematic review by exploring several databases describing assistive technologies for dementia and identifying relevant publications for this review. We found there is significant need for appropriate user testing of such devices and have highlighted certifying bodies for this purpose. Given the safety measures imposed by the COVID-19 pandemic, this review identifies the benefits and challenges of existing assistive technologies for people living with dementia and their caregivers. It also provides suggestions for future research in these areas.
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Affiliation(s)
- Chandan Kumar Behera
- Intelligent Systems Research Centre, Faculty of Computing, Engineering and Built Environment, University of Ulster, Northland Road, Londonderry BT48 7JL, UK; (C.K.B.); (S.D.); (G.L.)
| | - Joan Condell
- Intelligent Systems Research Centre, Faculty of Computing, Engineering and Built Environment, University of Ulster, Northland Road, Londonderry BT48 7JL, UK; (C.K.B.); (S.D.); (G.L.)
| | - Shirin Dora
- Intelligent Systems Research Centre, Faculty of Computing, Engineering and Built Environment, University of Ulster, Northland Road, Londonderry BT48 7JL, UK; (C.K.B.); (S.D.); (G.L.)
| | - David S. Gibson
- Northern Ireland Centre for Stratified Medicine (NICSM), Biomedical Sciences Research Institute, University of Ulster, Altnagelvin Area Hospital, C-TRIC Building, Glenshane Road, Londonderry BT47 6SB, UK;
| | - Gerard Leavey
- Intelligent Systems Research Centre, Faculty of Computing, Engineering and Built Environment, University of Ulster, Northland Road, Londonderry BT48 7JL, UK; (C.K.B.); (S.D.); (G.L.)
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2
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Current State of Non-wearable Sensor Technologies for Monitoring Activity Patterns to Detect Symptoms of Mild Cognitive Impairment to Alzheimer's Disease. Int J Alzheimers Dis 2021; 2021:2679398. [PMID: 33628484 PMCID: PMC7889365 DOI: 10.1155/2021/2679398] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 01/20/2021] [Accepted: 01/26/2021] [Indexed: 11/17/2022] Open
Abstract
Mild cognitive impairment (MCI) could be a transitory stage to Alzheimer's disease (AD) and underlines the importance of early detection of this stage. In MCI stage, though the older adults are not completely dependent on others for day-to-day tasks, mild impairments are seen in memory, attention, etc., subtly affecting their daily activities/routines. Smart sensing technologies, such as wearable and non-wearable sensors, coupled with advanced predictive modeling techniques enable daily activities/routines based early detection of MCI symptoms. Non-wearable sensors are less intrusive and can monitor activities at naturalistic environment with no interference to an individual's daily routines. This review seeks to answer the following questions: (1) What is the evidence for use of non-wearable sensor technologies in early detection of MCI/AD utilizing daily activity data in an unobtrusive manner? (2) How are the machine learning methods being employed in analyzing activity data in this early detection approach? A systematic search was conducted in databases such as IEEE Explorer, PubMed, Science Direct, and Google Scholar for the papers published from inception till March 2019. All studies that fulfilled the following criteria were examined: a research goal of detecting/predicting MCI/AD, daily activities data to detect MCI/AD, noninvasive/non-wearable sensors for monitoring activity patterns, and machine learning techniques to create the prediction models. Out of 2165 papers retrieved, 12 papers were eligible for inclusion in this review. This review found a diverse selection of aspects such as sensors, activity domains/features, activity recognition methods, and abnormality detection methods. There is no conclusive evidence on superiority of one or more of these aspects over the others, especially on the activity feature that would be the best indicator of cognitive decline. Though all these studies demonstrate technological developments in this field, they all suggest it is far in the future it becomes an effective diagnostic tool in real-life clinical practice.
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Schütz N, Saner H, Botros A, Buluschek P, Urwyler P, Müri RM, Nef T. Wearable Based Calibration of Contactless In-home Motion Sensors for Physical Activity Monitoring in Community-Dwelling Older Adults. Front Digit Health 2021; 2:566595. [PMID: 34713038 PMCID: PMC8522020 DOI: 10.3389/fdgth.2020.566595] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 09/03/2020] [Indexed: 12/02/2022] Open
Abstract
Passive infrared motion sensors are commonly used in telemonitoring applications to monitor older community-dwelling adults at risk. One possible use case is quantification of in-home physical activity, a key factor and potential digital biomarker for healthy and independent aging. A major disadvantage of passive infrared sensors is their lack of performance and comparability in physical activity quantification. In this work, we calibrate passive infrared motion sensors for in-home physical activity quantification with simultaneously acquired data from wearable accelerometers and use the data to find a suitable correlation between in-home and out-of-home physical activity. We use data from 20 community-dwelling older adults that were simultaneously provided with wireless passive infrared motion sensors in their homes, and a wearable accelerometer for at least 60 days. We applied multiple calibration algorithms and evaluated results based on several statistical and clinical metrics. We found that using even relatively small amounts of wearable based ground-truth data over 7-14 days, passive infrared based wireless sensor systems can be calibrated to give largely better estimates of older adults' daily physical activity. This increase in performance translates directly to stronger correlations of measured physical activity levels with a variety of age relevant health indicators and outcomes known to be associated with physical activity.
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Affiliation(s)
- Narayan Schütz
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Hugo Saner
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Sechenov First Moscow State Medical University, Moscow, Russia
| | - Angela Botros
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | | | - Prabitha Urwyler
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Neurology, University Neurorehabilitation Unit, University Hospital Bern (Inselspital), University of Bern, Bern, Switzerland
| | - René M. Müri
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Neurology, University Neurorehabilitation Unit, University Hospital Bern (Inselspital), University of Bern, Bern, Switzerland
| | - Tobias Nef
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
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Lussier M, Aboujaoudé A, Couture M, Moreau M, Laliberté C, Giroux S, Pigot H, Gaboury S, Bouchard K, Belchior P, Bottari C, Paré G, Consel C, Bier N. Using Ambient Assisted Living to Monitor Older Adults With Alzheimer Disease: Single-Case Study to Validate the Monitoring Report. JMIR Med Inform 2020; 8:e20215. [PMID: 33185555 PMCID: PMC7695528 DOI: 10.2196/20215] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 08/10/2020] [Accepted: 09/26/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Many older adults choose to live independently in their homes for as long as possible, despite psychosocial and medical conditions that compromise their independence in daily living and safety. Faced with unprecedented challenges in allocating resources, home care administrators are increasingly open to using monitoring technologies known as ambient assisted living (AAL) to better support care recipients. To be effective, these technologies should be able to report clinically relevant changes to support decision making at an individual level. OBJECTIVE The aim of this study is to examine the concurrent validity of AAL monitoring reports and information gathered by care professionals using triangulation. METHODS This longitudinal single-case study spans over 490 days of monitoring a 90-year-old woman with Alzheimer disease receiving support from local health care services. A clinical nurse in charge of her health and social care was interviewed 3 times during the project. Linear mixed models for repeated measures were used to analyze each daily activity (ie, sleep, outing activities, periods of low mobility, cooking-related activities, hygiene-related activities). Significant changes observed in data from monitoring reports were compared with information gathered by the care professional to explore concurrent validity. RESULTS Over time, the monitoring reports showed evolving trends in the care recipient's daily activities. Significant activity changes occurred over time regarding sleep, outings, cooking, mobility, and hygiene-related activities. Although the nurse observed some trends, the monitoring reports highlighted information that the nurse had not yet identified. Most trends detected in the monitoring reports were consistent with the clinical information gathered by the nurse. In addition, the AAL system detected changes in daily trends following an intervention specific to meal preparation. CONCLUSIONS Overall, trends identified by AAL monitoring are consistent with clinical reports. They help answer the nurse's questions and help the nurse develop interventions to maintain the care recipient at home. These findings suggest the vast potential of AAL technologies to support health care services and aging in place by providing valid and clinically relevant information over time regarding activities of daily living. Such data are essential when other sources yield incomplete information for decision making.
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Affiliation(s)
- Maxime Lussier
- Research Center of Institut universitaire de gériatrie de Montréal, Integrated Health and Social Services University Network for South-Central Montreal, Montreal, QC, Canada
- School of Rehabilitation, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Aline Aboujaoudé
- Research Center of Institut universitaire de gériatrie de Montréal, Integrated Health and Social Services University Network for South-Central Montreal, Montreal, QC, Canada
| | - Mélanie Couture
- Integrated Health and Social Services University Network for West-Central Montreal, Université de Sherbrooke, Sherbrooke, QC, Canada
- Department of Psychology, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Maxim Moreau
- Research Chair in Digital Health, High Commercial Studies of Montreal, Montreal, QC, Canada
| | - Catherine Laliberté
- Faculty of Sciences and Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Sylvain Giroux
- Faculty of Sciences and Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Hélène Pigot
- Faculty of Sciences and Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Sébastien Gaboury
- Department of Mathematics and Computer Science, Université du Québec à Chicoutimi, Chicoutimi, QC, Canada
| | - Kévin Bouchard
- Department of Mathematics and Computer Science, Université du Québec à Chicoutimi, Chicoutimi, QC, Canada
| | - Patricia Belchior
- Research Center of Institut universitaire de gériatrie de Montréal, Integrated Health and Social Services University Network for South-Central Montreal, Montreal, QC, Canada
- School of Physical and Occupational Therapy, McGill University, Montreal, QC, Canada
| | - Carolina Bottari
- School of Rehabilitation, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Guy Paré
- Research Chair in Digital Health, High Commercial Studies of Montreal, Montreal, QC, Canada
| | - Charles Consel
- Bordeaux Institute of Technology & Inria, Bordeaux, France
| | - Nathalie Bier
- Research Center of Institut universitaire de gériatrie de Montréal, Integrated Health and Social Services University Network for South-Central Montreal, Montreal, QC, Canada
- School of Rehabilitation, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
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5
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Thomas NWD, Beattie Z, Marcoe J, Wright K, Sharma N, Mattek N, Dodge H, Wild K, Kaye J. An Ecologically Valid, Longitudinal, and Unbiased Assessment of Treatment Efficacy in Alzheimer Disease (the EVALUATE-AD Trial): Proof-of-Concept Study. JMIR Res Protoc 2020; 9:e17603. [PMID: 32459184 PMCID: PMC7287724 DOI: 10.2196/17603] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 02/24/2020] [Accepted: 02/26/2020] [Indexed: 01/24/2023] Open
Abstract
Background The current clinical trial assessment methodology relies on a combination of self-report measures, cognitive and physical function tests, and biomarkers. This methodology is limited by recall bias and recency effects in self-reporting and by assessments that are brief, episodic, and clinic based. Continuous monitoring of ecologically valid measures of cognition and daily functioning in the community may provide a more sensitive method to detect subtle, progressive changes in patients with cognitive impairment and dementia. Objective This study aimed to present an alternative trial approach using a home-based sensing and computing system to detect changes related to common treatments employed in Alzheimer disease (AD). This paper introduces an ongoing study that aims to determine the feasibility of capturing sensor-based data at home and to compare the sensor-based outcomes with conventional outcomes. We describe the methodology used in the assessment protocol and present preliminary results of feasibility measures and examples of data related to medication-taking behavior, activity levels, and sleep. Methods The EVALUATE-AD (Ecologically Valid, Ambient, Longitudinal and Unbiased Assessment of Treatment Efficacy in Alzheimer’s Disease) trial is a longitudinal naturalistic observational cohort study recruiting 30 patients and 30 spouse coresident care partners. Participants are monitored continuously using a home-based sensing and computing system for up to 24 months. Outcome measures of the automated system are compared with conventional clinical outcome measures in AD. Acceptance of the home system and protocol are assessed by rates of dropout and protocol adherence. After completion of the study monitoring period, a composite model using multiple functional outcome measures will be created that represents a behavioral-activity signature of initiating or discontinuing AD-related medications, such as cholinesterase inhibitors, memantine, or antidepressants. Results The home-based sensing and computing system has been well accepted by individuals with cognitive impairment and their care partners. Participants showed good adherence to the completion of a weekly web-based health survey. Daily activity, medication adherence, and total time in bed could be derived from algorithms using data from the sensing and computing system. The mean monitoring time for current participants was 14.6 months. Medication adherence, as measured with an electronic pillbox, was 77% for participants taking AD-related medications. Conclusions Continuous, home-based assessment provides a novel approach to test the impact of new or existing dementia treatments generating objective, clinically meaningful measures related to cognition and everyday functioning. Combining this approach with the current clinical trial methodology may ultimately reduce trial durations, sample size needs, and reliance on a clinic-based assessment. International Registered Report Identifier (IRRID) DERR1-10.2196/17603
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Affiliation(s)
- Neil William Douglas Thomas
- Bruyère Research Institute, Ottawa, ON, Canada.,Department of Medicine, University of Ottawa, Ottawa, ON, Canada.,Department of Neurology, Oregon Health and Science University, Portland, OR, United States.,Department of Neurology, Department of Veterans Affairs, VA Medical Center, Portland, OR, United States
| | - Zachary Beattie
- Department of Neurology, Oregon Health and Science University, Portland, OR, United States
| | - Jennifer Marcoe
- Department of Neurology, Oregon Health and Science University, Portland, OR, United States
| | - Kirsten Wright
- Department of Neurology, Oregon Health and Science University, Portland, OR, United States
| | - Nicole Sharma
- Department of Neurology, Oregon Health and Science University, Portland, OR, United States
| | - Nora Mattek
- Department of Neurology, Oregon Health and Science University, Portland, OR, United States
| | - Hiroko Dodge
- Department of Neurology, Oregon Health and Science University, Portland, OR, United States.,Department of Neurology, University of Michigan, Ann Arbor, MI, United States
| | - Katherine Wild
- Department of Neurology, Oregon Health and Science University, Portland, OR, United States
| | - Jeffrey Kaye
- Department of Neurology, Oregon Health and Science University, Portland, OR, United States.,Department of Neurology, Department of Veterans Affairs, VA Medical Center, Portland, OR, United States
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6
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Piau A, Rumeau P, Nourhashemi F, Martin MS. Information and Communication Technologies, a Promising Way to Support Pharmacotherapy for the Behavioral and Psychological Symptoms of Dementia. Front Pharmacol 2019; 10:1122. [PMID: 31632271 PMCID: PMC6779021 DOI: 10.3389/fphar.2019.01122] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 08/30/2019] [Indexed: 12/17/2022] Open
Abstract
Health care systems face an expansion in the number of older individuals with a high prevalence of neurodegenerative diseases and related behavioral and psychological symptoms of dementia (BPSDs). Health care providers are expected to develop innovative solutions to manage and follow up patients over time in the community. To date, we are unable to continuously and accurately monitor the nature, frequency, severity, impact, progression, and response to treatment of BPSDs after the initial assessment. Technology could address this need and provide more sensitive, less biased, and more ecologically valid measures. This could provide an opportunity to reevaluate therapeutic strategies more quickly and, in some cases, to treat earlier, when symptoms are still amenable to therapeutic solutions or even prevention. Several studies confirm the relationship between sensor-based data and cognition, mood, and behavior. Most scientific work on mental health and technologies supports digital biomarkers, not so much as diagnostic tools but rather as monitoring tools, an area where unmet needs are significant. In addition to the implications for clinical care, these real-time measurements could lead to the discovery of new early biomarkers in mental health. Many also consider digital biomarkers as a way to better understand disease processes and that they may contribute to more effective pharmaceutical research by (i) targeting the earliest stage, (ii) reducing sample size required, (iii) providing more objective measures of behaviors, (iv) allowing better monitoring of noncompliance, (v) and providing a better understanding of failures. Finally, communication technologies provide us with the opportunity to support and renew our clinical and research practices.
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Affiliation(s)
- Antoine Piau
- Gérontopôle, CHU Toulouse, Toulouse, France.,Oregon Center for Aging & Technology (ORCATECH), Oregon Health & Science University, Portland, OR, United States
| | | | - Fati Nourhashemi
- Gérontopôle, CHU Toulouse, Toulouse, France.,UMR 1027, INSERM, Toulouse, France
| | - Maria Soto Martin
- Gérontopôle, CHU Toulouse, Toulouse, France.,UMR 1027, INSERM, Toulouse, France
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7
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Piau A, Wild K, Mattek N, Kaye J. Current State of Digital Biomarker Technologies for Real-Life, Home-Based Monitoring of Cognitive Function for Mild Cognitive Impairment to Mild Alzheimer Disease and Implications for Clinical Care: Systematic Review. J Med Internet Res 2019; 21:e12785. [PMID: 31471958 PMCID: PMC6743264 DOI: 10.2196/12785] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 05/22/2019] [Accepted: 06/29/2019] [Indexed: 12/16/2022] Open
Abstract
Background Among areas that have challenged the progress of dementia care has been the assessment of change in symptoms over time. Digital biomarkers are defined as objective, quantifiable, physiological, and behavioral data that are collected and measured by means of digital devices, such as embedded environmental sensors or wearables. Digital biomarkers provide an alternative assessment approach, as they allow objective, ecologically valid, and long-term follow-up with continuous assessment. Despite the promise of a multitude of sensors and devices that can be applied, there are no agreed-upon standards for digital biomarkers, nor are there comprehensive evidence-based results for which digital biomarkers may be demonstrated to be most effective. Objective In this review, we seek to answer the following questions: (1) What is the evidence for real-life, home-based use of technologies for early detection and follow-up of mild cognitive impairment (MCI) or dementia? And (2) What transformation might clinicians expect in their everyday practices? Methods A systematic search was conducted in PubMed, Cochrane, and Scopus databases for papers published from inception to July 2018. We searched for studies examining the implementation of digital biomarker technologies for mild cognitive impairment or mild Alzheimer disease follow-up and detection in nonclinic, home-based settings. All studies that included the following were examined: community-dwelling older adults (aged 65 years or older); cognitively healthy participants or those presenting with cognitive decline, from subjective cognitive complaints to early Alzheimer disease; a focus on home-based evaluation for noninterventional follow-up; and remote diagnosis of cognitive deterioration. Results An initial sample of 4811 English-language papers were retrieved. After screening and review, 26 studies were eligible for inclusion in the review. These studies ranged from 12 to 279 participants and lasted between 3 days to 3.6 years. Most common reasons for exclusion were as follows: inappropriate setting (eg, hospital setting), intervention (eg, drugs and rehabilitation), or population (eg, psychiatry and Parkinson disease). We summarized these studies into four groups, accounting for overlap and based on the proposed technological solutions, to extract relevant data: (1) data from dedicated embedded or passive sensors, (2) data from dedicated wearable sensors, (3) data from dedicated or purposive technological solutions (eg, games or surveys), and (4) data derived from use of nondedicated technological solutions (eg, computer mouse movements). Conclusions Few publications dealt with home-based, real-life evaluations. Most technologies were far removed from everyday life experiences and were not mature enough for use under nonoptimal or uncontrolled conditions. Evidence available from embedded passive sensors represents the most relatively mature research area, suggesting that some of these solutions could be proposed to larger populations in the coming decade. The clinical and research communities would benefit from increasing attention to these technologies going forward.
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Affiliation(s)
- Antoine Piau
- Gerontopole, University Hospital of Toulouse, Université Paul Sabatier, Toulouse, France.,Oregon Center for Aging and Technology, Oregon Health and Science University, Portland, OR, United States
| | - Katherine Wild
- Oregon Center for Aging and Technology, Oregon Health and Science University, Portland, OR, United States
| | - Nora Mattek
- Oregon Center for Aging and Technology, Oregon Health and Science University, Portland, OR, United States
| | - Jeffrey Kaye
- Oregon Center for Aging and Technology, Oregon Health and Science University, Portland, OR, United States
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8
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Astell AJ, Bouranis N, Hoey J, Lindauer A, Mihailidis A, Nugent C, Robillard JM. Technology and Dementia: The Future is Now. Dement Geriatr Cogn Disord 2019; 47:131-139. [PMID: 31247624 PMCID: PMC6643496 DOI: 10.1159/000497800] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Technology has multiple potential applications to dementia from diagnosis and assessment to care delivery and supporting ageing in place. OBJECTIVES To summarise key areas of technology development in dementia and identify future directions and implications. METHOD Members of the US Alzheimer's Association Technology Professional Interest Area involved in delivering the annual pre-conference summarised existing knowledge on current and future technology developments in dementia. RESULTS The main domains of technology development are as follows: (i) diagnosis, assessment and monitoring, (ii) maintenance of functioning, (iii) leisure and activity, (iv) caregiving and management. CONCLUSIONS The pace of technology development requires urgent policy, funding and practice change, away from a narrow medical approach, to a holistic model that facilitates future risk reduction and prevention strategies, enables earlier detection and supports implementation at scale for a meaningful and fulfilling life with dementia.
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Affiliation(s)
- Arlene J. Astell
- Department of Occupational Sciences and Occupational Therapy, University of Toronto, Toronto, Ontario, Canada,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada,Toronto Rehabilitation Institute, Toronto, Toronto, Ontario, Canada,School of Psychology and Clinical Language Sciences, University of Reading, Reading, United Kingdom,*Arlene J. Astell, School of Psychology & Clinical Language Sciences, University of Reading, Reading (UK), E-Mail
| | - Nicole Bouranis
- Layton Aging and Alzheimer's Disease Center, Oregon Health and Science University, Portland, Oregon, USA
| | - Jesse Hoey
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
| | - Allison Lindauer
- Oregon Roybal Center for Aging and Technology (ORCATECH), Oregon Health and Science University, Portland, Oregon, USA
| | - Alex Mihailidis
- Department of Occupational Sciences and Occupational Therapy, University of Toronto, Toronto, Ontario, Canada
| | - Chris Nugent
- School of Computing, Ulster University, Northern Ireland, United Kingdom
| | - Julie M. Robillard
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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Lussier M, Lavoie M, Giroux S, Consel C, Guay M, Macoir J, Hudon C, Lorrain D, Talbot L, Langlois F, Pigot H, Bier N. Early Detection of Mild Cognitive Impairment With In-Home Monitoring Sensor Technologies Using Functional Measures: A Systematic Review. IEEE J Biomed Health Inform 2018; 23:838-847. [PMID: 29994013 DOI: 10.1109/jbhi.2018.2834317] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The aging of the world population is accompanied by a substantial increase in neurodegenerative disorders, such as dementia. Early detection of mild cognitive impairment (MCI), a clinical diagnostic that comes with an increased chance to develop dementias, could be an essential condition for promoting quality of life and independent living, as it would provide a critical window for the implementation of early pharmacological and nonpharmacological interventions. This systematic review aims to investigate the current state of knowledge on the effectiveness of smart home sensors technologies for the early detection of MCI through the monitoring of everyday life activities. This approach offers many advantages, including the continuous measurement of functional abilities in ecological environments. A systematic search of publications in MEDLINE, EMBASE, and CINAHL, before November 2017, was conducted. Seventeen studies were included in this review. Thirteen studies were based on real-life monitoring, with several sensors installed in participants' actual homes, and four studies included scenario-based assessments, in which participants had to complete various tasks in a research lab apartment. In real-life monitoring, the most used indicators of MCI were walking speed and activity/motion in the house. In scenario-based assessment, time of completion, quality of activity completion, number of errors, amount of assistance needed, and task-irrelevant behaviors during the performance of everyday activities predicted MCI in participants. Despite technological limitations and the novelty of the field, smart home technologies represent a promising potential for the early screening of MCI and could support clinicians in geriatric care.
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10
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A Review of Smart House Analysis Methods for Assisting Older People Living Alone. JOURNAL OF SENSOR AND ACTUATOR NETWORKS 2017. [DOI: 10.3390/jsan6030011] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Smart Houses are a prominent field of research referring to environments adapted to assist people in their everyday life. Older people and people with disabilities would benefit the most from the use of Smart Houses because they provide the opportunity for them to stay in their home for as long as possible. In this review, the developments achieved in the field of Smart Houses for the last 16 years are described. The concept of Smart Houses, the most used analysis methods, and current challenges in Smart Houses are presented. A brief introduction of the analysis methods is given, and their implementation is also reported.
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11
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Urwyler P, Stucki R, Rampa L, Müri R, Mosimann UP, Nef T. Cognitive impairment categorized in community-dwelling older adults with and without dementia using in-home sensors that recognise activities of daily living. Sci Rep 2017; 7:42084. [PMID: 28176828 PMCID: PMC5296716 DOI: 10.1038/srep42084] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 01/03/2017] [Indexed: 11/28/2022] Open
Abstract
Cognitive impairment due to dementia decreases functionality in Activities of Daily Living (ADL). Its assessment is useful to identify care needs, risks and monitor disease progression. This study investigates differences in ADL pattern-performance between dementia patients and healthy controls using unobtrusive sensors. Around 9,600 person-hours of activity data were collected from the home of ten dementia patients and ten healthy controls using a wireless-unobtrusive sensors and analysed to detect ADL. Recognised ADL were visualized using activity maps, the heterogeneity and accuracy to discriminate patients from healthy were analysed. Activity maps of dementia patients reveal unorganised behaviour patterns and heterogeneity differed significantly between the healthy and diseased. The discriminating accuracy increases with observation duration (0.95 for 20 days). Unobtrusive sensors quantify ADL-relevant behaviour, useful to uncover the effect of cognitive impairment, to quantify ADL-relevant changes in the course of dementia and to measure outcomes of anti-dementia treatments.
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Affiliation(s)
- Prabitha Urwyler
- Gerontechnology &Rehabilitation Group, University of Bern, Bern, Switzerland.,ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,University Hospital of Old Age Psychiatry, University of Bern, Anna-Seiler-Haus,Bern, Switzerland
| | - Reto Stucki
- Gerontechnology &Rehabilitation Group, University of Bern, Bern, Switzerland
| | - Luca Rampa
- University Hospital of Old Age Psychiatry, University of Bern, Anna-Seiler-Haus,Bern, Switzerland
| | - René Müri
- Gerontechnology &Rehabilitation Group, University of Bern, Bern, Switzerland.,University Neurorehabilitation Clinics, Department of Neurology, Inselspital, and University of Bern, Anna-Seiler-Haus,, Bern, Switzerland
| | - Urs P Mosimann
- Gerontechnology &Rehabilitation Group, University of Bern, Bern, Switzerland.,ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,University Hospital of Old Age Psychiatry, University of Bern, Anna-Seiler-Haus,Bern, Switzerland
| | - Tobias Nef
- Gerontechnology &Rehabilitation Group, University of Bern, Bern, Switzerland.,ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
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12
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Rentz DM. Validating Use of Technology for Cognitive Test Assessment. EBioMedicine 2016; 11:23-24. [PMID: 27498366 PMCID: PMC5049922 DOI: 10.1016/j.ebiom.2016.08.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 08/01/2016] [Indexed: 11/17/2022] Open
Affiliation(s)
- Dorene M Rentz
- Harvard Medical School, Departments of Neurology, Brigham and Women's Hospital, Massachusetts General Hospital, 221 Longwood Avenue M97, Boston, MA 02115, United States.
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13
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Lyons BE, Austin D, Seelye A, Petersen J, Yeargers J, Riley T, Sharma N, Mattek N, Wild K, Dodge H, Kaye JA. Pervasive Computing Technologies to Continuously Assess Alzheimer's Disease Progression and Intervention Efficacy. Front Aging Neurosci 2015; 7:102. [PMID: 26113819 PMCID: PMC4462097 DOI: 10.3389/fnagi.2015.00102] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2015] [Accepted: 05/13/2015] [Indexed: 11/24/2022] Open
Abstract
Traditionally, assessment of functional and cognitive status of individuals with dementia occurs in brief clinic visits during which time clinicians extract a snapshot of recent changes in individuals’ health. Conventionally, this is done using various clinical assessment tools applied at the point of care and relies on patients’ and caregivers’ ability to accurately recall daily activity and trends in personal health. These practices suffer from the infrequency and generally short durations of visits. Since 2004, researchers at the Oregon Center for Aging and Technology (ORCATECH) at the Oregon Health and Science University have been working on developing technologies to transform this model. ORCATECH researchers have developed a system of continuous in-home monitoring using pervasive computing technologies that make it possible to more accurately track activities and behaviors and measure relevant intra-individual changes. We have installed a system of strategically placed sensors in over 480 homes and have been collecting data for up to 8 years. Using this continuous in-home monitoring system, ORCATECH researchers have collected data on multiple behaviors such as gait and mobility, sleep and activity patterns, medication adherence, and computer use. Patterns of intra-individual variation detected in each of these areas are used to predict outcomes such as low mood, loneliness, and cognitive function. These methods have the potential to improve the quality of patient health data and in turn patient care especially related to cognitive decline. Furthermore, the continuous real-world nature of the data may improve the efficiency and ecological validity of clinical intervention studies.
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Affiliation(s)
- Bayard E Lyons
- Oregon Center for Aging and Technology, Oregon Health and Science University , Portland, OR , USA ; Department of Neurology, Oregon Health and Science University , Portland, OR , USA
| | - Daniel Austin
- Oregon Center for Aging and Technology, Oregon Health and Science University , Portland, OR , USA ; Department of Biomedical Engineering, Oregon Health and Science University , Portland, OR , USA
| | - Adriana Seelye
- Oregon Center for Aging and Technology, Oregon Health and Science University , Portland, OR , USA ; Department of Neurology, Oregon Health and Science University , Portland, OR , USA
| | - Johanna Petersen
- Oregon Center for Aging and Technology, Oregon Health and Science University , Portland, OR , USA ; Department of Biomedical Engineering, Oregon Health and Science University , Portland, OR , USA
| | - Jonathan Yeargers
- Oregon Center for Aging and Technology, Oregon Health and Science University , Portland, OR , USA
| | - Thomas Riley
- Oregon Center for Aging and Technology, Oregon Health and Science University , Portland, OR , USA
| | - Nicole Sharma
- Oregon Center for Aging and Technology, Oregon Health and Science University , Portland, OR , USA
| | - Nora Mattek
- Oregon Center for Aging and Technology, Oregon Health and Science University , Portland, OR , USA ; Department of Neurology, Oregon Health and Science University , Portland, OR , USA
| | - Katherine Wild
- Oregon Center for Aging and Technology, Oregon Health and Science University , Portland, OR , USA ; Department of Neurology, Oregon Health and Science University , Portland, OR , USA
| | - Hiroko Dodge
- Oregon Center for Aging and Technology, Oregon Health and Science University , Portland, OR , USA ; Department of Neurology, Oregon Health and Science University , Portland, OR , USA
| | - Jeffrey A Kaye
- Oregon Center for Aging and Technology, Oregon Health and Science University , Portland, OR , USA ; Department of Neurology, Oregon Health and Science University , Portland, OR , USA ; Department of Biomedical Engineering, Oregon Health and Science University , Portland, OR , USA ; Neurology Service, Portland Veteran Affairs Medical Center , Portland, OR , USA
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14
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Austin D, Cross RM, Hayes T, Kaye J. Regularity and predictability of human mobility in personal space. PLoS One 2014; 9:e90256. [PMID: 24587302 PMCID: PMC3937357 DOI: 10.1371/journal.pone.0090256] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2013] [Accepted: 01/30/2014] [Indexed: 11/19/2022] Open
Abstract
Fundamental laws governing human mobility have many important applications such as forecasting and controlling epidemics or optimizing transportation systems. These mobility patterns, studied in the context of out of home activity during travel or social interactions with observations recorded from cell phone use or diffusion of money, suggest that in extra-personal space humans follow a high degree of temporal and spatial regularity – most often in the form of time-independent universal scaling laws. Here we show that mobility patterns of older individuals in their home also show a high degree of predictability and regularity, although in a different way than has been reported for out-of-home mobility. Studying a data set of almost 15 million observations from 19 adults spanning up to 5 years of unobtrusive longitudinal home activity monitoring, we find that in-home mobility is not well represented by a universal scaling law, but that significant structure (predictability and regularity) is uncovered when explicitly accounting for contextual data in a model of in-home mobility. These results suggest that human mobility in personal space is highly stereotyped, and that monitoring discontinuities in routine room-level mobility patterns may provide an opportunity to predict individual human health and functional status or detect adverse events and trends.
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Affiliation(s)
- Daniel Austin
- Oregon Health and Science University, Department of Biomedical Engineering, Portland, Oregon, United States of America
- * E-mail:
| | - Robin M. Cross
- Oregon State University, Agricultural and Resource Economics, Corvallis, Oregon, United States of America
| | - Tamara Hayes
- Oregon Health and Science University, Department of Biomedical Engineering, Portland, Oregon, United States of America
| | - Jeffrey Kaye
- Oregon Health and Science University, Department of Biomedical Engineering, Portland, Oregon, United States of America
- Oregon Health and Science University, Department of Neurology, Portland, Oregon, United States of America
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15
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Parsey CM, Schmitter-Edgecombe M. Applications of technology in neuropsychological assessment. Clin Neuropsychol 2013; 27:1328-61. [PMID: 24041037 DOI: 10.1080/13854046.2013.834971] [Citation(s) in RCA: 93] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Most neuropsychological assessments include at least one measure that is administered, scored, or interpreted by computers or other technologies. Despite supportive findings for these technology-based assessments, there is resistance in the field of neuropsychology to adopt additional measures that incorporate technology components. This literature review addresses the research findings of technology-based neuropsychological assessments, including computer- and virtual reality-based measures of cognitive and functional abilities. We evaluate the strengths and limitations of each approach, and examine the utility of technology-based assessments to obtain supplemental cognitive and behavioral information that may be otherwise undetected by traditional paper-and-pencil measures. We argue that the potential of technology use in neuropsychological assessment has not yet been realized, and continued adoption of new technologies could result in more comprehensive assessment of cognitive dysfunction and in turn, better informed diagnosis and treatments. Recommendations for future research are also provided.
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Affiliation(s)
- Carolyn M Parsey
- a Department of Psychology , Washington State University , Pullman , WA , USA
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16
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Parsey CM, Schmitter-Edgecombe M, Belenky G. Sleep and Everyday Functioning in Older Adulthood. J Appl Gerontol 2012; 34:48-72. [DOI: 10.1177/0733464812458364] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
As individuals age they report increasing numbers of sleep problems (e.g., increased nighttime wakings) and this poorer sleep quality has been associated with increased risk for various medical conditions; however limited research has focused on the implications of sleep quality on everyday functioning in older adulthood. We compared three methods of sleep data collection (wrist actigraphy, self-report questionnaires, and sleep diary) and evaluated their relationships with three approaches to assessing everyday functioning (direct observation, self-report, and paper-and-pencil-based problem-solving tasks) in cognitively healthy older adults. Consistent with previous research, subjective sleep measures correlated significantly with each other but did not correlate with objective sleep measures. Multiple regression analyses revealed neither objective nor subjective sleep measures predicted everyday functioning. Individual variability in sleep may affect prediction of everyday functioning using a cross-sectional sample. Future research should investigate the combined influence of sleep and cognitive factors on everyday functioning in older adults.
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Affiliation(s)
| | | | - Gregory Belenky
- Sleep and Performance Research Center, Washington State University, Spokane, WA, USA
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17
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Woods DL, Yefimova M. Evening cortisol is associated with intra-individual instability in daytime napping in nursing home residents with dementia: an allostatic load perspective. Biol Res Nurs 2012; 14:387-95. [PMID: 22811289 DOI: 10.1177/1099800412451118] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Circadian rhythm disruption, reflected in alterations in sleep-wake activity and daytime napping behavior, is consistently reported in nursing home (NH) residents with dementia. This disruption may be reflected in day-to-day instability. The concept of allostatic load (AL), a measure of cumulative biological burden over a lifetime, may be a helpful model for understanding cortisol diurnal rhythm and daytime napping activity in this population. The purpose of this study was to examine the association between intra-individual daytime napping episodes and basal cortisol diurnal rhythm in NH residents with dementia in the context of AL. METHOD U sing a within-individual longitudinal design (N = 51), the authors observed and recorded daytime napping activity every 20 min for 10 hr per day across 4 consecutive days. The authors obtained saliva samples 4 times each day (upon participants' waking and within 1 hr, 6 hr, and 12 hr of participants' wake time) for cortisol analysis. RESULTS The authors categorized participants as high changers (HCs; day-to-day instability in napping activity) or low changers (LCs; day-to-day stability). There were no significant differences in resident characteristics between groups. There was a significant difference between HCs and LCs in napping episodes (F = 4.86, p = .03), with an interaction effect of evening cortisol on napping episodes in the HC group (F = 10.161, p = .001). CONCLUSIONS NH residents with unstable day-to-day napping episodes are more responsive to alterations in evening cortisol, an index of a dysregulated hypothalamic-pituitary-adrenal (HPA) axis. They may also be more amenable to environmental intervention, an avenue for further research.
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Affiliation(s)
- Diana Lynn Woods
- School of Nursing, University of California, Los Angeles, 90095, USA.
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18
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House S, Connell S, Milligan I, Austin D, Hayes TL, Chiang P. Indoor localization using pedestrian dead reckoning updated with RFID-based fiducials. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:7598-601. [PMID: 22256097 DOI: 10.1109/iembs.2011.6091873] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We describe a low-cost wearable system that tracks the location of individuals indoors using commonly available inertial navigation sensors fused with radio frequency identification (RFID) tags placed around the smart environment. While conventional pedestrian dead reckoning (PDR) calculated with an inertial measurement unit (IMU) is susceptible to sensor drift inaccuracies, the proposed wearable prototype fuses the drift-sensitive IMU with a RFID tag reader. Passive RFID tags placed throughout the smart-building then act as fiducial markers that update the physical locations of each user, thereby correcting positional errors and sensor inaccuracy. Experimental measurements taken for a 55 m × 20 m 2D floor space indicate an over 1200% improvement in average error rate of the proposed RFID-fused system over dead reckoning alone.
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Affiliation(s)
- Samuel House
- School of Electrical Engineering and Computer Sciences, Oregon State University, Corvallis, OR, USA.
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19
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Abstract
Motor speed is an important indicator and predictor of both cognitive and physical function. One common assessment of motor speed is the finger-tapping test (FTT), which is typically administered as part of a neurological or neuropsychological assessment. However, the FTT suffers from several limitations, including infrequent in-person administration, the need for a trained assessor and dedicated equipment, and potential short-term sensory-motor fatigue. In this article, we propose an alternative method of measuring motor speed, with face validity to the FTT, that addresses these limitations by measuring the interkeystroke intervals (IKI) of familiar and repeated login data collected in the home during a subject's regular computer use. We show significant correlations between the mean tapping speeds from the FTT and the median IKIs of the nondominant (r = .77) and dominant (r = .70) hands, respectively, in an elderly cohort of subjects living independently. Finally, we discuss how the proposed method for measuring motor speed fits well into the framework of unobtrusive and continuous in-home assessment.
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20
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Déjos M, Sauzéon H, N'kaoua B. [Virtual reality for clinical assessment of elderly people: early screening for dementia]. Rev Neurol (Paris) 2011; 168:404-14. [PMID: 22137150 DOI: 10.1016/j.neurol.2011.09.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2011] [Revised: 07/18/2011] [Accepted: 09/20/2011] [Indexed: 11/24/2022]
Abstract
Today, there are 24.3 million people suffering from dementia worldwide, that is a new case every 7 seconds (Ferri et al., 2005) and more than 80 million cases expected in 2040. Aging-related morbidity is a real social problem making screening a major challenge. Currently, screening and diagnostic tools for dementia remain independent from each other, screening tools being non-specific and diagnostic tools non-naturalistic. With the technological possibilities offered by virtual reality, it is becoming easier to investigate cognition and behavior in elderly people. Virtual reality allows a better understanding and assessment, and perhaps could stimulate cognitive functioning of elderly people. Combining measurements of cognitive impairment and disability might help close the gap between structural and naturalistic validity.
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Affiliation(s)
- M Déjos
- Laboratoire EA4136, université Bordeaux-Saignat, 146 rue Léo-Saignat, Bordeaux, France.
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Matic A, Mehta P, Rehg JM, Osmani V, Mayora O. Monitoring dressing activity failures through RFID and video. Methods Inf Med 2011; 51:45-54. [PMID: 21533305 DOI: 10.3414/me10-02-0026] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2010] [Accepted: 12/01/2010] [Indexed: 11/09/2022]
Abstract
BACKGROUND Monitoring and evaluation of Activities of Daily Living in general, and dressing activity in particular, is an important indicator in the evaluation of the overall cognitive state of patients. In addition, the effectiveness of therapy in patients with motor impairments caused by a stroke, for example, can be measured through long-term monitoring of dressing activity. However, automatic monitoring of dressing activity has not received significant attention in the current literature. OBJECTIVES Considering the importance of monitoring dressing activity, the main goal of this work was to investigate the possibility of recognizing dressing activities and automatically identifying common failures exhibited by patients suffering from motor or cognitive impairments. METHODS The system developed for this purpose comprised analysis of RFID (radio frequency identification) tracking and computer vision processing. Eleven test subjects, not connected to the research, were recruited and asked to perform the dressing task by choosing any combination of clothes without further assistance. Initially the test subjects performed correct dressing and then they were free to choose from a set of dressing failures identified from the current research literature. RESULTS The developed system was capable of automatically recognizing common dressing failures. In total, there were four dressing failures observed for upper garments and three failures for lower garments, in addition to recognizing successful dressing. The recognition rate for identified dressing failures was between 80% and 100%. CONCLUSIONS We developed a robust system to monitor the dressing activity. Given the importance of monitoring the dressing activity as an indicator of both cognitive and motor skills the system allows for the possibility of long term tracking and continuous evaluation of the dressing task. Long term monitoring can be used in rehabilitation and cognitive skills evaluation.
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Affiliation(s)
- A Matic
- Ubiquitous Interaction Group, CREATE-NET, via alla Cascata 56/D, 38123 Povo, Trento, Italy.
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Evans DA, Grodstein F, Loewenstein D, Kaye J, Weintraub S. Reducing case ascertainment costs in U.S. population studies of Alzheimer's disease, dementia, and cognitive impairment-Part 2. Alzheimers Dement 2011; 7:110-23. [PMID: 21255748 PMCID: PMC3033654 DOI: 10.1016/j.jalz.2010.11.008] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Dementia of the Alzheimer's type (DAT) is a major public health threat in developed countries where longevity has been extended to the eighth decade of life. Estimates of prevalence and incidence of DAT vary with what is measured, be it change from a baseline cognitive state or a clinical diagnostic endpoint, such as Alzheimer's disease. Judgment of what is psychometrically "normal" at the age of 80 years implicitly condones a decline from what is normal at the age of 30. However, because cognitive aging is very heterogeneous, it is reasonable to ask "Is 'normal for age' good enough to screen for DAT or its earlier precursors of cognitive impairment?" Cost containment and accessibility of ascertainment methods are enhanced by well-validated and reliable methods such as screening for cognitive impairment by telephone interviews. However, focused assessment of episodic memory, the key symptom associated with DAT, might be more effective at distinguishing normal from abnormal cognitive aging trajectories. Alternatively, the futuristic "Smart Home," outfitted with unobtrusive sensors and data storage devices, permits the moment-to-moment recording of activities so that changes that constitute risk for DAT can be identified before the emergence of symptoms.
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Affiliation(s)
- Denis A Evans
- Rush Institute on Healthy Aging, Rush University Medical Center, Chicago, IL, USA.
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Hagler S, Austin D, Hayes TL, Kaye J, Pavel M. Unobtrusive and ubiquitous in-home monitoring: a methodology for continuous assessment of gait velocity in elders. IEEE Trans Biomed Eng 2009; 57:813-20. [PMID: 19932989 DOI: 10.1109/tbme.2009.2036732] [Citation(s) in RCA: 65] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Gait velocity has been shown to quantitatively estimate risk of future hospitalization, a predictor of disability, and has been shown to slow prior to cognitive decline. In this paper, we describe a system for continuous and unobtrusive in-home assessment of gait velocity, a critical metric of function. This system is based on estimating walking speed from noisy time and location data collected by a "sensor line" of restricted view passive infrared motion detectors. We demonstrate the validity of our system by comparing with measurements from the commercially available GAITRite walkway system gait mat. We present the data from 882 walks from 27 subjects walking at three different subject-paced speeds (encouraged to walk slowly, normal speed, or fast) in two directions through a sensor line. The experimental results show that the uncalibrated system accuracy (average error) of estimated velocity was 7.1 cm/s (SD = 11.3 cm/s), which improved to 1.1 cm/s (SD = 9.1 cm/s) after a simple calibration procedure. Based on the average measured walking speed of 102 cm/s, our system had an average error of less than 7% without calibration and 1.1% with calibration.
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Affiliation(s)
- Stuart Hagler
- Division of Biomedical Engineering, Oregon Health and Science University, Portland, OR 97239, USA.
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