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Individualized Motion Monitoring by Wearable Sensor: Pre-impact fall detection using SVM and sensor fusion
KTH, School of Technology and Health (STH).
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Individanpassad rörelsemonitorering med hjälp av bärbara sensorer (Swedish)
Abstract [en]

Among the elderly, falling represents a major threat to the individual health, and is considered as a major source of morbidity and mortality. In Sweden alone, three elderly are lost each day in accidents related to falling. The elderly who survive the fall are likely to be suffering from decreased quality of life. As the percentage of elderly increase in the population worldwide, the need for preventive methods and tools will grow drastically in order to deal with the increasing health-care costs. This report is the result of a conceptual study where an algorithm for individualized motion monitoring and pre-impact fall detection is developed. The algorithm learns the normal state of the wearer in order to detect anomalous events such as a fall. Furthermore, this report presents the requirements and issues related to the implementation of such a system. The result of the study is presented as a comparison between the individualized system and a more generalized fall detection system. The conclusion is that the presented type of algorithm is capable of learning the user behaviour and is able to detect a fall before the user impacts the ground, with a mean lead time of 301ms.

Abstract [sv]

Bland äldre är risken för att drabbas av fallrelaterade skador överhängande, ofta med svåra fysiska skador och psykiska effekter som följd. Med en ökande andel äldre i befolkningsmängden beräknas även samhällets kostnad för vård att stiga. Genom aktiva samt preventiva åtgärder kan graden av personligt lidande och fallre- laterade samhällskostnader reduceras. Denna rapport är resultatet av en konceptuell studie där en algoritm för aktiv, individanpassad falldetektion utvecklats. Algoritmen lär sig användarens normala rörelsemönster och skall därefter särskilja dessa från onormala rörelsemönster. Rapporten beskriver de krav och frågeställningar som är relevanta för utvecklingen av ett sådant system. Vidare presenteras resultatet av studien i form av en jämförelse mellan ett individanpassat och generellt system. Resultatet av studien visar att algoritmen kan lära sig användarens vanliga rörelsemönster och därefer särskilja dessa från ett fall, i medelvärde 301ms innan användaren träffar marken.

Place, publisher, year, edition, pages
2015. , 130 p.
Series
TRITA-STH, 2015:073
Keyword [en]
support vector machine (SVM), pre-impact fall detection, elderly, accelerometer, gyroscope, MEMS
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-171088OAI: oai:DiVA.org:kth-171088DiVA: diva2:842308
External cooperation
ÅF Technology AB
Educational program
Master of Science - Medical Engineering
Examiners
Available from: 2015-08-12 Created: 2015-07-18 Last updated: 2015-08-12Bibliographically approved

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Individualized Motion Monitoring by Wearable Sensor Pre impact fall detection using SVM and sensor fusion(7617 kB)547 downloads
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CiteExportLink to record
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Citation style
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