TY - JOUR
T1 - Machine Learning-Based Human Motion Recognition via Wearable plastic Fiber Sensing System
AU - Wu, Qiang
AU - Liu, Bin
AU - Wang, Yu-Lin
AU - Hu, Yingying
AU - Liu, Juan
AU - He, Xing-Dao
AU - Yuan, Jinhui
AU - Wang, Shuang
N1 - Funding information: This work was supported in part by the National Natural
Science Foundation of China (NSFC) under Grant 11864025, Grant 62175097,
and Grant 62065013; and in part by the Natural Science Foundation of Jiangxi
Province under Grant 20212BAB202024.
PY - 2023/10/15
Y1 - 2023/10/15
N2 - 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.
AB - 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.
KW - Human motion recognition
KW - MobileNetV2 network
KW - plastic optical fiber (POF)
KW - support vector machine (SVM)
KW - transfer learning
KW - wearable device
UR - http://www.scopus.com/inward/record.url?scp=85160217103&partnerID=8YFLogxK
U2 - 10.1109/jiot.2023.3277829
DO - 10.1109/jiot.2023.3277829
M3 - Article
SN - 2327-4662
VL - 10
SP - 17893
EP - 17904
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 20
ER -