Sensor Based Monitoring for People with Dementia: Searching for Movement Markers in Alzheimer’s Disease for a Early Diagnostic

Hoffmeyer, André; Yordanova, Kristina; Teipel, Stefan J.; Kirste, Thomas

doi:10.1007/978-3-642-31479-7_21

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…[ 157 , 158 ] Monitoring typically lasted 2–6 days (6/11 studies, 54.5%). [ 149 , 153 , 155 – 158 ] Most studies involved people with a presumed Alzheimer's dementia or a combination of Alzheimer’s dementia and frontotemporal or Lewy Body dementias (8/11 studies, 72.7%). [ 148 – 150 , 152 , 153 , 155 , 157 , 158 ] Severity of cognitive dysfunction was usually mild to moderate (9/11 studies, 81.8%).…”

Section: Resultsmentioning

confidence: 99%

“…[ 149 , 153 , 155 – 158 ] Most studies involved people with a presumed Alzheimer's dementia or a combination of Alzheimer’s dementia and frontotemporal or Lewy Body dementias (8/11 studies, 72.7%). [ 148 – 150 , 152 , 153 , 155 , 157 , 158 ] Severity of cognitive dysfunction was usually mild to moderate (9/11 studies, 81.8%). [ 148 , 149 , 151 – 157 , 159 ] Only 2 studies involved people with severe cognitive disability.…”

Section: Resultsmentioning

confidence: 99%

“…[ 63 , 69 ]), dementia (e.g. [ 151 , 153 ]), and stroke [ 118 ] than in controls. In people with moderate to severe PD, pattern of activity was different (sedentary bouts were longer) but total volume of sedentary time was similar to controls.…”

Section: Discussionmentioning

confidence: 99%

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Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review

et al. 2016

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ObjectiveTo perform a systematic review of studies using remote physical activity monitoring in neurological diseases, highlighting advances and determining gaps.MethodsStudies were systematically identified in PubMed/MEDLINE, CINAHL and SCOPUS from January 2004 to December 2014 that monitored physical activity for ≥24 hours in adults with neurological diseases. Studies that measured only involuntary motor activity (tremor, seizures), energy expenditure or sleep were excluded. Feasibility, findings, and protocols were examined.Results137 studies met inclusion criteria in multiple sclerosis (MS) (61 studies); stroke (41); Parkinson's Disease (PD) (20); dementia (11); traumatic brain injury (2) and ataxia (1). Physical activity levels measured by remote monitoring are consistently low in people with MS, stroke and dementia, and patterns of physical activity are altered in PD. In MS, decreased ambulatory activity assessed via remote monitoring is associated with greater disability and lower quality of life. In stroke, remote measures of upper limb function and ambulation are associated with functional recovery following rehabilitation and goal-directed interventions. In PD, remote monitoring may help to predict falls. In dementia, remote physical activity measures correlate with disease severity and can detect wandering.ConclusionsThese studies show that remote physical activity monitoring is feasible in neurological diseases, including in people with moderate to severe neurological disability. Remote monitoring can be a psychometrically sound and responsive way to assess physical activity in neurological disease. Further research is needed to ensure these tools provide meaningful information in the context of specific neurological disorders and patterns of neurological disability.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review

et al. 2016

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“…The application of machine learning techniques could also be useful for anomaly detection, whereas more sophisticated analysis could be conducted on data coming from several days of observation. For example, as Alzheimer's disease and dementia are frequently preceded by gait disorders, balance problems, abnormal motor behaviors, and degeneration of the sleep-waking cycle (Nams et al, 2010;Hoffmeyer et al, 2012;Pettersson et al, 2005Pettersson et al, , 2002, the application of machine learning techniques in the framework of the Habitat project could pave the way to enhancements in early detection of these diseases.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Complex reactive event processing for assisted living: The Habitat project case study

Loreti

Chesani

Mello

et al. 2019

Expert Systems with Applications

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While the increasing average age of population is posing new challenges to societies and healthcare systems, the emergence of the Internet of Things research area is generating the hope for automated assisted environments, which could combine the advances in sensors networks with that of runtime monitoring systems, in order to create smart houses able to take care of their older inhabitants and delay the recourse to hospitals and nursing homes. However, although various assisted living systems have been proposed in the last decade, the goal of realizing an effective domestic support system for elderly is still far from reached.In this work, we present a project aiming to re-engineer a set of everyday life objects, equipping them with environmental and wearable sensors, thus

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Cognitive Modeling and Support for Ambient Assistance

Michael

Grießer

Strobl

et al. 2013

Lecture Notes in Business Information Processing

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Sensor Based Monitoring for People with Dementia: Searching for Movement Markers in Alzheimer’s Disease for a Early Diagnostic

Cited by 3 publications

References 13 publications

Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review

Remote Physical Activity Monitoring in Neurological Disease: A Systematic Review

Complex reactive event processing for assisted living: The Habitat project case study

Cognitive Modeling and Support for Ambient Assistance

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