The Effect of Personalization on Smartphone-Based Fall Detectors

The risk of falling is high among different groups of people, such as older people, individuals with Parkinson's disease or patients in neuro-rehabilitation units. Developing robust fall detectors is important for acting promptly in case of a fall. Therefore, in this study we propose to persona...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 16; číslo 1; s. 117
Hlavní autoři: Medrano, Carlos, Plaza, Inmaculada, Igual, Raúl, Sánchez, Ángel, Castro, Manuel
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI 18.01.2016
MDPI AG
Témata:
Accelerometry - instrumentation
Accidental Falls - prevention & control
Activities of Daily Living - classification
Adult
Algorithms
fall detection
Humans
novelty detection
personalization
Precision Medicine - instrumentation
Precision Medicine - methods
Smartphone
Young Adult
personalization
novelty detection
smartphone
fall detection
ISSN:1424-8220, 1424-8220
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Shrnutí:The risk of falling is high among different groups of people, such as older people, individuals with Parkinson's disease or patients in neuro-rehabilitation units. Developing robust fall detectors is important for acting promptly in case of a fall. Therefore, in this study we propose to personalize smartphone-based detectors to boost their performance as compared to a non-personalized system. Four algorithms were investigated using a public dataset: three novelty detection algorithms—Nearest Neighbor (NN), Local Outlier Factor (LOF) and One-Class Support Vector Machine (OneClass-SVM)—and a traditional supervised algorithm, Support Vector Machine (SVM). The effect of personalization was studied for each subject by considering two different training conditions: data coming only from that subject or data coming from the remaining subjects. The area under the receiver operating characteristic curve (AUC) was selected as the primary figure of merit. The results show that there is a general trend towards the increase in performance by personalizing the detector, but the effect depends on the individual being considered. A personalized NN can reach the performance of a non-personalized SVM (average AUC of 0.9861 and 0.9795, respectively), which is remarkable since NN only uses activities of daily living for training.
Bibliografie:ObjectType-Article-1
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ISSN:1424-8220
1424-8220
DOI:10.3390/s16010117