Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch
Human activity recognition using wearable devices has been actively investigated in a wide range of applications. Most of them, however, either focus on simple activities wherein whole body movement is involved or require a variety of sensors to identify daily activities. In this study, we propose a...
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| Veröffentlicht in: | Wireless communications and mobile computing 2018-01, Vol.2018 (2018), p.1-9 |
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| creator | Kwon, Min-Cheol Choi, Sunwoong |
| description | Human activity recognition using wearable devices has been actively investigated in a wide range of applications. Most of them, however, either focus on simple activities wherein whole body movement is involved or require a variety of sensors to identify daily activities. In this study, we propose a human activity recognition system that collects data from an off-the-shelf smartwatch and uses an artificial neural network for classification. The proposed system is further enhanced using location information. We consider 11 activities, including both simple and daily activities. Experimental results show that various activities can be classified with an accuracy of 95%. |
| doi_str_mv | 10.1155/2018/2618045 |
| format | Article |
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| ispartof | Wireless communications and mobile computing, 2018-01, Vol.2018 (2018), p.1-9 |
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| source | Wiley Online Library Open Access; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Accelerometers Algorithms Artificial neural networks Classification Classification schemes Human activity recognition Internet of Things Machine learning Moving object recognition Neural networks Performance evaluation Sensors Servers Smartphones Smartwatches Standard deviation Wearable technology Writing |
| title | Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch |
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