Evaluation of a Framework for Visual-Feedback Training Based on a Modified Self-Organizing Map Using Sensing Information Including Muscle Activity

In this research, we propose a visual-feedback system and evaluate it based on motion-sensing and computational technologies. This system will help amateur athletes imitate the motor skills of professionals. Using a self-organizing map (SOM) to visualize high-dimensional time-series motion data, we recorded the cyclic motion information, including the muscle activities, of a male subject as he pedaled a bicycle ergometer. To clarify the difference between the subject’s motor skill and the target motor skill in a cyclic movement, we used the modified SOM algorithm; a visual-feedback system was developed, which displayed the target motion as a circular trajectory on a two-dimensional motor skills map. The subject trained by observing only the displayed static target trajectory; the subject’s real-time trajectory was constructed from the subject’s real-time motion. We validated our proposed framework for the visual-feedback system by evaluating the motion performance of a subject using feedback training.

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Framework for visual-feedback training based on a modified self-organizing map to imitate complex motion

Proceedings of the Institution of Mechanical Engineers Part P Journal of Sports Engineering and Technology ◽

10.1177/1754337119872405 ◽

2019 ◽

Vol 234 (1) ◽

pp. 49-58 ◽

Cited By ~ 2

Author(s):

Hiroki Yokota ◽

Munekazu Naito ◽

Naoki Mizuno ◽

Shigemichi Ohshima

Keyword(s):

Motor Skills ◽

Visual Feedback ◽

Human Movement ◽

Feedback System ◽

Bicycle Ergometer ◽

Self Organizing Map ◽

Map Algorithm ◽

Time Motion ◽

The Difference ◽

Self Organizing

The goal of this research was to develop a visual-feedback system, based on motion sensing and computational technologies, to help athletes and patients imitate desired motor skills. To accomplish this objective, the authors used a self-organizing map to visualize high-dimensional, time-series motion data. The cyclic motion of one expert and five non-experts was captured as they pedaled a bicycle ergometer. A self-organizing map algorithm was used to display the corresponding circular motion trajectories on a two-dimensional motor skills map. The non-experts modified their motion to make their real-time motion trajectory approach that of the expert, thereby training themselves to imitate the expert motion. The root mean square error, which represents the difference between the non-expert motion and the expert motion, was significantly reduced upon using the proposed visual-feedback system. This indicates that the non-expert subjects successfully approximated the expert motion by repeated comparison of their trajectories on the motor skills map with that of the expert. The results demonstrate that the self-organizing map algorithm provides a unique way to visualize human movement and greatly facilitates the task of imitating a desired motion. By capturing the appropriate movements for display in the visual-feedback system, the proposed framework may be adopted for sports training or clinical practice.

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Real-time multiple plane area detection using a self-organizing map

Optical Engineering ◽

10.1117/1.oe.51.1.017207 ◽

2012 ◽

Vol 51 (1) ◽

pp. 017207 ◽

Cited By ~ 1

Author(s):

Jeong-Hyun Kim

Keyword(s):

Real Time ◽

Self Organizing Map ◽

Multiple Plane ◽

Plane Area ◽

Self Organizing

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Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps

Applied Sciences ◽

10.3390/app11041933 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1933

Author(s):

Hiroomi Hikawa ◽

Yuta Ichikawa ◽

Hidetaka Ito ◽

Yutaka Maeda

Keyword(s):

Real Time ◽

Gesture Recognition ◽

Weight Vector ◽

Recognition System ◽

Self Organizing Map ◽

Self Organizing Maps ◽

Time Dynamic ◽

System Input ◽

Self Organizing ◽

Gesture Spotting

In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. Then, gesture identification and gesture spotting are carried out in the self-organizing map (SOM)-Hebb classifier. The gesture spotting function detects the end of the gesture by using the vector distance between the posture sequence vector and the winner neuron’s weight vector. The proposed gesture recognition method was tested by simulation and real-time gesture recognition experiment. Results revealed that the system could recognize nine types of gesture with an accuracy of 96.6%, and it successfully outputted the recognition result at the end of gesture using the spotting result.

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Bird’s-Eye View Provided in Real-time via Drone Improves Spatial Perception for Team Sports

10.21203/rs.3.rs-148092/v1 ◽

2021 ◽

Author(s):

Satoshi Miura ◽

Kento Nakagawa ◽

Kazumasa Hirooka ◽

Yuya Matsumoto ◽

Yumi Umesawa ◽

...

Keyword(s):

Real Time ◽

Visual Feedback ◽

Spatial Perception ◽

Team Sports ◽

Space Perception ◽

Feedback System ◽

Smart Glasses ◽

Perceptual Abilities ◽

Individual Sports ◽

Error Distance

Abstract Sports-assisting technologies have been developed; however, most are to improve performances in individual sports such as ski, batting, and swimming. Few studies focused on team sports which require not only motor ability of individual players but also perceptual abilities to grasp positions of their own and others. In the present study, we aim to validate the feasibility of a visual feedback system for the improvement of space perception in relation to other persons that is necessary. Herein, the visual feedback system is composed of a flying drone that transmits the image to the participant’s smart glasses. With and without the system, the participant was able to see his/her own relative position in real time though the glass. Nine participants tried to position themselves on the line between two experimenters 30 m away from each other, which simulated the situation of a baseball cutoff man. As a result, the error distance between the participants’ position and the line significantly decreased when using the system than that without the system. Furthermore, after participants practiced the task with the system the error decreased compared to that before the practice. In conclusion, the real-time feedback system from the bird’s-eye view would work for improving the accuracy of space perception.

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Application of Real-Time Visual Feedback System in Balance Training of the Center of Pressure with Smart Wearable Devices

10.21203/rs.3.rs-129265/v1 ◽

2020 ◽

Author(s):

I-Lin Wang ◽

Li-I Wang ◽

Yang Liu ◽

Shi-Jie Xue ◽

Rui Hu ◽

...

Keyword(s):

Real Time ◽

Visual Feedback ◽

Postural Sway ◽

Center Of Pressure ◽

Balance Training ◽

Training Group ◽

Wearable Devices ◽

Control Group ◽

Feedback Training ◽

Smart Wearable

Abstract Background: Visual feedback from the center of pressure (COP) on the benefits of standing quietly remains controversial. The study was to investigate the adaptive effect of COP real-time visual feedback training provided by smart wearable devices on standing in silence. Methods: Thirty healthy female college students were randomly divided into three groups (visual feedback balance training group (VFT), non-visual feedback balance training group (NVFT) and control group (CG)) .Two force plates were used to calculate the coordinates of COP anteroposterior (COPAP) and COP mediolateral (COPML).The motion analysis system is used to calculate the coordinates of the center of mass in two directions. Enhanced visual feedback on the screen in the form of fluctuating in different directions, VFT received real-time visual feedback from Podoon APP for training, the NVFT only performs open eye balance without receiving real-time visual feedback. The CG group did not receive any visual feedback. The training lasted 4 weeks, the training lasts 30 minutes at an interval of 1 days. Results: After four weeks of balance training, the results showed that visual feedback training can improve the stability of human posture control by one leg stance and tandem stance static balance training on VFT intelligent App. The parameters of COPML/AP max displacement, COPML/AP velocity and COP radius and COP area in the VFT were significantly increased (p＜0.05).Conclusion: The conclusion shows that COP real-time visual feedback training provided by smart wearable devices can reduce postural sway better and improve body balance ability than general training when standing quietly.

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Opti-speech: a real-time, 3d visual feedback system for speech training

10.21437/interspeech.2014-298 ◽

2014 ◽

Author(s):

William Katz ◽

Thomas F. Campbell ◽

Jun Wang ◽

Eric Farrar ◽

J. Coleman Eubanks ◽

...

Keyword(s):

Real Time ◽

Visual Feedback ◽

Feedback System ◽

Speech Training

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Manual ventilation quality is improved with a real-time visual feedback system during simulated resuscitation

International Journal of Emergency Medicine ◽

10.1186/s12245-020-00276-y ◽

2020 ◽

Vol 13 (1) ◽

Cited By ~ 1

Author(s):

Jeffrey R. Gould ◽

Lisa Campana ◽

Danielle Rabickow ◽

Richard Raymond ◽

Robert Partridge

Keyword(s):

Real Time ◽

Visual Feedback ◽

Feedback System ◽

Manual Ventilation

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Real time system for human action evaluation

10.32920/ryerson.14660982.v1 ◽

2021 ◽

Author(s):

Randy Tan

Keyword(s):

Real Time ◽

Visual Feedback ◽

Ground Truth ◽

Feedback System ◽

Human Action ◽

Complete Sequence ◽

Real Time System ◽

Analysis System ◽

Value Decomposition ◽

The Impact

This thesis presents a real-time human activity analysis system, where a user’s activity can be quantitatively evaluated with respect to a ground truth recording. Multiple Kinects are used to solve the problem of self-occlusion while performing an activity. The Kinects are placed in locations with different perspectives to extract the optimal joint positions of a user using Singular Value Decomposition (SVD) and Sequential Quadratic Programming (SQP). The extracted joint positions are then fed through our Incremental Dynamic Time Warping (IDTW) algorithm so that an incomplete sequence of an user can be optimally compared against the complete sequence from an expert (ground truth). Furthermore, the user’s performance is communicated through a novel visual feedback system, where colors on the skeleton present the user’s level of performance. Experimental results demonstrate the impact of our system, where through elaborate user testing we show that our IDTW algorithm combined with visual feedback improves the user’s performance quantitatively.

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