Agitation Detection in People Living with Dementia using Multimodal Sensors

Khan, Shehroz S.; Spasojević, Sofija; Nogas, Jacob; Yan, Bing; Mihailidis, Alex; Iaboni, Andrea; Wang, Angel; Martin, Lori Schindel; Newman, Kristine

doi:10.1109/embc.2019.8857781

Cited by 27 publications

(48 citation statements)

References 12 publications

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“…Verhaltensauffälligkeiten gehören häufig zum klinischen Bild von Demenzerkrankungen [14]. Diese stellen eine besondere Herausforderung in der Betreuung von DemenzpatientInnen dar, sie werden vom Caregiver jedoch oft nur unzureichend berichtet, bzw.…”

Section: Verhaltensauffälligkeitenunclassified

“…Ergänzt wird dies auch durch multimodale Sensordaten, die physiologische Daten erfassen, und eine höhere Genauigkeit haben sollen. Präliminäre Daten zeigten bisher, dass über einen Zeitraum von 28 Tagen Episoden mit Agitiertheit durch multimodale Sensordaten verlässlich detektiert wer-den konnten [14]. Eine andere Arbeit zeigte, dass auch signifikante Life Events des Patienten (sei es positive oder negative) durch den Einsatz von Wearables, welche sowohl physiologische als auch Akzelerometer-Daten auswerteten, detektiert werden konnten [16].…”

Section: Verhaltensauffälligkeitenunclassified

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Wearables bei Demenzerkrankungen

Pirker-Kees

Baumgartner

2021

Klinische Neurophysiologie

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ZusammenfassungDemenzerkrankungen führen durch den schleichenden Abbau kognitiver, sozialer und emotionaler Fähigkeiten, auch zu einem Verlust von Autonomie und Selbstbestimmtheit. Wearables sind am Körper getragene Sensoren: Akzelerometer und GPS-Tracker sind im Freizeit- und Fitnessbereich allgegenwärtig – sie zeichnen Bewegungs- und Positionsdaten auf. Das Potenzial, diese bei Demenzpatienten einzusetzen ist groß und wird intensiv beforscht. Wearables sind tlw. auch am Markt erhältlich (bspw. GPS-Tracker in Schuhsohlen). Informationen über Gangbild und Bewegungsdaten können auch Hinweise auf das Sturzrisiko, Verhaltensstörungen/Life-Events oder differenzialdiagnostische Aspekte geben. Trotz des großen Potenzials dürfen ethische Aspekte betreffend die Privatsphäre und den Datenschutz in der Entwicklung nicht außer Acht gelassen werden. Dieser Artikel gibt einen Überblick über die aktuelle Entwicklung von Wearables und damit verbundene ethische Aspekte.

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Section: Verhaltensauffälligkeitenunclassified

Wearables bei Demenzerkrankungen

Pirker-Kees

Baumgartner

2021

Klinische Neurophysiologie

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“…The exclusion criterion is that the patient has other neurological disabilities or has significantly reduced ability to complete instrumental activities of daily living due to other non-dementia conditions. Informed and written consent shall be obtained from both the participant and their caregiver for both of their participations, however if the participant is incapable of consenting, their Next of Kin (NoK) or Power of Attorney (POA) will be asked to provide written and informed consent for them, with verbal assent being sought from the participant [11,13,14,[33][34][35][36][37]. All consenting and assenting parties will be informed they can withdraw from the study at any time without being required to give a reason or justification.…”

Section: Data Collection Experimentsmentioning

confidence: 99%

Challenges in Data Capturing and Collection for Physiological Detection of Dementia-Related Difficulties and Proposed Solutions

Harper

Ghali

Hussain

et al. 2021

Intelligent Computing Theories and Application

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Dementia is a neurodegenerative disease which leads to the individual experiencing difficulties in their daily lives. Often these difficulties cause a large amount of stress, frustration and upset in the individual, however identifying when the difficulties are occurring or beginning can be difficult for caregivers, until the difficulty has caused problematic behavior or undeniable difficulty to the person with dementia. Therefore, a system for identifying the onset of dementia-related difficulties would be helpful in the management of dementia. Previous work highlighted wearable computing-based systems for analyzing physiological data as particularly promising. In this paper, we outline the methodology used to perform a systematic search for a relevant dataset. However, no such dataset was found. As such, a methodology for collecting such a dataset and making it publicly available is proposed, as well as for using it to train classification models that can predict difficulties from the physiological data. Several solutions to overcome the lack of available data are identified and discussed: data collection experiments to collect novel datasets; anonymization and pseudonymization to remove all identifiable data from the dataset; and synthetic data generation to produce a larger, anonymous training dataset. In conclusion, a combination of all the identified methods should ideally be employed in future solutions. Future work should focus on the conductance of the proposed experiment and the sharing of the collected data in the manner proposed, with data ideally being collected from as many people as possible with as many different types of dementia as possible.

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“…Recruitment is the process by which study participants are identified, approached, informed, and asked to participate [15]. Recruitment was discussed in 14 papers [16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Different recruitment methods are employed depending on the setting of the experiment and stage of dementia studied.…”

Section: Recruitmentmentioning

confidence: 99%

Review of Methods for Data Collection Experiments with People with Dementia and the Impact of COVID-19

Harper

Ghali

Hussain

et al. 2021

Intelligent Computing Theories and Application

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The development of a wearable-based system for detecting difficulties in the daily lives of people with dementia would be highly useful in the day-today management of the disease. To develop such a system, it would be necessary to identify physiological indicators of the difficulties, which can be identified by analyzing physiological datasets from people with dementia. However, there is no such data available to researchers. As such, it is vital that data is collected and made available in future. In this paper we perform a review of past physiological data collection experiments conducted with people with dementia and evaluate the methods used at each stage of the experiment. Consideration is also given to the impacts and limitations imposed by the COVID-19 pandemic and lockdowns both on the people with dementia-such people being one of the most at risk and affected groups-and on the efficacy and safety of each of the methods. It is concluded that the choice of method to be utilized in future data collection experiments is heavily dependent on the type and severity of the dementia the participants are experiencing, and that the choice of remote or COVID-secure methods should be used during the COVID-19 pandemic; many of the methods reviewed could allow for the spread of the virus if utilized during a pandemic.

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Agitation Detection in People Living with Dementia using Multimodal Sensors

Cited by 27 publications

References 12 publications

Wearables bei Demenzerkrankungen

Wearables bei Demenzerkrankungen

Challenges in Data Capturing and Collection for Physiological Detection of Dementia-Related Difficulties and Proposed Solutions

Review of Methods for Data Collection Experiments with People with Dementia and the Impact of COVID-19

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