Personal Training System Research Articles

MultiSenseBadminton: Wearable Sensor–Based Biomechanical Dataset for Evaluation of Badminton Performance

The sports industry is witnessing an increasing trend of utilizing multiple synchronized sensors for player data collection, enabling personalized training systems with multi-perspective real-time feedback. Badminton could benefit from these various sensors, but there is a scarcity of comprehensive badminton action datasets for analysis and training feedback. Addressing this gap, this paper introduces a multi-sensor badminton dataset for forehand clear and backhand drive strokes, based on interviews with coaches for optimal usability. The dataset covers various skill levels, including beginners, intermediates, and experts, providing resources for understanding biomechanics across skill levels. It encompasses 7,763 badminton swing data from 25 players, featuring sensor data on eye tracking, body tracking, muscle signals, and foot pressure. The dataset also includes video recordings, detailed annotations on stroke type, skill level, sound, ball landing, and hitting location, as well as survey and interview data. We validated our dataset by applying a proof-of-concept machine learning model to all annotation data, demonstrating its comprehensive applicability in advanced badminton training and research.

PARSAT: Fuzzy logic for adaptive spatial ability training in an augmented reality system

Personalized training systems and augmented reality are two of the most promising educational technologies since they could enhance engineering students? spatial ability. Prior research has examined the benefits of the integration of augmented reality in increasing students? motivation and enhancing their spatial skills. However, based on the review of the literature, current training systems do not provide adaptivity to students? individual needs. In view of the above, this paper presents a novel adaptive augmented reality training system, which teaches the knowledge domain of technical drawing. The novelty of the proposed system is that it proposes using fuzzy sets to represent the students? knowledge levels more accurately in the adaptive augmented reality training system. The system determines the amount and the level of difficulty of the learning activities delivered to the students, based on their progress. The main contribution of the system is that it is student-centered, providing the students with an adaptive training experience. The evaluation of the system took place during the 2021-22 and 2022-23 winter semesters, and the results are very promising.

Technical Diagnosis on Elite Female Discus Athletes Based on Grey Relational Analysis.

The personalized training of elite athletes is the key to the breakthrough of Chinese track and field events in the Tokyo Olympic Games. The brilliance of Chinese women's throwing benefits from the closed-loop personalized training system. Training starts from the construction of accurate personalized technology and physical fitness model. This paper introduces the concept of champion model and puts forward a targeted technical training system based on the differentiation research of champion model. This paper mainly studies and analyzes some important technical parameters and achievements of elite female discus athletes aged 2018∼2021 by using the methods of Pearson, partial correlation and grey correlation analysis. We select from several technical parameters with significant differences, choose from several technical parameters that have significant difference, and calculate the correlation parameters and results. The results show that the influence degree of these technical parameters is as follows: torso angle of right foot touching the ground, discus release angle, discus release speed, shoulder and arm passing angle of left foot off the ground, discus moving distance of double support, center of mass moving distance of double support, and time of the second single support stage. This is different from our view that the hand speed is the most important, so the training of elite athletes should be more refined and specialized to promote the improvement of their performance. Through the application of technical diagnosis model in Chen Yang in a period of time, Chen Yang got her best result (65.14 m) in Chongqing Athletics Championship, which verified the success of the technical diagnosis model and application in this study.

A novel remote hands-on training for neuroendovascular-based treatment in the era of the COVID-19 pandemic.

The COVID-19 pandemic has demanded a change in learning modalities, which led us to develop a remote personal training system for clinicians performing neuroendovascular procedures. A portable vascular model designed for practicing catheter navigation guidance, thrombus retrieval, and intracranial aneurysm coil embolisation was established. We created an environment that enabled interactive dialogue and communication across long distances using the Internet. The instructor conducted approximately 2 h of hands-on training with two to four trainees at a time. Despite the restrictions enforced by the Government of Japan due to the COVID-19 pandemic, 17 online hands-on training were successfully conducted throughout Japan over 1 year for a total of 48 trainees. The developed remote training programme, to avoid the aggregation of a large number of trainees at a developed time, proved to be at par with the conventional learning system. The training was well-received since the operation time was longer and the question and answer sessions were more fulfilling compared to the conventional format in which a group of trainees had got a brief opportunity to receive actual hands-on experience.

User experience design for a smart-mirror-based personalized training system

This paper describes the user experience (UX) design for a smart-mirror-based personalized training system which aims to help people live a healthy life. A number of researchers and companies have developed fitness systems that use a virtual coach which shows the user with actions they should perform. However such systems can be difficult to accurately follow the virtual guide’s motions and there are also limitations in the feedback provided to inform users of their correct body posture. This is because most systems are designed for users to simply watch and follow a character’s motions (poses) from a third person perspective. In our smart mirror-based system, users are able to follow the exercise-postures of a virtual professional trainer shown in a first person viewpoint and receive coaching through a real-time motion correction. This is based on a predefined database of the trainer’s postures gained from motion-capture technology, and it is personalized to the user’s body 3D model acquired through an instant one-time scanning process. In this paper, we report on the UX design of our system, mainly focusing on understandable visualization, intuitive interaction, attractive information representation and easily acceptable user scenarios. Through a series of user studies, we analyze and discuss user friendliness, information comprehension, and user satisfaction as they relate to our design. In addition, we also assess the similarity and effectiveness of the proposed system compared to traditional personalized training (PT) at a gym. Based on the implications, we discuss future research directions for improving the user experience of the smart-mirror-based PT system.

Content adaptation neural network method cause-specific the state of users

The aim of the study is to develop recommendations for adapting educational content based on the use of a neural network method for recognizing the human autonomic nervous system degree of activity. The initial data for decision making are the vectors of cardiointervals obtained with the help of the pulse sensor. The state of the autonomic nervous system is monitored by a two-layer artificial neural network of direct propagation. The artificial neural network was trained by combining gradient and stochastic training methods. A trial sample was used as a training set, consisting of 168 records of people’s cardiointervals belonging to different social and age groups. The presented method for recognizing the autonomic nervous system activity degree was tested via the shared programming support environment Lazarus. As a result of network training, a set of weighting coefficient values was found, for which 100% correct recognition is performed over the entire trial sample. A personalized training system that monitors the functional state of the student varies the presentation of educational content. When the functional state changes during the training load, the virtual agent organizes the training process so as to avoid stress load.

A neurophysiological approach to assess training outcome under stress: A virtual reality experiment of industrial shutdown maintenance using Functional Near-Infrared Spectroscopy (fNIRS)

Shutdown maintenance, i.e., turning off a facility for a short period for renewal or replacement operations is a highly stressful task. With the limited time and complex operation procedures, human stress is a leading risk. Especially shutdown maintenance workers often need to go through excessive and stressful on-site trainings to digest complex operation information in limited time. The challenge is that workers’ stress status and task performance are hard to predict, as most trainings are only assessed after the shutdown maintenance operation is finished. A proactive assessment or intervention is needed to evaluate workers’ stress status and task performance during the training to enable early warning and interventions. This study proposes a neurophysiological approach to assess workers’ stress status and task performance under different virtual training scenarios. A Virtual Reality (VR) system integrated with the eye-tracking function was developed to simulate the power plant shutdown maintenance operations of replacing a heat exchanger in both normal and stressful scenarios. Meanwhile, a portable neuroimaging device – Functional Near-Infrared Spectroscopy (fNIRS) was also utilized to collect user’s brain activities by measuring hemodynamic responses associated with neuron behavior. A human–subject experiment (n = 16) was conducted to evaluate participants’ neural activity patterns and physiological metrics (gaze movement) related to their stress status and final task performance. Each participant was required to review the operational instructions for a pipe maintenance task for a short period and then perform the task based on their memory in both normal and stressful scenarios. Our experiment results indicated that stressful training had a strong impact on participants’ neural connectivity patterns and final performance, suggesting the use of stressors during training to be an important and useful control factors. We further found significant correlations between gaze movement patterns in review phase and final task performance, and between the neural features and final task performance. In summary, we proposed a variety of supervised machine learning classification models that use the fNIRS data in the review session to estimate individual’s task performance. The classification models were validated with the k-fold (k = 10) cross-validation method. The Random Forest classification model achieved the best average classification accuracy (80.38%) in classifying participants’ task performance compared to other classification models. The contribution of our study is to help establish the knowledge and methodological basis for an early warning and estimating system of the final task performance based on the neurophysiological measures during the training for industrial operations. These findings are expected to provide more evidence about an early performance warning and prediction system based on a hybrid neurophysiological measure method, inspiring the design of a cognition-driven personalized training system for industrial workers.

Development of a Personalized Training System Using the Lung Image Database Consortium and Image Database Resource Initiative Database

The aim of this study was to develop a personalized training system using the Lung Image Database Consortium (LIDC) and Image Database resource Initiative (IDRI) Database, because collecting, annotating, and marking a large number of appropriate computed tomography (CT) scans, and providing the capability of dynamically selecting suitable training cases based on the performance levels of trainees and the characteristics of cases are critical for developing a efficient training system. A novel approach is proposed to develop a personalized radiology training system for the interpretation of lung nodules in CT scans using the Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which provides a Content-Boosted Collaborative Filtering (CBCF) algorithm for predicting the difficulty level of each case of each trainee when selecting suitable cases to meet individual needs, and a diagnostic simulation tool to enable trainees to analyze and diagnose lung nodules with the help of an image processing tool and a nodule retrieval tool. Preliminary evaluation of the system shows that developing a personalized training system for interpretation of lung nodules is needed and useful to enhance the professional skills of trainees. The approach of developing personalized training systems using the LIDC/IDRL database is a feasible solution to the challenges of constructing specific training program in terms of cost and training efficiency.

A Performance Weighted Collaborative Filtering algorithm for personalized radiology education

Devising an accurate prediction algorithm that can predict the difficulty level of cases for individuals and then selects suitable cases for them is essential to the development of a personalized training system. In this paper, we propose a novel approach, called Performance Weighted Collaborative Filtering (PWCF), to predict the difficulty level of each case for individuals. The main idea of PWCF is to assign an optimal weight to each rating used for predicting the difficulty level of a target case for a trainee, rather than using an equal weight for all ratings as in traditional collaborative filtering methods. The assigned weight is a function of the performance level of the trainee at which the rating was made. The PWCF method and the traditional method are compared using two datasets. The experimental data are then evaluated by means of the MAE metric. Our experimental results show that PWCF outperforms the traditional methods by 8.12% and 17.05%, respectively, over the two datasets, in terms of prediction precision. This suggests that PWCF is a viable method for the development of personalized training systems in radiology education.

A Content-Boosted Collaborative Filtering Algorithm for Personalized Training in Interpretation of Radiological Imaging

Devising a method that can select cases based on the performance levels of trainees and the characteristics of cases is essential for developing a personalized training program in radiology education. In this paper, we propose a novel hybrid prediction algorithm called content-boosted collaborative filtering (CBCF) to predict the difficulty level of each case for each trainee. The CBCF utilizes a content-based filtering (CBF) method to enhance existing trainee-case ratings data and then provides final predictions through a collaborative filtering (CF) algorithm. The CBCF algorithm incorporates the advantages of both CBF and CF, while not inheriting the disadvantages of either. The CBCF method is compared with the pure CBF and pure CF approaches using three datasets. The experimental data are then evaluated in terms of the MAE metric. Our experimental results show that the CBCF outperforms the pure CBF and CF methods by 13.33 and 12.17%, respectively, in terms of prediction precision. This also suggests that the CBCF can be used in the development of personalized training systems in radiology education.

조직의 임파워먼트가 직원들의 조직몰입, 서비스품질에 미치는 영향 - 치과병,의원 직원들을 중심으로 -

This study analyzed affecting factors of employees working at dental hospitals to the organizational commitment and service quality, and then tried to utilize it as a basic data in executing personnel training policies for dental hospital's practitioners. The survey was conducted from July 1st to July 30th, 2010, and used 193 copies for this research analysis. As a hypothetical verification result through regression analysis, means and capabilities among empowerment factors influenced significant positive (+) effects, and self-determination and influences did not make significant effects. Also, organization commitment was displayed as influencing significant positive (+) effects to the service quality. When considering this research's results as a basement, an alternative for the managerial innovation shall be established for promoting mutual developments of individuals and organization in midst of promptly changing economic environments. That is, a personal training system has to be set up in which more decision-making rights on given assignments of dental hospital's employments are endowed, and then responsibilities are directed to be charged so much by introducing the empowerment concept instead of the managerial technique centering on controls.

MOPET: A context-aware and user-adaptive wearable system for fitness training

Cardiovascular disease, obesity, and lack of physical fitness are increasingly common and negatively affect people's health, requiring medical assistance and decreasing people's wellness and productivity. In the last years, researchers as well as companies have been increasingly investigating wearable devices for fitness applications with the aim of improving user's health, in terms of cardiovascular benefits, loss of weight or muscle strength. Dedicated GPS devices, accelerometers, step counters and heart rate monitors are already commercially available, but they are usually very limited in terms of user interaction and artificial intelligence capabilities. This significantly limits the training and motivation support provided by current systems, making them poorly suited for untrained people who are more interested in fitness for health rather than competitive purposes. To better train and motivate users, we propose the mobile personal trainer (MOPET) system. MOPET is a wearable system that supervises a physical fitness activity based on alternating jogging and fitness exercises in outdoor environments. By exploiting real-time data coming from sensors, knowledge elicited from a sport physiologist and a professional trainer, and a user model that is built and periodically updated through a guided autotest, MOPET can provide motivation as well as safety and health advice, adapted to the user and the context. To better interact with the user, MOPET also displays a 3D embodied agent that speaks, suggests stretching or strengthening exercises according to user's current condition, and demonstrates how to correctly perform exercises with interactive 3D animations. By describing MOPET, we show how context-aware and user-adaptive techniques can be applied to the fitness domain. In particular, we describe how such techniques can be exploited to train, motivate, and supervise users in a wearable personal training system for outdoor fitness activity.