Gesture recognition by using Inertial Measurement Unit (IMU) sensors plays an important role in various Internet of Things (IOT) applications, e.g., smart home, intelligent medical system and so on. Traditional technologies usually utilize machine learning algorithms to train different gestures during the offline phase, then recognize the gesture during the online phase. However, such technologies cannot recognize these gestures without prior training. Even for the same gesture, with different gesture amplitude may result in unsuccessful recognition. Also if we change the person to perform the same gesture, the algorithms fails. In order to overcome these drawbacks, we propose an approach, which will be able to track the human body motion in realtime and also recognize complicated gestures. It utilizes the accelerometer information and proposes comprehensive localization algorithms for each deployed sensor attached on the human body. Then, it takes the correlation and limitation among body parts into account to recognize the gesture. Our experiments results show that, the successful recognition rate of our algorithm is 100%. Furthermore, any part of the human body can be well tracked, the tracking accuracy can reach 0.06m.
|Number of pages||13|
|Journal||IEEE Transactions on Mobile Computing|
|Publication status||Published - 30 Nov 2021|
Bibliographical noteThis research was supported in part by NSFC 61872247, Shenzhen Peacock Talent Grant 827-000175 and Guangdong Natural Science Funds (Grant No.2019A1515011064).
- gesture recognition
- motion tracking
- Wireless communication
- Wireless sensor networks
- Gesture recognition
- Real-time systems