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Title:
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DEEP LEARNING-BASED MOTION ACTIVITY
RECOGNITION USING SMARTPHONE SENSORS |
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Author(s):
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Saedeh Abbaspour, Faranak Fotouhi, Hossein Fotouhi, Maryam Vahabi and Maria Linden |
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ISBN:
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978-989-8704-18-4 |
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Editors:
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Mário Macedo |
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Year:
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2020 |
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Edition:
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Single |
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Keywords:
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MHealth, Activity Recognition, Accelerometer, Deep Learning |
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Type:
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Full |
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First Page:
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128 |
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Last Page:
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134 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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MHealth systems establish a new way to transfer the health service to remote places. These systems offer significant
benefits for continuous health monitoring. Motion activity recognition is one of the challenging mHealth use cases that
incorporates continuous data collection and analysis of measurements. The main goal of this research is to analyze physical
activity data. We employ measurements from the WISDM lab dataset 1. These data are collected from participants
performing motion activities. This data is then used by deep learning algorithms to predict special activities. In particular,
CNN and CNN-LSTM algorithms are used to compare their accuracy, which resulted in approximately 95% and 97%
respectively. Thus, the CNN-LSTM has higher accuracy in this analysis. |
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