Machine Learning-Based Human Motion Recognition via Wearable plastic Fiber Sensing System

Qiang Wu*, Bin Liu*, Yu-Lin Wang, Yingying Hu, Juan Liu, Xing-Dao He, Jinhui Yuan, Shuang Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
19 Downloads (Pure)


Wearable human-machine interface (HMI) is a medium for information transmission and exchange between people and computers. It is widely used in the fields of human motion capture and recognition and augmented/virtual reality (AR/VR). This research proposes a wearable plastic-optical-fiber (POF) sensing system based on machine learning for human motion recognition. The wearable sports sleeve is designed and worn on the elbow and knee joints of human body. The wearable sensor system uses a D-shaped POF (DPOF) sensor, whose coefficient of determination (R 2) is 0.96496 and sensitivity is-0.7859% per degree. Support vector machines (SVMs), MobileNetV2 network, and transfer learning were used to identify six types of movement: walking, running, going upstairs, going downstairs, high leg lifts, and rope skipping. The accuracy of classification based on the four joint position monitoring can reach 98.28%, 98.94%, and 99.74%, respectively. The proposed POF wearable system has good applications for human motion state recognition and possesses great application potential in AR/VR.

Original languageEnglish
Pages (from-to)17893-17904
Number of pages12
JournalIEEE Internet of Things Journal
Issue number20
Early online date19 May 2023
Publication statusPublished - 15 Oct 2023

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