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Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia
Luleå University of Technology, Department of Health Sciences, Nursing Care.
Information Technologies Institute, Centre for Research & Technology Hellas.
Information Technologies Institute, Centre for Research & Technology Hellas.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
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Number of Authors: 82016 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 16, no 12, article id 1989Article in journal (Refereed) Published
Abstract [en]

Stress is a common problem that affects most people with dementia and their caregivers. Stress symptoms for people with dementia are often measured by answering a checklist of questions by the clinical staff who work closely with the person with the dementia. This process requires a lot of effort with continuous observation of the person with dementia over the long term. This article investigates the effectiveness of using a straightforward method, based on a single wristband sensor to classify events of "Stressed" and "Not stressed" for people with dementia. The presented system calculates the stress level as an integer value from zero to five, providing clinical information of behavioral patterns to the clinical staff. Thirty staff members participated in this experiment, together with six residents suffering from dementia, from two nursing homes. The residents were equipped with the wristband sensor during the day, and the staff were writing observation notes during the experiment to serve as ground truth. Experimental evaluation showed relationships between staff observations and sensor analysis, while stress level thresholds adjusted to each individual can serve different scenarios.

Place, publisher, year, edition, pages
2016. Vol. 16, no 12, article id 1989
National Category
Nursing Media and Communication Technology
Research subject
Nursing; Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-60730DOI: 10.3390/s16121989ISI: 000391303000009PubMedID: 27886155Scopus ID: 2-s2.0-84997328010OAI: oai:DiVA.org:ltu-60730DiVA, id: diva2:1050151
Note

Validerad; 2016; Nivå 2; 2016-11-28 (andbra)

Available from: 2016-11-28 Created: 2016-11-28 Last updated: 2018-10-15Bibliographically approved
In thesis
1. Unobtrusive Activity Recognition in Resource-Constrained Environments
Open this publication in new window or tab >>Unobtrusive Activity Recognition in Resource-Constrained Environments
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Diskret Aktivitetsigenkänning i Resursbegränsade Miljöer
Abstract [en]

This thesis discusses activity recognition from a perspective of unobtrusiveness, where devices are worn or placed in the environment without being stigmatising or in the way. The research focuses on performing unobtrusive activity recognition when computational and sensing resources are scarce. This includes investigating unobtrusive ways to gather data, as well as adapting data modelling and classification to small, resource-constrained, devices.

The work presents different aspects of data collection and data modelling when only using unobtrusive sensing. This is achieved by considering how different sensor placements affects prediction performance and how activity models can be created when using a single sensor, or when using a number of simple binary sensors, to perform movement analysis, recognise everyday activities, and perform stress detection. The work also investigates how classification can be performed on resource-constrained devices, resulting in a novel computation-efficient classifier and an efficient hand-made classification model. The work finally sets unobtrusive activity recognition into real-life contexts where it can be used for interventions to reduce stress, sedentary behaviour and symptoms of dementia.

The results indicate that activities can be recognised unobtrusively and that classification can be performed even on resource-constrained devices. This allows for monitoring a user’s activities over extensive periods, which could be used for creating highly personal digital interventions and in-time advice that help users make positive behaviour changes. Such digital health interventions based on unobtrusive activity recognition for resource-constrained environments are important for addressing societal challenges of today, such as sedentary behaviour, stress, obesity, and chronic diseases. The final conclusion is that unobtrusive activity recognition is a cornerstone necessary for bringing many digital health interventions into a wider use.

Abstract [sv]

Denna avhandling diskuterar aktivitetsigenkänning ur ett diskret perspektiv, där enheter bärs eller placeras i miljön utan att vara stigmatiserande eller i vägen. Forskningen fokuserar på att utföra diskret aktivitetsigenkänning när beräknings- och sensor-resurser är knappa. Detta inkluderar att undersöka diskreta sätt att samla in data, samt att anpassa datamodellering och klassificering till små, resursbegränsade enheter.

Arbetet presenterar olika aspekter av datainsamling och datamodellering när man bara använder diskreta sensorer. Detta uppnås genom att överväga hur olika sensorplaceringar påverkar prediktionsprestanda och hur aktivitetsmodeller kan skapas vid användning av en enda sensor eller vid användning av ett antal enkla binära sensorer, för att utföra rörelsesanalys, känna igen vardagliga aktiviteter och utföra stressdetektering. Arbetet undersöker också hur klassificering kan utföras på resursbegränsade enheter, vilket resulterar i en ny beräkningseffektiv klassificeringsalgoritm och en effektiv handgjord klassificeringsmodell. Slutligen sätter arbetet in diskret aktivitetsigenkänning i verkliga sammanhang där det kan användas för interventioner för att minska stress, stillasittande  beteende och symptom på demens.

Resultaten visar att diskret aktivitetsigenkänning är möjligt och att klassificeringen kan utföras även på resursbegränsade enheter. Detta möjliggör övervakning av användarens aktiviteter under längre  perioder, vilket kan användas för att skapa personliga digitala interventioner och tidsanpassad rådgivning som hjälper användarna att göra positiva beteendeförändringar. Sådana digitala hälsointerventioner baserade på diskret aktivitetsigenkänning i resursbegränsade miljöer är viktiga för att ta itu med dagens samhällsutmaningar, såsom stillasittande beteende, stress, fetma och kroniska sjukdomar. En slutsats av arbetet är att diskret aktivitetsigenkänning är en hörnsten som är nödvändig för att få en större användning av digitala hälsointerventioner.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2018
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-71073 (URN)978-91-7790-232-4 (ISBN)978-91-7790-233-1 (ISBN)
Public defence
2018-12-11, C305, Luleå Tekniska Universitet, 97187 Luleå, Luleå, 09:00 (English)
Opponent
Supervisors
Available from: 2018-10-16 Created: 2018-10-15 Last updated: 2018-12-28Bibliographically approved

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Kikhia, BaselKarvonen, NiklasSävenstedt, StefanMelander, Catharina

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