Computer-assisted self-training system for sports exercise using kinects

Chen, Hua-Tsung; He, Yu-Zhen; Chou, Chien-Li; Lee, Suh-Yin; Lin, Bor‐Shyh; Yu, Jen-Yu

doi:10.1109/icmew.2013.6618307

Cited by 40 publications

(25 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Additionally, they haven’t applied the yoga identification system to evaluating and guiding the yoga postures. YogaST [22], adaboost algorithm [28] and OpenPose [29] were also widely used in yoga posture recognition, but they similarly only modeled a few yoga posture without body folding and the recognition accuracy was lower than our proposed method. In addition, they also haven’t applied the yoga recognition system to evaluating and guiding the yoga postures.…”

Section: Resultsmentioning

confidence: 99%

“…Luo et al [10] configured 16 IMUs on human body to measure 15 joint angles. Chen et al [22] separately placed two Kinetics in front and to the side of the trainer, which were used to acquire the binarization of posture images and extract human body contours. Postures were recognized by calculating the similarity between the trainer’s posture and the standard posture model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Yoga Posture Recognition and Quantitative Evaluation with Wearable Sensors Based on Two-Stage Classifier and Prior Bayesian Network

Zhang

Chen

et al. 2019

Sensors

View full text Add to dashboard Cite

Currently, with the satisfaction of people’s material life, sports, like yoga and tai chi, have become essential activities in people’s daily life. For most yoga amateurs, they could only learn yoga by self-study, like mechanically imitating from yoga video. They could not know whether they performed standardly without feedback and guidance. In this paper, we proposed a full-body posture modeling and quantitative evaluation method to recognize and evaluate yoga postures to provide guidance to the learner. Back propagation artificial neural network (BP-ANN) was adopted as the first classifier to divide yoga postures into different categories, and fuzzy C-means (FCM) was utilized as the second classifier to classify the postures in a category. The posture data on each body part was regarded as a multidimensional Gaussian variable to build a Bayesian network. The conditional probability of the Gaussian variable corresponding to each body part relative to the Gaussian variable corresponding to the connected body part was used as criterion to quantitatively evaluate the standard degree of body parts. The angular differences between nonstandard parts and the standard model could be calculated to provide guidance with an easily-accepted language, such as “lift up your left arm”, “straighten your right forearm”. To evaluate our method, a wearable device with 11 inertial measurement units (IMUs) fixed onto the body was designed to measure yoga posture data with quaternion format, and the posture database with a total of 211,643 data frames and 1831 posture instances was collected from 11 subjects. Both the posture recognition test and evaluation test were conducted. In the recognition test, 30% data was randomly picked from the database to train BP-ANN and FCM classifiers, and the recognition accuracy of the remaining 70% data was 95.39%, which is highly competitive with previous posture recognition approaches. In the evaluation test, 30% data were picked randomly from subject three, subject four, and subject six, to train the Bayesian network. The probabilities of nonstandard parts were almost all smaller than 0.3, while the probabilities of standard parts were almost all greater than 0.5, and thus the nonstandard parts of body posture could be effectively separated and picked for guidance. We also tested separately the trainers’ yoga posture performance in the condition of without and with guidance provided by our proposed method. The results showed that with guidance, the joint angle errors significantly decreased.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Yoga Posture Recognition and Quantitative Evaluation with Wearable Sensors Based on Two-Stage Classifier and Prior Bayesian Network

Zhang

Chen

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Hua-Tsung Chen et al [4], used Contour and Skeleton Computation to capture binary maps of the body. Then used Feature Axis Extraction algorithm to extract certain points on the body to measure the correctness of the three Yoga poses.…”

Section: A Coachingmentioning

confidence: 99%

Smart Coaching: Enhancing Weightlifting and Preventing Injuries

Yasser¹,

Tariq²,

Samy³

et al. 2019

IJACSA

View full text Add to dashboard Cite

Getting injured is one of the most devastating and dangerous challenges that an athlete can go through and if it is a big injury it could end his/her athletic career. In this paper, we propose a system to automate the idea of coaching an athlete, by using an IR Camera (Microsoft Kinect Xbox 360) to detect the misplaced joints of the athlete while doing the lift, and alerting the athlete before an injury can occur. We are now able to detect if the lift was correct or wrong and to detect what kind of mistake has been done in the lift by the athlete by using the Fast Dynamic time warping (FastDTW) method. The FastDTW method has outperformed other classification methods and can achieve recognition with 100% accuracy for dependent user movements.

show abstract

“…In the past few years, computer vision has been applied to the 4G/LTE network testbed at NCTU; many applications were introduced [14][24] [25]. For example, in research [24], we proposed a platform to recognize tactic patterns in broadcast basketball videos.…”

Section: Computer Vision For V2x Based On Deep Learningmentioning

confidence: 99%

“…This system used Kalman filter to automatically detects the court lines, tracks the players and ball, captures and analyzes ball trajectory, calibrates the players' positions to the real-world, etc. In research [25], we developed a preliminary system called YogaST, which utilized C++ with OpenNI 1.5.4.0 and OpenCV to assist the Yoga practitioner in self-training. It aims at instructing him/her to adjust his/her posture correctly and prevents injury caused by improper postures.…”

Section: Computer Vision For V2x Based On Deep Learningmentioning

confidence: 99%

Big Data and Machine Learning Driven Open5GMEC for Vehicular Communications

Lin

et al. 2018

TNC

View full text Add to dashboard Cite

Mobile Edge Computing (MEC) is an emerging technology and an essential component of 5G networks to bring cloud services closer to users. That means data collection, storage, processing, computing, communication, and network control are implemented at network edges. MEC is expected to be able to satisfy a variety of delay-sensitive services and applications. On the other hand, the development of vehicles to everything (V2X) communication brings many requirements to future networks to guarantee full intelligence, automatic, and faster computation, management, and optimization to fulfill network QoS (quality of service) and QoE (quality of experience). To deal with those requirements, recently, softwaredefined networking (SDN), network functions virtualization (NFV), big data, and machine learning (ML) have been proposed as emerging technologies and the necessary tools for MEC and vehicular networks. This study aims to integrate those technologies to build a comprehensive architecture and an experimental framework for future 5G MEC called Open5GMEC. Moreover, the authors analyzed challenges and proposed relevant solutions for future vehicular communications in 5G networks. Finally, based on this framework, we successfully implemented several powerful ML-based applications for V2X such as object detection, network slicing, and migration services, which are executed at Broadband Mobile Lab (BML), National Chiao Tung University (NCTU).

show abstract

Computer-assisted self-training system for sports exercise using kinects

Cited by 40 publications

References 7 publications

Yoga Posture Recognition and Quantitative Evaluation with Wearable Sensors Based on Two-Stage Classifier and Prior Bayesian Network

Yoga Posture Recognition and Quantitative Evaluation with Wearable Sensors Based on Two-Stage Classifier and Prior Bayesian Network

Smart Coaching: Enhancing Weightlifting and Preventing Injuries

Big Data and Machine Learning Driven Open5GMEC for Vehicular Communications

Contact Info

Product

Resources

About