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Descripción
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| This study analyzes the impact of segmentation methods on deep learning model performance, and compares four data transformation approaches. An experiment with HAR based on acceleration data from multiple wearable devices was conducted. The multichannel method, which treats the data for the three axes as three overlapped color channels, produced the best performance. The highest overall recognition accuracy achieved was 97.20% for eight daily activities, based on the data from seven wearable sensors, which outperformed most of the other machine learning techniques. Moreover, the multichannel approach was applied to three public datasets and produced satisfying results for multi-source acceleration data. The proposed method can help better analyze workers? activities and help to integrate people into CPS. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Sensors |
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ISSN
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1424-8220 |
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Factor de impacto JCR
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2,475 |
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Información de impacto
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Datos JCR del año 2017 |
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Volumen
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18 |
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DOI
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10.3390/s18072146 |
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Número de revista
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7 |
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Desde la página
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1 |
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Hasta la página
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13 |
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Mes
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JULIO |
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Ranking
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