Research on human gait prediction and recognition algorithm of lower limb-assisted exoskeleton robot

Qin, Tao; Yang, Yong; Wen, Bangchun; Chen, Zhengxiang; Bao, Zhong; Dong, Hao; Dou, Kai; Yang, Changmao

doi:10.1007/s11370-021-00367-6

Cited by 18 publications

(11 citation statements)

References 23 publications

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“…The ways to acquire depth information can be divided into two categories according to the different sensors used: active and passive. The active method uses sensors such as laser, radar, infrared, and ultrasonic to emit energy waves to the measured target and calculates the position of the target by calculating the TOF (Time-Of-Flight) of the energy wave [ 9 , 10 ]. This type of method has good stability, high accuracy, and fast calculation processing, but the cost of active sensors is high.…”

Section: Human Motion Recognition Algorithm Based On Depth Informationmentioning

confidence: 99%

“…But its weight coefficient depends on the product of domain kernel formula (9) and value domain kernel formula (10). After the two are multiplied, taking into account the difference between the spatial domain and the value domain at the same time, a data-dependent bilateral filtering weight function is generated as shown in…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

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Design of Children’s Motor Skills Training Management System Based on Human Movement Recognition Algorithm of Depth Information

Cheng

2022

Computational Intelligence and Neuroscience

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With the deepening of the concept of all-round development, the training of children’s motor skills is becoming more and more popular. Children’s motor skills training management system is based on the product of children’s motor skills training combined with modern technology. This article aims to design a management system for children’s motor skills training based on human motion recognition methods. This article proposes a human motion recognition algorithm based on depth information. The traditional human motion recognition algorithm is more biased towards children’s motor skills. For the design of the system, this article also pays great attention to children’s experience. After analyzing the performance of the system in this study, it is found that the system is not ideal for multitarget human motion recognition. Therefore, this article optimizes the recognition. After the optimization, the recognition rate of the head, upper body, and lower body of the multiobjective human motion recognition is more than 80%.

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Section: Human Motion Recognition Algorithm Based On Depth Informationmentioning

confidence: 99%

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

Design of Children’s Motor Skills Training Management System Based on Human Movement Recognition Algorithm of Depth Information

Cheng

2022

Computational Intelligence and Neuroscience

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“…For each cluster in the classification results, the average dimension of the annotation box in the same cluster is obtained, so as to obtain a group of anchor candidate box dimensions representative of the person size in the human keypoint dataset. In this study, the character annotation box provided by the MS COCO dataset is clustered [ 19 – 22 ]. Considering that the characters have rich actions, resulting in a large difference in the vertical and horizontal proportion of characters, the number of cluster clusters is increased to 13, and the dimension of the dimension box in each cluster is averaged.…”

Section: Methodsmentioning

confidence: 99%

Motion Recognition Based on Deep Learning and Human Joint Points

Jun-ping

2022

Computational Intelligence and Neuroscience

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In order to solve the problem that the traditional feature extraction methods rely on manual design, the research method is changed from the traditional method to the deep learning method based on convolutional neural networks. The experimental results show that the larger average DTW occurs near the 55th calculation, that is, about the 275th frame of the video. In the 55th calculation, the joint angle with the largest DTW distance is the right knee joint. A multiscene action similarity analysis algorithm based on human joint points has been realized. In the fitness scene, by analyzing the joint angle through cosine similarity, the time of fitness key posture in the action sequence can be recognized. In the sports scene, through the similarity analysis of joint angle sequences by the DTW algorithm, we can get the similarity between people’s actions in the sports video and the joint positions with large differences in some time intervals, and the real validity of the experiment is verified. The accuracy of motion recognition before and after the improvement is 95.2% and 97.1%, which is 0.19% higher than that before the improvement. The methods and results are widely used in the fields of sports recognition, movement specification, sports training, health management, and so on.

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“…Based on the hierarchical cluster analysis knowledge identification, the classification of rural tourism features is greatly affected by the accuracy of the weight index, so it is very important to select the feature classification index. In this paper, rural tourism features are divided into four categories: industry-driven, social effect, infrastructure construction, and human environment, and 16 specific feature classification indicators are divided on this basis, thus constituting a feature classification system with hierarchical structure [7][8][9].…”

Section: Construction Of Rural Tourism Characteristic Classification ...mentioning

confidence: 99%

Classification of Rural Tourism Features Based on Hierarchical Clustering Analysis Knowledge Recognition Algorithm

Wang

Sui-ying

Chen

et al. 2022

Mathematical Problems in Engineering

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The vigorous development of tourism has made rural tourism a highlight of the new era. In order to better realize the classification of rural tourism features, this paper proposes a knowledge recognition algorithm based on hierarchical clustering analysis. Firstly, the rationality of the optimization of the rural tourism feature algorithm is analyzed in this paper; secondly, the rural tourism feature classification index system is constructed based on the hierarchical clustering analysis; finally, the index weights are clearly divided according to the characteristics through the hierarchical clustering analysis of the knowledge identification algorithm. According to the specialties of hierarchical clustering analysis, the criteria of the algorithm are determined, and the characteristics of rural tourism are carefully classified. Rural tourists can visit different scenic spots in high density and improve the mobility of rural tourism. The experimental results show that: through the analysis of the characteristic classification data of the Rural Tourism College in this paper, it can be seen that the average daily income of the scenic spot is higher than that of the traditional rural scenic spot. The average daily income of rural tourism has increased by more than 261,900 yuan, which has largely promoted the development trend of rural tourism in my country. It is proved that the hierarchical clustering analysis method is helpful for rational zoning and serious thinking about the characteristics of rural tourism. This paper provides a reference for promoting the classification of rural tourism.

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Research on human gait prediction and recognition algorithm of lower limb-assisted exoskeleton robot

Cited by 18 publications

References 23 publications

Design of Children’s Motor Skills Training Management System Based on Human Movement Recognition Algorithm of Depth Information

Design of Children’s Motor Skills Training Management System Based on Human Movement Recognition Algorithm of Depth Information

Motion Recognition Based on Deep Learning and Human Joint Points

Classification of Rural Tourism Features Based on Hierarchical Clustering Analysis Knowledge Recognition Algorithm

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