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Abstract
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| When developing a fully automatic system for evaluating motor activities performed by a person, it is necessary to segment and recognize the different activities in order to focus the analysis. This process must be carried out by a Human Activity Recognition (HAR) system. This paper proposes a user adaptation technique for improving a HAR system based on Hidden Markov Models (HMMs). This system segments and recognizes six different physical activities (walking, walking upstairs, walking downstairs, sitting, standing and lying down) using inertial signals from a smartphone. The system is composed of a feature extractor for obtaining the most relevantcharacteristicsfromtheinertialsignals,amodulefortrainingthesixHMMs(oneperactivity), and the last module for segmenting new activity sequences using these models. The user adaptation technique consists of a Maximum A Posteriori (MAP) approach that adapts the activity HMMs to the user, using some activity examples from this speci?c user. The main results on a public dataset have reported a signi?cant relative error rate reduction of more than 30%. In conclusion, adapting a HAR system to the user who is performing the physical activities provides signi?cant improvement in the system?s performance. | |
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International
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Si |
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JCR
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No |
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Title
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Algorithms |
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ISBN
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1999-4893 |
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Impact factor JCR
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Impact info
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Volume
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9 |
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10.3390/a9030060 |
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Journal number
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3 |
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From page
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60 |
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To page
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73 |
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Month
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SIN MES |
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Ranking
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