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Taekwondo has evolved from a traditional martial art into an official Olympic sport. This study introduces a novel action recognition model tailored for Taekwondo unit actions, utilizing joint-motion data acquired via wearable inertial measurement unit (IMU) sensors. The utilization of IMU sensor-measured motion data facilitates the capture of the intricate and rapid movements characteristic of Taekwondo techniques. The model, underpinned by a conventional convolutional neural network (CNN)-based image classification framework, synthesizes action images to represent individual Taekwondo unit actions. These action images are generated by mapping joint-motion profiles onto the RGB color space, thus encapsulating the motion dynamics of a single unit action within a solitary image. To further refine the representation of rapid movements within these images, a time-warping technique was applied, adjusting motion profiles in relation to the velocity of the action. The effectiveness of the proposed model was assessed using a dataset compiled from 40 Taekwondo experts, yielding remarkable outcomes: an accuracy of 0.998, a precision of 0.983, a recall of 0.982, and an F1 score of 0.982. These results underscore this time-warping technique’s contribution to enhancing feature representation, as well as the proposed method’s scalability and effectiveness in recognizing Taekwondo unit actions.

Heart rate variability (HRV) is an effective tool for objectively evaluating physiological stress indices in psychological states. This study aimed to develop multiple linear regression equations to predict HRV variables using physical characteristics, body composition, and heart rate (HR) variables (eg, sex, age, height, weight, body mass index, fat-free mass, percent body fat, resting HR, maximal HR, and HR reserve) in Korean adults. Six hundred eighty adults (male, n = 236, female, n = 444) participated in this study. HRV variable estimation multiple linear regression equations were developed using a stepwise technique. The regression equation’s coefficient of determination for time-domain variables was significantly high (SDNN = adjusted R 2 : 73.6%, P

Issues of fairness and consistency in Taekwondo poomsae evaluation have often occurred due to the lack of an objective evaluation method. This study proposes a three-dimensional (3D) convolutional neural network–based action recognition model for an objective evaluation of Taekwondo poomsae. The model exhibits robust recognition performance regardless of variations in the viewpoints by reducing the discrepancy between the training and test images. It uses 3D skeletons of poomsae unit actions collected using a full-body motion-capture suit to generate synthesized two-dimensional (2D) skeletons from desired viewpoints. The 2D skeletons obtained from diverse viewpoints form the training dataset, on which the model is trained to ensure consistent recognition performance regardless of the viewpoint. The performance of the model was evaluated against various test datasets, including projected 2D skeletons and RGB images captured from diverse viewpoints. Comparison of the performance of the proposed model with those of previously reported action recognition models demonstrated the superiority of the proposed model, underscoring its effectiveness in recognizing and classifying Taekwondo poomsae actions.

Defect inspection is important to ensure consistent quality and efficiency in industrial manufacturing. Recently, machine vision systems integrating artificial intelligence (AI)-based inspection algorithms have exhibited promising performance in various applications, but practically, they often suffer from data imbalance. This paper proposes a defect inspection method using a one-class classification (OCC) model to deal with imbalanced datasets. A two-stream network architecture consisting of global and local feature extractor networks is presented, which can alleviate the representation collapse problem of OCC. By combining an object-oriented invariant feature vector with a training-data-oriented local feature vector, the proposed two-stream network model prevents the decision boundary from collapsing to the training dataset and obtains an appropriate decision boundary. The performance of the proposed model is demonstrated in the practical application of automotive-airbag bracket-welding defect inspection. The effects of the classification layer and two-stream network architecture on the overall inspection accuracy were clarified by using image samples collected in a controlled laboratory environment and from a production site. The results are compared with those of a previous classification model, demonstrating that the proposed model can improve the accuracy, precision, and F1 score by up to 8.19%, 10.74%, and 4.02%, respectively.

The main advantage of measuring functional fitness (FF) in older adults is that individual tests can estimate and track the rate of decline with age. This study aimed to develop a multiple linear regression model for predicting FF variables using easy-to-measure independent variables (e.g., sex, age, body mass index, and percent body fat) in Korean older adults. National Fitness Award datasets from the Republic of Korea were used in this analysis. The participants were aged ≥65 years and included 61,465 older men and 117,395 older women. The FF variables included the hand grip strength, lower body strength (30-s chair stand), lower body flexibility (chair sit-and-reach), coordination (figure of 8 walk), agility/dynamic balance (timed up-and-go), and aerobic endurance (2-min step test). An estimation multiple linear regression model was developed using a stepwise technique. In the regression model, the coefficient of determination in the hand grip strength test (adjusted R2 = 0.773, p < 0.001) was significantly high. However, the coefficient of determination in the 30-s chair stand (adjusted R2 = 0.296, p < 0.001), chair sit-and-reach (adjusted R2 = 0.435, p < 0.001), figure of 8 walk (adjusted R2 = 0.390, p < 0.001), timed up-and-go (adjusted R2 = 0.384, p < 0.001), and 2-min step tests (adjusted R2 = 0.196, p < 0.001) was significantly low to moderate. Our findings suggest that easy-to-measure independent variables can predict the hand grip strength in older adults. In future studies, explanatory power will be further improved if multiple linear regression analysis, including the physical activity level and nutritional status of older adults, is performed to predict the FF variables.

Cricothyrotomy procedures, involving risky incisions on the neck skin and internal membranes, require rigorous training. In this paper, a novel incision sensor measuring the incision path in a cricothyrotomy training simulation is proposed. The sensor provides quantitative feedback to trainees on their incision practice, enhancing the effectiveness of the simulation. The sensor measures the electric potential, which decreases monotonically along the direction of current flow on the conductive material at the incision point, and converts it to coordinate values, based on the relationship between the electric potential and the position. The sensor comprises three layers of conductive tape, which are electrically isolated by two dielectric layers, and is fabricated as a thin film. The first two conductive layers (driving layers) are alternately energized to create distributions of electric potential in the x or y directions across the sensor plane. The third conductive layer (sensing layer), placed under the driving layers, transfers the electric potential to the output channel of the sensor at the point where a metal blade creates a short circuit between the energized driving layer and the sensing layer. The alternating measurements are converted to x and y coordinates of the incision position. The experiments for characterization and performance validation were performed using sensor prototypes fabricated with the proposed design and fabrication procedures. The experimental results show that the proposed sensor facilitates the measurement of the incision paths aligned with and diagonal to the x andy axes within root mean square errors of 0.98 and 1.03 mm, respectively.

Continuous health care and the measurement of health-related physical fitness (HRPF) is necessary for prevention against chronic diseases; however, HRPF measurements including laboratory methods may not be practical for large populations owing to constraints such as time, cost, and the requirement for qualified technicians. This study aimed to develop a multiple linear regression model to estimate the HRPF of Korean adults, using easy-to-measure dependent variables, such as gender, age, body mass index, and percent body fat. The National Fitness Award datasets of South Korea were used in this analysis. The participants were aged 19–64 years, including 319,643 male and 147,600 females. HRPF included hand grip strength (HGS), flexibility (sit and reach), muscular endurance (sit-ups), and cardiorespiratory fitness (estimated VO2max). An estimation multiple linear regression model was developed using the stepwise technique. The outlier data in the multiple regression model was identified and removed when the absolute value of the studentized residual was ≥2. In the regression model, the coefficient of determination for HGS (adjusted R2: 0.870, P < 0.001), muscular endurance (adjusted R2: 0.751, P < 0.001), and cardiorespiratory fitness (adjusted R2: 0.885, P < 0.001) were significantly high. However, the coefficient of determination for flexibility was low (adjusted R2: 0.298, P < 0.001). Our findings suggest that easy-to-measure dependent variables can predict HGS, muscular endurance, and cardiorespiratory fitness in adults. The prediction equation will allow coaches, athletes, healthcare professionals, researchers, and the general public to better estimate the expected HRPF.

Objective To evaluate a novel multi-channel functional electrical stimulation (FES) rehabilitation method based on the evaluation of patient-specific walking dysfunction. Methods This study investigated a novel multi-channel FES-based rehabilitation method that analysed the patient’s muscle synergy and walking posture. A patient-specific FES profile was produced in the pre-evaluation stage by comparing the muscle synergy and walking posture of the patient with those of healthy control subjects. During the rehabilitation phase, this profile was used to determine an appropriate FES pulse width and amplitude for stimulating the patient’s muscles as they walked across a flat surface. Results Two stroke patients with hemiplegic symptoms participated in a clinical evaluation of the proposed method involving a 4-week course of rehabilitation. An evaluation of the rehabilitation results based on a comparison of the pre- and post-rehabilitation muscle synergy and walking posture revealed that the rehabilitation enhanced the muscle synergy similarity between the patients and healthy control subjects and their quantitative walking performance, as measured by a 10-m walk test and walking speed, by up to 23.38% and 30.00%, respectively. Conclusion These results indicated that the proposed rehabilitation method improved walking ability by improving muscle coordination and adequately supporting weakened muscles in stroke patients.

Target-following mobile robots have gained attention in various industrial applications. This study proposes an ultra-wideband-based target localization method that provides highly accurate and robust target tracking performance for a following robot. Based on the least square approximation framework, the proposed method improves localization accuracy by compensating localization bias and high-frequency deviations component by component. Initial calibration method is proposed to measure the device-dependent localization bias, which enables a compensation of the bias error not only at the calibration points, but also at the any other points. An iterative complementary filter, which recursively produces optimal estimation for each timeframe as a weighted sum of previous and current estimation depending on the reliability of each estimation, is proposed to reduce the deviation of the localization error. The performance of the proposed method is validated using simulations and experiments. Both the magnitude and deviation of the localization error were significantly improved by up to 77 and 51%, respectively, compared with the previous method.

A haptic interface based on electrical muscle stimulation (EMS) has huge potential in terms of usability and applicability compared with conventional haptic interfaces. This study analyzed the force response characteristics of forearm extensor muscles for EMS-based haptic rendering. We introduced a simplified mathematical model of the force response, which has been developed in the field of rehabilitation, and experimentally validated its feasibility for haptic applications. Two important features of the force response, namely the peak force and response time, with respect to the frequency and amplitude of the electrical stimulation were identified by investigating the experimental force response of the forearm extensor muscles. An exponential function was proposed to estimate the peak force with respect to the frequency and amplitude, and it was verified by comparing with the measured peak force. The response time characteristics were also examined with respect to the frequency and amplitude. A frequency-dependent tendency, i.e., an increase in response time with increasing frequency, was observed, whereas there was no correlation with the amplitude. The analysis of the force response characteristics with the application of the proposed force response model may help enhance the fidelity of EMS-based haptic rendering.

In taekwondo, poomsae (i.e., form) competitions have no quantitative scoring standards, unlike gyeorugi (i.e., full-contact sparring) in the Olympics. Consequently, there are diverse fairness issues regarding poomsae evaluation, and the demand for quantitative evaluation tools is increasing. Action recognition is a promising approach, but the extreme and rapid actions of taekwondo complicate its application. This study established the Taekwondo Unit technique Human Action Dataset (TUHAD), which consists of multimodal image sequences of poomsae actions. TUHAD contains 1936 action samples of eight unit techniques performed by 10 experts and captured by two camera views. A key frame-based convolutional neural network architecture was developed for taekwondo action recognition, and its accuracy was validated for various input configurations. A correlation analysis of the input configuration and accuracy demonstrated that the proposed model achieved a recognition accuracy of up to 95.833% (lowest accuracy of 74.49%). This study contributes to the research and development of taekwondo action recognition.

This paper offers a review of the latest robotic remote control schemes that are based on human motion through the virtual collaboration system. The robotic remote control schemes have been studied and developed to combine the intelligence of humans with the precision and mechanical strength of robots. They have performed an important role in some dangerous environments by replacing humans in many difficult jobs, but the lack of intuitive control interfaces causes decreased efficiency. To address this problem, various approaches have been recently investigated, including human motion-based control, visual feedback systems, tactile or force feedback, and other various haptic systems. This paper describes the tendencies of these efforts and discusses possibilities and limitations in three parts: remote control based on human motion, multimodal feedback schemes for realistic interaction, and virtual collaboration systems.

The vehicular ad hoc network (VANET) has been identified as one of the most promising technologies for managing future intelligent transportation systems. This paper proposes a distributed transmission power adjustment algorithm for communication congestion control and awareness enhancement to address communication congestion problems that can arise in VANETs. The objective of the proposed algorithm is to provide maximum awareness of surrounding vehicles' status while maintaining a communications channel load below the allowed threshold. The proposed algorithm accomplishes this by adjusting the transmission range of each vehicle in the network progressively and gradually, while monitoring the communications channel load of each vehicle. By changing the transmission range of a vehicle little by little according to the communications channel load of its neighboring vehicles, the algorithm finds the optimal transmission range that provides maximum awareness without resulting in communications congestion. In addition, the proposed algorithm appropriately controls the channel load in a fair manner without sacrificing awareness of specific vehicles in the congested situation. This allows nearby vehicles to obtain more peripheral information to help them stay away from potential hazards and maintain safety. The proposed algorithm was implemented in a simulation environment, and its performance was validated in various traffic scenarios. The simulation results show that the proposed algorithm can deal with communication congestion by controlling the transmission power fairly to a target threshold in various traffic situations.