A child caring robot for the dangerous behavior detection based on the object recognition and human action recognition

Nie, Qiang; Wang, Xin; Wang, Jiangliu; Wang, Manlin; Liu, Yunhui

doi:10.1109/robio.2018.8665218

Cited by 4 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ability to automate the process of detecting, recognizing, and classifying actions performed by young children and infants from visual data can prove useful across applications. Examples include monitoring for safety [4]- [6], studying infants' interaction with caregivers [7], identifying markers for diagnosis of neuromotor disor-ders [1], [2], [8]- [10], as well as-primarily of interest hereinclosing action-perception loops for smart environments and assistive wearable robotic devices [11]- [15].…”

Section: Introductionmentioning

confidence: 99%

BabyNet: A Lightweight Network for Infant Reaching Action Recognition in Unconstrained Environments to Support Future Pediatric Rehabilitation Applications

Dechemi

Bhakri

Sahin

et al. 2021

2021 30th IEEE International Conference on Robot &Amp; Human Interactive Communication (RO-MAN)

View full text Add to dashboard Cite

Action recognition is an important component to improve autonomy of physical rehabilitation devices, such as wearable robotic exoskeletons. Existing human action recognition algorithms focus on adult applications rather than pediatric ones. In this paper, we introduce BabyNet, a light-weight (in terms of trainable parameters) network structure to recognize infant reaching action from off-body stationary cameras. We develop an annotated dataset that includes diverse reaches performed while in a sitting posture by different infants in unconstrained environments (e.g., in home settings, etc.). Our approach uses the spatial and temporal connection of annotated bounding boxes to interpret onset and offset of reaching, and to detect a complete reaching action. We evaluate the efficiency of our proposed approach and compare its performance against other learning-based network structures in terms of capability of capturing temporal inter-dependencies and accuracy of detection of reaching onset and offset. Results indicate our BabyNet can attain solid performance in terms of (average) testing accuracy that exceeds that of other larger networks, and can hence serve as a light-weight data-driven framework for video-based infant reaching action recognition.

show abstract