Multi-activity 3D human motion recognition and tracking in composite motion model with synthesized transition bridges

Yu, Jialin; Sun, Jun; Liu, Shengqing; Luo, Shasha

doi:10.1007/s11042-017-4847-y

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…It is a brand-new dance education supplement. Dance movements can be accurately recognized and irregular movements of dancers can be distinguished and corrected using human body motion recognition technology applied to dance image motion recognition [2]. To extract key frames, Wang et al used K-means clustering and then interpolation.…”

Section: Introductionmentioning

confidence: 99%

Stage Performance Characteristics of Minority Dance Based on Human Motion Recognition

Shi

2022

Mobile Information Systems

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Due to the different development history of different nationalities, there is a big gap between traditional cultures. Especially, as an important embodiment and component of traditional culture, ethnic minority dance has unique characteristics. With the development of machine vision technology, human motion recognition has gradually become a hot research direction, but its application research in the field of dance motion recognition is still in its infancy. In this paper, the characteristics of stage performances of ethnic minority dances in the field of intelligent auxiliary training based on human motion recognition technology are introduced. The human body regions in the frame are obtained by using human posture, and 3D_SIFT and optical flow features are extracted from each region. Then, we use the extracted key frames and DTW (dynamic time warping) algorithm to realize the motion recognition of the motion capture data and carry out simulation experiments. The test results show that the algorithm can identify the minority dance video database, effectively identify the dance movements, and realize the movement correction of dancers.

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Section: Introductionmentioning

confidence: 99%

Stage Performance Characteristics of Minority Dance Based on Human Motion Recognition

Shi

2022

Mobile Information Systems

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Human motion tracking and 3D motion track detection technology based on visual information features and machine learning

Zhang

Liao

2022

Neural Comput & Applic

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Smartphone Motion Sensor-Based Complex Human Activity Identification Using Deep Stacked Autoencoder Algorithm for Enhanced Smart Healthcare System

Alo¹,

Nweke

Wah

et al. 2020

Sensors

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Human motion analysis using a smartphone-embedded accelerometer sensor provided important context for the identification of static, dynamic, and complex sequence of activities. Research in smartphone-based motion analysis are implemented for tasks, such as health status monitoring, fall detection and prevention, energy expenditure estimation, and emotion detection. However, current methods, in this regard, assume that the device is tightly attached to a pre-determined position and orientation, which might cause performance degradation in accelerometer data due to changing orientation. Therefore, it is challenging to accurately and automatically identify activity details as a result of the complexity and orientation inconsistencies of the smartphone. Furthermore, the current activity identification methods utilize conventional machine learning algorithms that are application dependent. Moreover, it is difficult to model the hierarchical and temporal dynamic nature of the current, complex, activity identification process. This paper aims to propose a deep stacked autoencoder algorithm, and orientation invariant features, for complex human activity identification. The proposed approach is made up of various stages. First, we computed the magnitude norm vector and rotation feature (pitch and roll angles) to augment the three-axis dimensions (3-D) of the accelerometer sensor. Second, we propose a deep stacked autoencoder based deep learning algorithm to automatically extract compact feature representation from the motion sensor data. The results show that the proposed integration of the deep learning algorithm, and orientation invariant features, can accurately recognize complex activity details using only smartphone accelerometer data. The proposed deep stacked autoencoder method achieved 97.13% identification accuracy compared to the conventional machine learning methods and the deep belief network algorithm. The results suggest the impact of the proposed method to improve a smartphone-based complex human activity identification framework.

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Multi-activity 3D human motion recognition and tracking in composite motion model with synthesized transition bridges

Cited by 3 publications

References 37 publications

Stage Performance Characteristics of Minority Dance Based on Human Motion Recognition

Stage Performance Characteristics of Minority Dance Based on Human Motion Recognition

Human motion tracking and 3D motion track detection technology based on visual information features and machine learning

Smartphone Motion Sensor-Based Complex Human Activity Identification Using Deep Stacked Autoencoder Algorithm for Enhanced Smart Healthcare System

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