Predicting physical activities from accelerometer readings in spherical coordinate system
© Springer International Publishing AG 2017. Recent advances in mobile computing devices enable smartphone an ability to sense and collect various possibly useful data from a wide range of its sensors. Combining these data with current data mining and machine learning techniques yields interesting a...
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| Main Authors: | , |
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| Format: | Book Series |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85034235448&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/43728 |
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| Institution: | Chiang Mai University |
| Summary: | © Springer International Publishing AG 2017. Recent advances in mobile computing devices enable smartphone an ability to sense and collect various possibly useful data from a wide range of its sensors. Combining these data with current data mining and machine learning techniques yields interesting applications which were not conceivable in the past. One of the most interesting applications is user activities recognition accomplished by analysing information from an accelerometer. In this work, we present a novel framework for classifying physical activities namely, walking, jogging, push-up, squatting and sit-up using readings from mobile phone’s accelerometer. In contrast to the existing methods, our approach first converts the readings which are originally in Cartesian coordinate system into representations in spherical coordinate system prior to a classification step. Experimental results demonstrate that the activities involving rotational movements can be better differentiated by the spherical coordinate system. |
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