scholarly journals Research on Aerobics Training and Evaluation Method Based on Artificial Intelligence-Aided Modeling

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chen Chen
Keyword(s):  
Artificial Intelligence ◽  
Evaluation Method ◽  
Human Motion ◽  
Video Annotation ◽  
Human Model ◽  
Training Methods ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Motion Characteristics ◽  
Motion Video

Traditional aerobics training methods have the problems of lack of auxiliary teaching conditions and low-training efficiency. With the in-depth application of artificial intelligence and computer-aided training methods in the field of aerobics teaching and practice, this paper proposes a local space-time preserving Fisher vector (FV) coding method and monocular motion video automatic scoring technology. Firstly, the gradient direction histogram and optical flow histogram are extracted to describe the motion posture and motion characteristics of the human body in motion video. After normalization and data dimensionality reduction based on the principal component analysis, the human motion feature vector with discrimination ability is obtained. Then, the spatiotemporal pyramid method is used to embed spatiotemporal features in FV coding to improve the ability to identify the correctness and coordination of human behavior. Finally, the linear model of different action classifications is established to determine the action score. In the key frame extraction experiment of the aerobics action video, the ST-FMP model improves the recognition accuracy of uncertain human parts in the flexible hybrid joint human model by about 15 percentage points, and the key frame extraction accuracy reaches 81%, which is better than the traditional algorithm. This algorithm is not only sensitive to human motion characteristics and human posture but also suitable for sports video annotation evaluation, which has a certain reference significance for improving the level of aerobics training.

Vehicle motion and motion sickness in pigs

1998 ◽  
Vol 66 (1) ◽  
pp. 239-245 ◽  
Author(s):  
J. M. Randall ◽  
R. H. Bradshaw
Keyword(s):  
Motion Sickness ◽  
Predictive Models ◽  
Low Frequency ◽  
Vertical Axis ◽  
Human Motion ◽  
Human Model ◽  
Road Vehicles ◽  
Low Frequencies ◽  
Vehicle Motion ◽  
Motion Characteristics

AbstractLow frequency oscillatory motion (0·05 to 0·5 Hz) experienced in ships and road vehicles is known to cause motion sickness in humans and some predictive models are available. There have been very few studies of the incidence of motion sickness in pigs and none which has attempted to identify the frequencies of motion of transporters which are likely to be implicated. In this study, the vibration and motion characteristics of a commercial pig transporter were measured while seven individually penned 40-kg pigs were transported for short (100 min) journeys and 80-kg pigs penned in groups of 12 or 13 were transported for longer (4·5 h) journeys. Direct behavioural observations were made of individual pigs for symptoms of travel sickness (sniffing, foaming at the mouth, chomping, and retching or vomiting). A comparison was then made between the incidence of travel sickness in pigs and that expected in humans given the measured vehicle vibration characteristics. The low frequencies of motion measured on the transporter (0·01 to 0·2 Hz) were well within the range implicated in human motion sickness with considerable power in the longitudinal and lateral axes but little in the vertical axis. On both short and long journeys pigs exhibited symptoms of travel sickness. The likely incidence of travel sickness on the short journeys predicted by the human model was 24 to 31% which corresponds to approximately two of the seven 40-kg pigs becoming travel sick. The numbers observed were generally lower than this although the same pigs were transported twice each day for 2 days and this may have therefore reflected the effects of habituation. The incidence of travel sickness on the long journeys predicted by the human model was 34%. During these journeys which involved four groups of 80-kg pigs which were not repeatedly transported, 26% of pigs vomited or retched (13 out of 50) while 50% showed advanced symptoms of foaming and chomping. These results are not inconsistent with the human model which should form the basis offurther research.

scholarly journals Key Frame Extraction Algorithm of Motion Video Based on Priori

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 174424-174436
Author(s):  
Qi Zhong ◽  
Yuan Zhang ◽  
Jinguo Zhang ◽  
Kaixuan Shi ◽  
Yang Yu ◽  
...  
Keyword(s):  
Key Frame Extraction ◽  
Key Frame ◽  
Extraction Algorithm ◽  
Motion Video

scholarly journals Motion Key frame Extraction Based on Grey Wolf Optimization Algorithm

2018 ◽  
Vol 232 ◽  
pp. 03032
Author(s):  
Yi Zhang ◽  
Juan Li ◽  
Min Zhang
Keyword(s):  
Fitness Function ◽  
Reconstruction Error ◽  
Human Motion ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Motion Data ◽  
Key Frames ◽  
Original Motion

In order to extract the key frames more effectively, we propose a key frame extraction method for human motion sequences based on Grey Wolf Optimization (GWO) algorithm. The fitness function is defined with the minimum reconstruction error and the optimal compression rate. The social hierarchy of grey wolves and hunting strategy are simulated to search key frames. Experimental results show that the proposed method can not only maintain the consistency of key frames between similar human motion sequences, but also effectively compress and summarize the original motion data. Under the same compression ratio, the reconstruction error is the minimum.

Key Frame Extraction from Video

2014 ◽  
Vol 4 (2) ◽  
pp. 68-79 ◽  
Author(s):  
Sergii Mashtalir ◽  
Olena Mikhnova
Keyword(s):  
Artificial Intelligence ◽  
Shot Boundary Detection ◽  
Post Processing ◽  
Extraction Techniques ◽  
Key Frame Extraction ◽  
Estimation Techniques ◽  
Key Frame ◽  
Shot Boundary ◽  
Textual Features ◽  
Key Frames

A complete overview of key frame extraction techniques has been provided. It has been found out that such techniques usually have three phases, namely shot boundary detection as a pre-processing phase, main phase of key frame detection, where visual, structural, audio and textual features are extracted from each frame, then processed and analyzed with artificial intelligence methods, and the last post-processing phase lies in removal of duplicates if they occur in the resulting sequence of key frames. Estimation techniques and available test video collections have been also observed. At the end, conclusions concerning drawbacks of the examined procedure and basic tendencies of its development have been marked.

Artificial Intelligence-Enabled Caregiving Walking Stick Powered by Ultra-Low-Frequency Human Motion

ACS Nano ◽  
2021 ◽  
Author(s):  
Xinge Guo ◽  
Tianyiyi He ◽  
Zixuan Zhang ◽  
Anxin Luo ◽  
Fei Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Low Frequency ◽  
Human Motion ◽  
Walking Stick

scholarly journals Large-Scale Video Retrieval via Deep Local Convolutional Features

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chen Zhang ◽  
Bin Hu ◽  
Yucong Suo ◽  
Zhiqiang Zou ◽  
Yimu Ji
Keyword(s):  
Large Scale ◽  
Video Retrieval ◽  
Video Data ◽  
Query Image ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Storage Cost ◽  
Extraction Algorithm ◽  
Feature Aggregation ◽  
And Storage

In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced. The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database. The results from extensive experiments on two publicly available datasets demonstrate that the proposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods.

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