A sport-specific wearable jump monitor for figure skating

Development of a simple algorithm to detect big air jumps and jumps during skiing

Jumping is an important task in skiing, snowboarding, ski jumping, figure skating, volleyball and many other sports. In these examples, jumping tasks are a performance criterion, and therefore detailed insight into them is important for athletes and coaches. Therefore, this paper aims to introduce a simple and easy-to-implement jump detection algorithm for skiing using acceleration data from inertial measurement units attached to ski boots. The algorithm uses the average of the absolute vertical accelerations of the two boots. We provide results for different parameter settings of the algorithm and two types of jumps: Big Air jumps and jumps during skiing. The latter are divided into small (time of flight < 500 ms) and medium (time of flight ≥ 500 ms) jumps. The algorithm detects 100% of Big Air, 94% of medium and 44% of small jumps. In addition, the settings with the highest detection rates also have the highest number of overdetected jumps. To resolve this conflict, a penalty-adjusted score that considers the number of overdetected jumps in the final performance analysis is proposed.

Musculoskeletal Injury in an Australian Professional Ballet Company, 2018-2021: 953 Medical-Attention and 706 Time-Loss Injuries Over 4 Years

OBJECTIVES: To describe the incidence rate, frequency, severity, recurrence, and burden of musculoskeletal injury in professional ballet. STUDY DESIGN: Descriptive epidemiological (retrospective). METHODS: Professional dancers (n = 73, 40 females, 33 males) provided consent for retrospective review of musculoskeletal injury data. Medical-attention injuries were reported to and recorded by onsite physiotherapists between January 2018 and December 2021. Time-loss injuries were any injury that prevented a dancer from taking a full part in all dance-related activities for >1 day. Injuries were classified using the OSICS-10.1 system. Injury incidence rates (IIRs; injuries/1000 h), severity, recurrence, and burden were calculated. RESULTS: Nine hundred and fifty-three medical-attention injuries were recorded in 72 (98%) dancers at an IIR of 2.79/1000 h (95% confidence interval [CI], 2.62-2.98). 706 were time-loss injuries, which were reported in 70 dancers at an IIR of 2.07/1000 h (95% CI: 1.92, 2.23). Overuse injuries represented 53% of medical-attention injuries. The most frequently injured body area and tissue/pathology were thoracic facet joint (n = 63/953, 7%) and ankle synovitis/impingement (n = 62/953, 6%). Bone stress injuries (BSIs) were the most severe with the highest median time loss (135 days, interquartile range [IQR] 181) followed by fractures (72.5 days, IQR 132). The injuries with the highest burden were tibial BSIs (13 days lost/1000 h; 95% CI: 13, 14). Jumping and lifting were the most frequently reported injury mechanisms. CONCLUSION: Almost all dancers required medical attention for at least one injury during the surveillance period. Approximately 74% of injuries resulted in time loss. BSIs and ankle synovitis/impingement were of high burden, and a high proportion of BSIs were recurrent. J Orthop Sports Phys Ther 2023;53(11):712-722. Epub 14 September 2023. doi:10.2519/jospt.2023.11858.

Telemedicine in Sports under Extreme Conditions: Data Transmission, Remote Medical Consultations, and Diagnostic Imaging

Telemedical technologies provide significant benefits in sports for performance monitoring and early recognition of many medical issues, especially when sports are practised outside a regulated playing field, where participants are exposed to rapidly changing environmental conditions or specialised medical assistance is unavailable. We provide a review of the medical literature on the use of telemedicine in adventure and extreme sports. Out of 2715 unique sport citations from 4 scientific databases 16 papers met the criteria, which included all research papers exploring the use of telemedicine for monitoring performance and health status in extreme environments. Their quality was assessed by a double-anonymised review with a specifically designed four-item scoring system. Telemedicine was used in high-mountain sports (37.5%; n = 6), winter sports (18.7%; n = 3), water sports (25%; n = 4), and long-distance land sports (18.7%; n = 3). Telemedicine was used for data transfer, teleconsulting, and the execution of remote-controlled procedures, including imaging diagnostics. Telemedical technologies were also used to diagnose and treat sport-related and environmentally impacted injuries, including emergencies in three extreme conditions: high mountains, ultraendurance activities, and in/under the water. By highlighting sport-specific movement patterns or physiological and pathological responses in extreme climatic conditions and environments, telemedicine may result in better preparation and development of strategies for an in-depth understanding of the stress of the metabolic, cardiorespiratory, biomechanical, or neuromuscular system, potentially resulting in performance improvement and injury prevention.

The development and validation of an open-source accelerometery algorithm for measuring jump height and frequency in ballet

The aim was to determine the validity of an open-source algorithm for measuring jump height and frequency in ballet using a wearable accelerometer. Nine professional ballet dancers completed a routine ballet class whilst wearing an accelerometer positioned at the waist. Two investigators independently conducted time-motion analysis to identify time-points at which jumps occurred. Accelerometer data were cross-referenced with time-motion data to determine classification accuracy. To determine the validity of the measurement of jump height, five participants completed nine jetés, nine sautés and three double tour en l'air from a force plate. The jump height predicted by the accelerometer algorithm was compared to the force plate jump height to determine agreement. Across 1440 jumps observed in time-motion analysis, 1371 true positives, 34 false positives and 69 false negatives were identified by the algorithm, resulting in a sensitivity of 0.98, a precision of 0.95 and a miss rate of 0.05. For all jump types, mean absolute error was 2.6 cm and the repeated measures correlation coefficient was 0.97. Bias was 1.2 cm and 95% limits of agreement were -4.9 to 7.2 cm. The algorithm may be used to manage jump load, implement periodization strategies, or plan return-to-jump pathways for rehabilitating athletes.

Multi-view 3D human pose reconstruction based on spatial confidence point group for jump analysis in figure skating

Competitive figure skaters perform successful jumps with critical parameters, which are valuable for jump analysis in athlete training. Driven by recent computer vision applications, recovering 3D pose of figure skater to obtain the meaningful variables has become increasingly important. However, conventional works have suffered from getting 3D information based on the corresponding 2D information directly or leaving the specificity of sports out of consideration. Issues such as self-occlusion, abnormal pose, limitation of venue and so on will result in poor results. Motivated by these problems, this paper proposes a multi-task architecture based on a calibrated multi-camera system to facilitate jointly 3D jump pose of figure skater. The proposed methods consist of three key components: Likelihood distribution and temporal smoothness- based discrete probability points selection filter out the most valuable 2D information; Multi-perspective and combinations unification-based large-scale venue 3D reconstruction is proposed to deal with the multi-camera; multi-constraint-based human skeleton estimation decides the final 3D coordinate from the candidates. This work is proved can be applied to 3D animated display and motion capture of the figure skating competition. The success rate of the independent joint is: 93.38% of 70 mm error range, 92.57% of 50 mm error range and 91.55% of 30 mm error range.

A Wearable System for Jump Detection in Inline Figure Skating

This article presents the design and experimental evaluation of a non-invasive wearable sensor system that can be used to acquire crucial information about athletes’ performance during inline figure skating training. By combining distance and time-of-flight sensors and gyroscopes, the system is able to detect when jumps are performed and provides a live view of the data (e.g., the number and height of jumps) through a graphical user interface. The main novelty of our approach lies in the way in which the optical sensors are orientated. Typically, the sensors are orientated horizontally and positioned in pairs on the ground, where they measure the time interval between the moment the athlete leaves the ground and the moment they land. In our system, an optical sensor is placed under each foot and is vertically orientated so as to constantly measure the distance from the ground. In addition, a gyroscope sensor is placed on the athlete’s back, which provides information on the direction and angular momentum of the movement. By combining this data, the system provides the accurate detection of various jumps and technical elements without any constraints on the training ground. In this paper, the system is also compared to similar platforms in the literature, although there are no other specific systems that are available for inline figure skating. The results of the experimental evaluation, which was performed by high profile athletes, confirm its effectiveness in correctly detecting jumps, especially considering its compromise between precision and the overall cost of the equipment.