Search Results (31)

It is critical to provide life-sustaining treatment to OHCA patients before ambulance care arrives. However, incorrectly performed resuscitation maneuvers reduce the chances of survival and recovery for the victims. Therefore, we must train regularly and learn how to do it correctly. To facilitate regular chest compression training, this study aims to improve the accuracy of a chest compression evaluation system using posture estimation and to develop a web application. To analyze and enhance accuracy, the YOLOv8 posture estimation was used to examine compression depth, recoil, and tempo, and its accuracy was compared to that of the manikin, which has evaluation systems. We conducted comparative experiments with different camera angles and heights to optimize the accuracy of the evaluation. The experimental results showed that an angle of 30 degrees and a height of 50 cm produced superior accuracy. For web application development, a system has been designed to allow users to upload videos for analysis and obtain appropriate compression parameters. The usability evaluation of the application confirmed its ease of use and accessibility, and positive feedback was obtained. In the conclusion, these findings suggest that optimizing recording conditions significantly improves the accuracy of posture-based chest compression evaluation. Future work will focus on enhancing real-time feedback functionality and improving the user interface of the web application. Full article

This study introduces a novel approach to cyber defense exercises, emphasizing the emulation of technical tasks to create realistic incident response scenarios. Unlike traditional cyber ranges or tabletop exercises, this method enables both management and technical leaders to engage in decision-making processes without requiring a full technical setup. The initial observations indicate that exercises based on the emulation of technical tasks require less preparation time compared to conventional methods, addressing the growing demand for efficient training solutions. This study aims to assist organizations in developing their own cyber defense exercises by providing practical insights into the benefits and challenges of this approach. The key advantages observed include improved procedural compliance, inter-team communication, and a better understanding of the chain of command as participants navigate realistic, organization-wide scenarios. However, new challenges have also emerged, such as managing the simulation tempo and balancing technical complexity—particularly in offense–defense team configurations. This study proposes a structured and scalable approach as a practical alternative to the traditional training methods, aligning better with the evolving demands of modern cyber defense. Full article

Gamification has been extensively applied to aerobic and cardiovascular training, but its adoption in resistance training remains limited. Unlike traditional strength training, which often relies on intrinsic motivation and discipline, gamification introduces extrinsic incentives and real-time feedback that improves engagement and adherence, affecting overall training outcomes. In this work, we develop a gamified resistance training program using wearable sensors to explore the potential benefits of interactive and data-driven exercise experiences. By leveraging real-time feedback and performance tracking, our system provides participants feedback to properly control key training variables such as form and tempo, essential factors for muscle hypertrophy and strength development. To evaluate the effectiveness of our approach, we conducted a short-term comparative study in which participants were assigned to either a gamified training group or a conventional resistance training control group. Over a four-week period, we assessed volitional adherence to prescribed tempo and repetition schemes, along with strength adaptations in the biceps and triceps. Our findings indicate that gamified resistance training significantly enhances adherence to tempo and repetition targets while fostering better adaptation to the workout regime. Participants in the gamified group exhibited measurable improvements in upper body strength compared to the control group. These results suggest that gamification when integrated with wearable sensor technology, can be a powerful tool for optimising resistance training effectiveness and motivation. Full article

Background/Objectives: Patients with physical disabilities, like cerebral palsy, the most common movement disorder in childhood, can benefit from instrumental therapy using piano. Playing the piano promotes the interaction between different brain regions and integrates motor skills, sensory skills, musical hearing, and emotions. A pilot music study examined the effects of six months of piano lessons on hand motor skills and musical hearing in groups of children with motor disabilities. Methods: The allocation to the group was not randomized. Various tests, including the standardized Box and Block Test (BBT) and piano tests, assessed hand motor skills. Musical hearing was evaluated, and a questionnaire was used to determine the participants’ enjoyment and experience with the piano lessons. The regularity, tempo of keystrokes, and synchronization between the two hands were assessed and compared to evaluate the effects of six months of piano training. Results: After six months of piano training, statistically significant improvements were observed in the BBT, as well as in the regularity and tempo of the non-dominant hand. The children showed significant improvement in hand-motor control, moving 27.3% more cubes in the BBT. Regularity and tempo in piano playing, especially in the non-dominant hand, also improved. Moreover, 55% of the children better recognized the correct pitches of notes. Conclusions: Thus, this study supports the concept that piano lessons are an effective form of physical therapy for the development of hand motor skills and musical hearing. Full article

Jump rope is a widely applied basic training technique in various sports, yet it is understudied biomechanically. This study investigates the impact of cycle-tempo-induced motor control changes in elite jump rope athletes, addressing the biomechanical gap of cyclic skill control. The hypothesis posited two accelerations per jump cycle—one in front of and one behind the body—and anticipated that increased cycle frequency would alter the distribution of acceleration time within a cycle. Using 3D motion capture technology, 12 young elite jump rope athletes were analyzed at 100, 140, and 180 revolutions per minute (rpm). The kinematic parameters obtained confirmed the presence of two distinct accelerations per cycle. As tempo increased, the percentage of rear acceleration time increased from 9.58% at 100 rpm to 17.42% at 180 rpm, while front acceleration time decreased from 39.03% at 100 rpm to 31.40% at 180 rpm, along with peak velocities increasing from 12.94 m/s at 100 rpm to 22.74 m/s at 180 rpm significantly (p < 0.01). Rope trajectory analysis indicated a consistent movement pattern across tempos, primarily in the sagittal plane. Variations in skill control revealed shorter contact phases, decreasing from 61.53% at 100 rpm to 48.25% at 180 rpm, as well as a reduced vertical range of motion for the center of gravity (from 0.15 body height at 100 rpm to 0.06 body height at 180 rpm) and feet (from 0.05 body height at 100 rpm to 0.03 body height at 180 rpm) (p < 0.05). Significant reductions were also observed in the flexion/extension range of motion for the hip (from 22.31° at 100 rpm to 3.47° at 180 rpm), knee (from 49.31° at 100 rpm to 9.35° at 180 rpm), and ankle (from 52.99° at 100 rpm to 21.41° at 180 rpm) (p < 0.01). These findings enhance the understanding of motor control adaptations to different tempos and have practical implications for developing coaching programs aimed at optimizing performance, stability, and efficiency in jump rope training. Full article

The coal mining industry in Northern Shaanxi is robust, with a prevalent use of the local dialect, known as “Shapu”, characterized by a distinct Northern Shaanxi accent. This study addresses the practical need for speech recognition in this dialect. We propose an end-to-end speech recognition model for the North Shaanxi dialect, leveraging the Conformer architecture. To tailor the model to the coal mining context, we developed a specialized corpus reflecting the phonetic characteristics of the dialect and its usage in the industry. We investigated feature extraction techniques suitable for the North Shaanxi dialect, focusing on the unique pronunciation of initial consonants and vowels. A preprocessing module was designed to accommodate the dialect’s rapid speech tempo and polyphonic nature, enhancing recognition performance. To enhance the decoder’s text generation capability, we replaced the Conformer decoder with a Transformer architecture. Additionally, to mitigate the computational demands of the model, we incorporated Connectionist Temporal Classification (CTC) joint training for optimization. The experimental results on our self-established voice dataset for the Northern Shaanxi coal mining industry demonstrate that the proposed Conformer–Transformer–CTC model achieves a 9.2% and 10.3% reduction in the word error rate compared to the standalone Conformer and Transformer models, respectively, confirming the advancement of our method. The next step will involve researching how to improve the performance of dialect speech recognition by integrating external language models and extracting pronunciation features of different dialects, thereby achieving better recognition results. Full article

The field of tissue engineering has made significant advancements with extrusion-based bioprinting, which uses shear forces to create intricate tissue structures. However, the success of this method heavily relies on the rheological properties of bioinks. Most bioinks use shear-thinning. While a few component-based efforts have been reported to predict the viscosity of bioinks, the impact of shear rate has been vastly ignored. To address this gap, our research presents predictive models using machine learning (ML) algorithms, including polynomial fit (PF), decision tree (DT), and random forest (RF), to estimate bioink viscosity based on component weights and shear rate. We utilized novel bioinks composed of varying percentages of alginate (2–5.25%), gelatin (2–5.25%), and TEMPO-Nano fibrillated cellulose (0.5–1%) at shear rates from 0.1 to 100 s⁻¹. Our study analyzed 169 rheological measurements using 80% training and 20% validation data. The results, based on the coefficient of determination (R2) and mean absolute error (MAE), showed that the RF algorithm-based model performed best: [(R2, MAE) RF = (0.99, 0.09), (R2, MAE) PF = (0.95, 0.28), (R2, MAE) DT = (0.98, 0.13)]. These predictive models serve as valuable tools for bioink formulation optimization, allowing researchers to determine effective viscosities without extensive experimental trials to accelerate tissue engineering. Full article

Background: The tempo of resistance exercises is known to influence performance outcomes, yet its specific effects on post-activation performance enhancement (PAPE) remain unclear. This study aimed to investigate the effects of fast versus slow repetitions at a load of 70% of one-repetition maximum (1-RM) in the bench press exercise, focusing on velocity, surface electromyographic (sEMG) activity, and applied force while equating time under tension on bench press throw performance. Methods: Eleven men (age: 23.5 ± 5.4 years, height: 1.79 ± 0.04 m, body mass: 79.1 ± 6.4 kg, maximum strength 1-RM: 91.0 ± 12.0 kg) participated. Two experimental conditions (FAST and SLOW) and one control (CTRL) were randomly assigned. Participants performed two sets of six repetitions as fast as possible (FAST condition) and two sets of three repetitions at a controlled tempo (SLOW condition) at half the concentric velocity of FAST, as determined in a preliminary session. Before and after the bench press participants performed bench press throws tests (Pre, 45 s, 4, 8, and 12 min after). Results: sEMG activity and peak force during the bench press were higher in FAST vs. SLOW conditioning activity (p < 0.001), with time under tension showing no significant differences between conditions (p > 0.05). Mean propulsive velocity (MPV) during the bench press throw improved equally in both FAST and SLOW conditions compared with baseline from the 4th to the 12th min of recovery (FAST: +6.8 ± 2.9% to +7.2 ± 3.3%, p < 0.01, SLOW: +4.0 ± 3.0% to +3.6 ± 4.5%, p < 0.01, respectively). Compared to the CTRL, both conditions exhibited improved MPV values from the 4th to 12th min (p < 0.01). Peak velocity improvements were observed only after the FAST condition compared to the baseline (p < 0.01) with no differences from SLOW. For all muscles involved and time points, sEMG activity during bench press throws was higher than CTRL in both experimental conditions (p < 0.01), with no differences between FAST and SLOW. Peak force increased in both FAST and SLOW conditions at all time points (p < 0.05), compared to CTRL. Conclusions: These findings suggest that post-activation performance enhancement is independent of movement tempo, provided that the resistive load and total time under tension of the conditioning activity are similar. This study provides valuable insights into the complex training method for athletes by demonstrating that varying tempo does not significantly affect post-activation performance enhancement when load and TUT are equated. Full article

After musculoskeletal injuries, there is often a loss of corticospinal control. Current tendon rehabilitation may not adequately address the corticospinal control of the muscle which may contribute to the recalcitrance of symptom recurrence. This review provides a summary of the current literature regarding the effectiveness of tempo-controlled resistance training (TCRT) in (1) promoting corticospinal plasticity, (2) improving physical performance, and (3) improving strength outcomes in healthy adults. A comprehensive literature search was conducted using electronic databases (PubMed, CINAHL, Embase, and Google Scholar) to identify relevant studies published between 2010 and 2023. Randomized control (RCT) studies that included recreationally trained and untrained healthy adults between 18 and 60 years of age and that compared a TCRT intervention to a control condition were included. Twelve of the 1255 studies identified in the initial search were included in the final analysis. Throughout all included studies, TCRT was shown to elicit greater neural adaptations compared to traditional resistance training methods (i.e., self-paced strength training). These results indicate that TCRT holds promise as an effective method for modulating corticospinal plasticity in healthy adults and may enhance neuromuscular adaptations, including improvements in CSE, decreased SICI, enhanced motor unit synchronization, and voluntary muscle activation. Full article

Surveillance video analytics encounters unprecedented challenges in 5G and IoT environments, including complex intra-class variations, short-term and long-term temporal dynamics, and variable video quality. This study introduces Edge-Enhanced TempoFuseNet, a cutting-edge framework that strategically reduces spatial resolution to allow the processing of low-resolution images. A dual upscaling methodology based on bicubic interpolation and an encoder–bank–decoder configuration is used for anomaly classification. The two-stream architecture combines the power of a pre-trained Convolutional Neural Network (CNN) for spatial feature extraction from RGB imagery in the spatial stream, while the temporal stream focuses on learning short-term temporal characteristics, reducing the computational burden of optical flow. To analyze long-term temporal patterns, the extracted features from both streams are combined and routed through a Gated Recurrent Unit (GRU) layer. The proposed framework (TempoFuseNet) outperforms the encoder–bank–decoder model in terms of performance metrics, achieving a multiclass macro average accuracy of 92.28%, an F1-score of 69.29%, and a false positive rate of 4.41%. This study presents a significant advancement in the field of video anomaly recognition and provides a comprehensive solution to the complex challenges posed by real-world surveillance scenarios in the context of 5G and IoT. Full article

Regimented resistance training (RT) has been shown to promote increases in muscle size. When engaging in RT, practitioners often emphasize the importance of appropriate exercise technique, especially when trying to maximize training adaptations (e.g., hypertrophy). This narrative review aims to synthesize existing evidence on what constitutes proper RT exercise technique for maximizing muscle hypertrophy, focusing on variables such as exercise-specific kinematics, contraction type, repetition tempo, and range of motion (ROM). We recommend that when trying to maximize hypertrophy, one should employ a ROM that emphasizes training at long muscle lengths while also employing a repetition tempo between 2 and 8 s. More research is needed to determine whether manipulating the duration of either the eccentric or concentric phase further enhances hypertrophy. Guidelines for body positioning and movement patterns are generally based on implied theory from applied anatomy and biomechanics. However, existing research on the impact of manipulating these aspects of exercise technique and their effect on hypertrophy is limited; it is therefore suggested that universal exercise-specific kinematic guidelines are followed and adopted in accordance with the above recommendations. Future research should investigate the impact of stricter versus more lenient exercise technique variations on hypertrophy. Full article

An important challenge in machine learning is performing with accuracy when few training samples are available from the target distribution. If a large number of training samples from a related distribution are available, transfer learning can be used to improve the performance. This paper investigates how to do transfer learning more effectively if the source and target distributions are related through a Sparse Mechanism Shift for the application of next-frame prediction. We create Sparse Mechanism Shift-TempoRal Intervened Sequences (SMS-TRIS), a benchmark to evaluate transfer learning for next-frame prediction derived from the TRIS datasets. We then propose to exploit the Sparse Mechanism Shift property of the distribution shift by disentangling the model parameters with regard to the true causal mechanisms underlying the data. We use the Causal Identifiability from TempoRal Intervened Sequences (CITRIS) model to achieve this disentanglement via causal representation learning. We show that encouraging disentanglement with the CITRIS extensions can improve performance, but their effectiveness varies depending on the dataset and backbone used. We find that it is effective only when encouraging disentanglement actually succeeds in increasing disentanglement. We also show that an alternative method designed for domain adaptation does not help, indicating the challenging nature of the SMS-TRIS benchmark. Full article

Eccentric resistance training that focuses on the lengthening phase of muscle actions has gained attention for its potential to enhance muscle strength, power, and performance (among others). This review presents a methodological proposal for classifying eccentric exercises based on complexity, objectives, methods, and intensity. We discuss the rationale and physiological implications of eccentric training, considering its benefits and risks. The proposed classification system considers exercise complexity and categorizing exercises by technical requirements and joint involvement, accommodating various skill levels. Additionally, training objectives are addressed, including (i) Sports Rehabilitation and Return To Sport, (ii) Muscle Development, (iii) Injury Prevention, (iv) Special Populations, and (v) Sporting Performance, proposing exercise selection with desired outcomes. The review also highlights various eccentric training methods, such as tempo, isoinertial, plyometrics, and moderate eccentric load, each with different benefits. The classification system also integrates intensity levels, allowing for progressive overload and individualized adjustments. This methodological proposal provides a framework for organizing eccentric resistance training programs, facilitating exercise selection, program design, and progression. Furthermore, it assists trainers, coaches, and professionals in optimizing eccentric training’s benefits, promoting advancements in research and practical application. In conclusion, this methodological proposal offers a systematic approach for classifying eccentric exercises based on complexity, objectives, methods, and intensity. It enhances exercise selection, program design, and progression in eccentric resistance training according to training objectives and desired outcomes. Full article

Music Information Retrieval aims at extracting relevant features from music material, while Music Performance Analysis uses these features to perform semi-automated music analysis. Examples of interdisciplinary cooperation are, for example, various classification tasks—from recognizing specific performances, musical structures, and composers to identifying music genres. However, some classification problems have not been addressed yet. In this paper, we focus on classifying string quartet music interpretations based on the origin of performers. Our dataset consists of string quartets from composers A. Dvořák, L. Janáček, and B. Smetana. After transferring timing information from reference recordings to all target recordings, we apply feature selection methods to rank the significance of features. As the main contribution, we show that there are indeed origin-based tempo differences, distinguishable by measure durations, by which performances may be identified. Furthermore, we train a machine learning classifier to predict the performers’ origin. We evaluate three different experimental scenarios and achieve higher classification accuracy compared to the baseline using synchronized measure positions. Full article

The ultimate goal of rehabilitation engineering is to provide objective assessment tools for the level of injury and/or the degree of neurorehabilitation recovery based on a combination of different sensing technologies that enable the monitoring of relevant measurable variables, as well as the assessment of non-measurable variables (such as muscle effort/force and joint mechanical stiffness). This paper aims to present a feasibility study for a general assessment methodology for subject-specific non-measurable elbow model parameter prediction and elbow joint stiffness estimation. Ten participants without sensorimotor disorders performed a modified “Reach and retrieve” task of the Wolf Motor Function Test while electromyography (EMG) data of an antagonistic muscle pair (the triceps brachii long head and biceps brachii long head muscle) and elbow angle were simultaneously acquired. A complete list of the Hill’s muscle model and passive joint structure model parameters was generated using a genetic algorithm (GA) on the acquired training dataset with a maximum deviation of 6.1% of the full elbow angle range values during the modified task 8 of the Wolf Motor Function Test, and it was also verified using two experimental test scenarios (a task tempo variation scenario and a load variation scenario with a maximum deviation of 8.1%). The recursive least square (RLS) algorithm was used to estimate elbow joint stiffness (Stiffness) based on the estimated joint torque and the estimated elbow angle. Finally, novel Stiffness scales (general patterns) for upper limb functional assessment in the two performed test scenarios were proposed. The stiffness scales showed an exponentially increasing trend with increasing movement tempo, as well as with increasing weights. The obtained general Stiffness patterns from the group of participants without sensorimotor disorders could significantly contribute to the further monitoring of motor recovery in patients with sensorimotor disorders. Full article