Research on Human Motion Recognition in Depth Video
XING Mengmeng1, YANG Feng1, XIN Zaihai1, WEI Guohui1
1. Department of Equipment, China Rehabilitation Research Center, Beijing 100071, China; 2. Department of Assets and Equipment,
Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan Shandong 250011, China;
3. Department of Medical Engineering, The First Affiliated Hospital of Shandong First Medical University, Jinan Shandong 250000,
China; 4. School of Intelligent and Information Engineering, Shandong University of Traditional Chinese Medicine,
Jinan Shandong 250300, China
Abstract：Objective Classification based on RGB video sequence is the main way to realize human motion recognition. However,
RGB video can clearly preserve human facial information while recording human motion. In order to protect privacy, this paper
proposes human motion recognition based on depth video. Methods The public UTD-MHAD dataset that contained 27 kinds of
depth video motion data was used for research. Firstly, the depth video sequence were preprocessed and transformed into motion
history map, and the detail information of motion history map were enhanced by pseudo color coding. Secondly, the pseudo color
coded motion history map were sent to the pre-trained convolutional neural network (CNN) to extract the depth feature vector of the
motion history map. Finally, the classifiers were used for classification. Results The human motion recognition method based on
depth video sequence achieved 90.02% accuracy and 1.8% error in UTD-MHAD dataset. Conclusion The human motion recognition
method based on depth video sequence method proposed in this paper has certain validity, and can be used as a new unsupervised
rehabilitation exercise method in the field of human motion recognition, which is helpful to promote the further standardization of
rehabilitation evaluation research.
邢蒙蒙1，杨锋2，辛在海3，魏国辉4. 深度视频下的人体动作识别研究[J]. 中国医疗设备, 2023, 38(1): 36-41.
XING Mengmeng1, YANG Feng1, XIN Zaihai1, WEI Guohui1. Research on Human Motion Recognition in Depth Video. China Medical Devices, 2023, 38(1): 36-41.
