scholarly journals Human motion characteristics in relation to feeling familiar or frightened during an announced short interaction with a proactive humanoid

2014 ◽  
Vol 8 ◽  
Author(s):  
Ritta Baddoura ◽  
Gentiane Venture
Keyword(s):  
Human Motion ◽  
Motion Characteristics

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 A Biomechanical Model for the Upper Extremity using Optimization Techniques

1974 ◽  
Vol 16 (6) ◽  
pp. 585-594 ◽  
Author(s):  
M. A. Ayoub ◽  
M. M. Ayoub ◽  
A. G. Walvekar
Keyword(s):  
Dynamic Programming ◽  
Geometric Programming ◽  
Three Dimensional ◽  
Biomechanical Model ◽  
Human Motion ◽  
Optimization Techniques ◽  
Future Perspectives ◽  
Underlying Principle ◽  
Motion Characteristics ◽  
Average Individual

A biomechanical model for predicting paths of motion and the associated motion characteristics for the arm articulation joints is presented. The underlying principle of the model is that the average individual follows an optimizing criterion in performing his tasks. A detailed description of the model assumptions, mechanics, and formulation is presented for three-dimensional motions. Three approaches are presented for solving the model: (1) suboptimization (linear and geometric programming); (2) dynamic programming; and (3) simulation. The accuracy and the adequacy of the model in predicting human motion under planar conditions were tested and evaluated. Future perspectives and limitations of modeling human motion are outlined and discussed.

scholarly journals Research on Intention Recognition Method Based on Radial Basis Function Neural Network

2019 ◽  
Vol 48 (4) ◽  
pp. 637-647
Author(s):  
Han Yan ◽  
Ming Han ◽  
Ruoxi Yang ◽  
Tiejun Li
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Human Motion ◽  
Motion Platform ◽  
Single Degree Of Freedom ◽  
Radial Basis ◽  
Impedance Parameters ◽  
Motion Characteristics ◽  
Human Robot Collaboration

A robot should be endowed with certain collaboration experience to recognize human’s behavioral intention. This paper provides a method based on machine learning to recognize the collaborator’s intention. A radial basis function neural network model was built for offline practice of a robot to recognize intention. Some collaboration skills can be obtained by the robot by building a map between the collaborator’s intention and the system state, deducing human’s intention based on the dynamic characteristics of collaborator and robot and taking the collaborator’s intention as the feedforward information for controlling the robot so as to estimate the human’s intention online based on collaborator’s force and robot’s motion characteristics during collaboration. The proposed method can overcome the difficulties in building the human-robot collaboration model by traditional method, especially the complicated human motion model, and difficulties in estimation of impedance parameters of human body. An experiment was conducted on a motion platform with single degree of freedom. The results prove that the collaborator’s force is reduced while synchronization of human-robot collaboration is improved, so that the compliance of collaborated motion is also improved.

scholarly journals Extraction of Motion Characteristics Corresponding to Sensitivity Information Using Dance Movement

2004 ◽  
Vol 8 (2) ◽  
pp. 168-180 ◽  
Author(s):  
Shihoko Kamisato ◽  
◽  
Satoru Odo ◽  
Yoshino Ishikawa ◽  
Kiyoshi Hoshino ◽  
...  
Keyword(s):  
Principal Component ◽  
Body Part ◽  
Human Motion ◽  
Dance Movement ◽  
Sensitivity Evaluation ◽  
The Common ◽  
Leg Motion ◽  
Motion Characteristics ◽  
Physical Features ◽  
The Relationship

This study is intended to quantitatively clarify the relationship between the motion characteristics behind the human motion in complicated motions like dancing and the subjective impressions of the observer. It examines the impression structures related to the motion of a determined body part of dancing and considers the motion characteristics giving a specific impression. To compare and consider the impression structures related to the motion of a body part, the authors made a principal component analysis, one of the multi-variable analytic methods, to check the arm and leg motions for any differences in the impression structure. Similarly, they considered any differences in the impression structures due to the experience knowledge of dance. Next, to consider any differences in the physical features that have effect on the impressions, they quantified the motion characteristics and used a heavy regression analysis to estimate the common motion characteristics that give the same impressions. In addition, they used the characteristics of the legs that are parts of the motion presumed to have the relationship with the impressions to reproduce the motion with CG for the consideration of these impressions. As a result, when the impressions of the arm and leg motions were compared, four impression evaluation axes of "like-dislike," "dynamic-static," "individual-monotonous," and "collected-wide" were extracted as the axes that evaluated the same impressions, but the impressions of "hard-soft" and "heavy-light" were extracted only from those of each arm or leg motion. When the evaluation axes of the impressions were compared between groups with differences in the knowledge of dance, five similar evaluation axes were extracted for each of them and there was no big difference in the impression structures themselves, but significant differences were found for the evaluation of impressions between the words used for the sensitivity evaluation in difference in knowledge. Attention was paid to the characteristics of the motion generating each impression to show the relationship between motion characteristics and subjective impressions.

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.

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