Frontiers in Sports and Active Living

A common approach in biomechanical analysis of running technique is to average data from several gait cycles to compute a representative mean. However, the impact of the quantity and selection of gait cycles on biomechanical measures is not well understood. We examined the effects of gait cycle selection on kinematic data by: (i) comparing representative means calculated from varying numbers of gait cycles to global means from the entire capture period; and (ii) comparing representative means from varying numbers of gait cycles sampled from different parts of the capture period. We used a public dataset (n = 28) of lower limb kinematics captured during a 30-second period of treadmill running at three speeds (2.5m {middle dot} s-1, 3.5m {middle dot} s-1 and 4.5m {middle dot} s-1). Ground truth values were determined by averaging data across all collected strides and compared to representative means calculated from random samples (1,000 samples) of n (range = 5--30) consecutive gait cycles. We also compared representative means calculated from n (range = 5--15) consecutive gait cycles randomly sampled (1,000 samples) from within the same data capture period. The mean, variance and range of the absolute error of the representative mean compared to the ground truth mean progressively reduced across all speeds as the number of gait cycles used increased. Similar magnitudes of error were observed between the 2.5m {middle dot} s-1 and 3.5m {middle dot} s-1 speeds at comparable gait cycle numbers -- where the maximum errors were < 1.5 degrees even with a small number of gait cycles (i.e. 5-10). At the 4.5m {middle dot} s-1 speed, maximum errors typically exceeded 2-4 degrees when a lower number of gait cycles were used. Subsequently, a higher number of gait cycles (i.e. 25-30) was required to achieve low errors (i.e. 1-2 degrees) at the 4.5m {middle dot} s-1 speed. The mean, variance and range of absolute error of representative means calculated from different parts of the capture period was consistent irrespective of the number of gait cycles used. The error between representative means was low (i.e. <1.5 degrees) and consistent across the different number of gait cycles at the 2.5m {middle dot} s-1 and 3.5m {middle dot} s-1 speeds, and consistent but larger (i.e. up to 2-4 degrees) at the 4.5m {middle dot} s-1 speed. Our findings suggest that selecting as many gait cycles as possible from a treadmill running bout will minimise potential error. Analysing a small sample (i.e. 5-10 cycles) will typically result in minimal error (i.e. < 2 degrees), particularly at lower speeds (i.e. 2.5m {middle dot} s-1 and 3.5m {middle dot} s-1). Researchers and clinicians should consider the balance between practicalities of collecting and analysing a smaller number of gait cycles against the potential error when determining their methodological approach. Irrespective of the number of gait cycles used, we recommend that the potential error introduced by the choice of gait cycle number be considered when interpreting the magnitude of effects in treadmill-based running studies.

Although weightlifting exercises and their pulling and catching derivatives have been well studied, less is known about the group of weightlifting overhead pressing derivatives (WOPDs). There were two purposes of the present study. Above all, it was to compare WOPDs (i.e. jerk and back jerk) with jump squat in enhancing sport performance such as vertical jump in the perspective of biomechanics. Furthermore, it was to gain a better understanding of the biomechanical difference between jerk and back jerk. The study compared the kinetics, kinematics, and muscle activation of the jerk, back jerk, vertical jump and jump squat. Ground reaction forces, joint angle data and electromyography were collected from 20 track and field athletes while they performed the four movements. The joint coordination pattern and EMG characters of the jump squat were more similar to those of the vertical jump. WOPDs, especially jerk, exhibited less similarity with vertical jump in joint coordination, as well as the subsequent peak activation. The electromyography data demonstrated significant differences in rectus femoris and gluteus maximus in the relative timing of peak activations and the maximum activation. Jump squat was greater in peak force (2211N) than jerk (P < .005), greater peak power (4749W) than jerk and back jerk (P < .001) and no significant difference with WOPDs in rate of force development. Back jerk produced a greater peak force (2223 N), peak power (3767W) and eccentric RFD (15965 N{middle dot}s-1) than jerk (2061N)(P < .001) (3364W)(P < .05) (12280 N{middle dot}s-1)(P < .01)and the opposite in concentric RFD that jerk (7702 N{middle dot}s-1) was greater than back jerk (5972 N{middle dot}s-1)(P < .05). We can conclude that for runner and jumpers, jump squat was better for improving vertical jump than WOPDs and back jerk is better than jerk in power training.

Anterior cruciate ligament (ACL) injury and ACL reconstruction (ACLR) surgery are common. Many ACL-injured subjects develop osteoarthritis within a decade of injury, a major cause of disability without cure. Laboratory-based biomechanical assessment can evaluate ACL injury risk and rehabilitation progress after ACLR; however, lab-based measurements are expensive and inaccessible to a majority of people. Portable sensors such as wearables and cameras can be deployed during sporting activities, in clinics, and in patient homes for biomechanical assessment. Although many portable sensing approaches have demonstrated promising results during various assessments related to ACL injury, they have not yet been widely adopted as tools for ACL injury prevention training, evaluation of ACL reconstructions, and return-to-sport decision making. The purpose of this review is to summarize research on out-of-lab portable sensing applied to ACL and ACLR and offer our perspectives on new opportunities for future research and development. We identified 49 original research articles on out-of-lab ACL-related assessment; the most common sensing modalities were inertial measurement units (IMUs), depth cameras, and RGB cameras. The studies combined portable sensors with direct feature extraction, physics-based modeling, or machine learning to estimate a range of biomechanical parameters (e.g., knee kinematics and kinetics) during jump-landing tasks, cutting, squats, and gait. Many of the reviewed studies depict proof-of-concept methods for potential future clinical applications including ACL injury risk screening, injury prevention training, and rehabilitation assessment. By synthesizing these results, we describe important opportunities that exist for using sophisticated modeling techniques to enable more accurate assessment along with standardization of data collection and creation of large benchmark datasets. If successful, these advances will enable widespread use of portable-sensing approaches to identify ACL injury risk factors, mitigate high-risk movements prior to injury, and optimize rehabilitation paradigms.

Clinical Gait Analysis (CGA) is a pivotal technique for evaluating pathological conditions, particularly musculoskeletal disorders. However, its efficacy is often hindered by the fact that normative gait data is almost always used worldwide as a basis for CGA, regardless of differences in critical parameters such as BMI, age, gender, and walking speeds. To address this, we developed multiple regression models for predicting lower limb sagittal kinematic waveforms. We recorded anthropometric, demographic, spatiotemporal, and kinematic data from 30 healthy individuals. Leveraging the gait cycle time and joint angles as dependent variables, and BMI, age, gender, and walking speeds as predictors, we developed 46 regression equations. We employed PCHIP utilizing 80% of the kinematic data to reconstruct the waveforms and validated via leave-one-out cross validation. Our models successfully reconstructed hip, knee, and ankle kinematic waveforms, achieving R2 [&ge;] 0.9 and RMSE [&le;] 6{degrees} from the validation study. P-values < 0.05 as well as the clinical relevance of the predictors were considered during the regression analysis. These outcomes underscore the potential for our approach to be used as the basis to enhance the precision of region-specific gait data predictions, thus facilitating more accurate CGA.

Repeated high-intensity running (RHIR) exercise is known to affect central and peripheral functioning. Declines in RHIR performance are exacerbated by environmental heat stress. Accordingly, the use of post-exercise cooling strategies (COOL) is recommended as it may assist recovery. The present study aimed to investigate, in a hot environment (> 30{o}C), the effects of local COOL following RHIR on indices of soccer kicking movement and performance in youth soccer. Fifteen academy under-17 players (16.27 {+/-} 0.86 years-old; all post-PHV), acting as their own controls, participated. In #Experiment 1, players completed an all-out RHIR protocol (10 x 30 m bouts interspersed with 30 s intervals). In #Experiment 2, the same players performed the same running protocol under two conditions, 1) 5 minutes of COOL where ice packs were applied to the quadriceps and hamstrings regions and, 2) a control condition involving only passive resting. In both experiments, perceptual measures [ratings of perceived exertion (RPE), pain and recovery], thigh temperature and kick-derived video kinematics (hip, knee, ankle and foot) and performance (ball speed and placement) were collected at baseline and post exercise and intervention. In the first experiment, RHIR led to moderate-to-large increases (p < 0.03) in RPE (d = 4.08), ankle eversion/inversion angle (d = 0.78) and mean radial error (d = 1.50) and small-to-large decreases (p < 0.04) in recovery (d = -1.83) and average/peak ball speeds (d = -0.42--0.36). In the second experiment RPE (p < 0.01; Kendalls W = 0.30) and mean radial error (p = 0.057; {eta}2 = 0.234) increased only post-control. Significant small declines in ball speed were also observed only post-control (p < 0.05; d = 0.35). Post-intervention CMfoot velocity was moderately faster in COOL as compared to control (p = 0.04; d = 0.60). RHIR acutely impaired kicking movement, ball speed and placement in youth soccer players. However, a short period of local cryotherapy may be beneficial in counteracting declines in indices of kicking performance in hot environment. Trial registration number#RBR-8prx2m - ReBEC Brazilian Clinical Trials Registry

BackgroundIt remains unclear which movement quality items are associated with second anterior cruciate ligament (ACL) injuries after ACL reconstruction. Hypotheses1) Movement quality measured with the Landing Error Scoring System (LESS) is not associated with second ACL injury in soccer players who returned to sports after ACL reconstruction. 2) In an exploratory analysis, soccer players with and without second ACL injuries display differences in individual LESS items. MethodsFourteen male recreational ACL-reconstructed soccer players had non-fatigued and fatigued movement quality measurement with LESS score three years ago and were now interviewed by phone about soccer participation and second ACL injuries. ResultsTwo soccer players did not return to soccer and were therefore excluded from analysis. Six of twelve (50%) included soccer players had a second ACL injury. There was no association between the non-fatigued (p=1.000) and fatigued (p=0.455) LESS score and second ACL injuries. However, when exploring individual LESS items, we found 50% of the soccer players with second ACL injury landing with little trunk and knee flexion displacement in combination with knee valgus, while none of the soccer players without second ACL injury had this combination of LESS errors. This difference was only seen in the fatigued state. ConclusionsIt might be the combination of little knee and trunk flexion displacement with knee valgus during landing which predisposes an athlete to a second ACL injury, especially in a fatigued state. Clinical relevanceWhen screening athletes after ACL reconstruction for second ACL injury risk, combining measurement of knee valgus with knee and trunk flexion displacement in a fatigued state might be give more insight into risk than using the complete LESS score or evaluating knee valgus as a sole risk factor.

IntroductionCycling performance is influenced by hip flexor and extensor muscle strength. While belt stabilised handheld dynamometers (B-HHD) are valid for measuring isometric hip muscle strength, fixed frame dynamometers are becoming popular, offering potentially better stability and reliability. However, the reliability of both devices has not been examined in cyclists. This study evaluated the test-retest reliability and agreement between a B-HHD (MicroFET2, Hoggan Scientific) and a fixed-frame dynamometer (ForceFrame (FF) Max, Vald Performance) for hip flexion and extension peak force measurement in cyclists. MethodsA test-retest design was used. Twenty-five recreational cyclists (age {+/-} SD: 36.64 ({+/-}12.34) years; 22 males) were tested twice, approximately 72 hours apart. Three unilateral maximal voluntary isometric contractions (MVIC) of the hip flexors and extensors of each limb were performed, using the B-HHD and FF in a random order. Within and between session reliability was determined using Intraclass correlation coefficients 3.1 & 3.k. Standard error of measurements (SEM) and minimal detectable changes (MDC) were calculated. Agreement was assessed using 95% limits of agreement (LOA). ResultsFor hip flexion, within and between session reliability was good to excellent, and SEMs were similar (B-HHD ICCs = 0.77-0.93, SEMs = 14.25-22.71N (7.19-10.38%); FF ICCs = 0.77-0.95, SEMs = 7.80N-18.98N (3.47%-8.54%)). FF MDCs were lower within-session (21.61-39.48N (9.60-17.97%)) than B-HHD MDCs (39.50-62.95N (19.94-28.78%)), but similar between-sessions (FF MDCs= 41.25-52.61N (19.42-23.66%); B-HHD MDCs 41.21N-48.95N (18.53-23.77%)). For hip extension, both devices demonstrated good to excellent reliability and SEMs were similar (B-HHD ICCs = 0.90-0.95, SEMs = 15.77-21.53N ( 7.38-9.96%); FF ICCs = 0.85-0.95, SEMs =19.21-29.05N (7.82-11.78%) within and between sessions). All LOA exceeded a 20N acceptability threshold. ConclusionBoth devices are reliable in recreational cyclists, but large MDCs suggest that caution is needed when interpreting repeated measurements. Both devices cannot be used interchangeably due to poor agreement.

To collect reliable data, it is important to determine how inertial measurement unit (IMU) sensor placement affects measurements of segment motion and to establish best practices for sensor placement and orientation procedures. We aimed to determine the extent to which a functional orientation method minimizes the effect of variations in sensor placement on IMU-derived segment excursions. Twenty healthy adults walked overground in a lab. Three IMUs were placed per segment on the pelvis, thigh, shank, and foot. Differences in estimated segment angular excursions between sensor placements were compared between an assumed sensor-to-segment orientation and two versions of a walking-based functional sensor-to-segment orientation. For time series data, differences in angular excursion between sensor placements were reduced from 60% of the gait cycle (assumed orientation) to 44% of the gait cycle (functional orientation) for the pelvis; from 31% (assumed) to 28% (functional) of the gait cycle for the thigh; and from 84% (assumed) to 0% (functional) of the gait cycle for the shank. Mean angular excursion RMSDs between sensor placements were <5{degrees} for most comparisons for the functionally oriented data. Functional orientation did not minimize inter-sensor placement effects when one sensor was located on a region with high soft tissue artifact (e.g., anterior thigh). Functional orientation reduced inter-sensor differences in segment excursion for the shank and foot and between-subject variance in inter-sensor differences for all segments. Functional orientation minimizes the effect of variations in IMU sensor placement, but care should be taken to select sensor placements that minimize soft-tissue artifact.

Navigating public environments requires adjustments to ones walking patterns to avoid stationary and moving obstacles. It is known that physical inactivity induces alterations in motor capacities, but the impact of inactivity on anticipatory locomotor adjustments (ALA) has not been studied. The purpose of the present study was to compare ALAs and related muscle co-contraction during a pedestrian circumvention task between active (AA) and inactive young adults (IA). Thirteen AA and thirteen IA were placed in a virtual environment simulating a public park. Participants circumvented virtual pedestrians walking towards them. Walking speed, onset of deviation, clearance, foot placement strategies and muscle co-contraction were analysed. IA exhibited slower walking speeds compared to the AA during circumvention condition but not during unobstructed walking condition. The distance at the onset of trajectory deviation was larger for IA. Both groups increased co-contraction for pedestrian circumvention at the ankle and left hip and IA displayed greater ankle co-contraction overall. No significant group differences were observed in minimum clearance. This study suggests that an inactive lifestyle influences ALAs by inducing a cautious behavior during pedestrian circumvention.

The ankle joint, a pivotal element in lower limb-ground interactions, plays a critical role in maintaining gait and balance. In this study, we utilized the Ambulosono device--a sensor- based, music-contingent digital tool designed to assist and monitor ankle training--to investigate the effects of ankle rotation training on functional balance. We measured the durations of the single-leg stand test (SLST) under eyes-closed conditions in a cohort of healthy young adults. Comparisons of pre- and post-training SLST durations were made between the trained and untrained legs within the same subjects. Our findings demonstrated a substantial increase in the SLST durations ipsilateral to the trained ankles, while the untrained ankles in the control legs showed no significant changes. This enhancement in balance function was observed to persist for several hours post-training.

The assessment of physical ability is a critical component in developing personalized exercise prescriptions, monitoring disease progression, and evaluating intervention outcomes across various clinical and general populations. This study evaluates how objective physical performance parameters, measured during a stepping in place (SIP) exercise via Ambulosono wearable system, relate to subjective perceptions of fatigue and breathlessness using Borg and Fatigue Scores. Our overall results show that SIP, as a convenient and simple exercise modality, can be used to rank a users physical ability level based on both objective and subjective parameters. Furthermore, while the objective walking/gait parameters may have some predictive ability for the such parameter as cadence, they do not appear to significantly predict the subjective fatigue or breathlessness scores, either before or after the activity. This lack of significant relationships suggests that factors other than the measured objective gait metrics may play a more important role in determining subjective experiences of fatigue and breathlessness during the stepping exercise.

This paper quantifies the effectiveness and generalizability of an intervention that aims at restoring bicycle balance control skills in seniors that have quit cycling. The intervention increased the difficulty of the bicycle balance control task in a stepwise fashion, gradually approaching the difficulty of bicycle balance control on the public roads. The intervention involved three components: (1) training on an exercise bicycle, (2) balance control training on a bicycle simulator, and (3) cycling on the public roads with a safe start-and-stop technique that was practiced on the bicycle simulator. The experimental group consisted of 23 community-dwelling senior citizens that had quit cycling (N=19) or that were on the verge of doing so (N=4). The effectiveness of the intervention was evaluated by comparing balance control skill and confidence between a post-and a pre-intervention measurement. To control for a possible spontaneous recovery, the same comparison was also performed in a matched control group that did not participate in the intervention. The intervention produced a very large improvement (Cohens d = 1.5, t(16)=6.0, p<.001) in balance control skills and confidence on the public roads. Bicycle balance control skills and confidence can thus be restored over a short period of time. In the Discussion section, I argue that this very large improvement does not rule out the importance of the slower process of acquiring a sufficient lower body strength.

Prior research has focused solely on the prevalence of anterior cruciate ligament (ACL) injuries in male and female alpine ski racers and on the Q-angle, the angle created between the quadriceps muscles and the patella tendon, in ways unrelated to alpine ski racing. A survey was sent to female alpine ski racers who represent the academy U16 level through World Cup professionals that included questions regarding the athletes racing level, Q- angle, and past knee injuries. A total of 124 responses were collected. Just under half (45.16%) of the survey participants reported a history of at least one knee injury. A great majority, 83.33%, of the Professional athletes reported tearing their knee at least once, which is 3.04 and 1.90 times more than athletes of the Pre Professional and U16 levels respectively. Athletes with a Q-angle of 15{degrees} or 20{degrees} had torn 85.26% of the total ligaments with the ACL, MCL, and meniscus being the most frequently torn ligaments. From this self-reported data, it is clear that Q-angles and racing level both influence the frequency of ligament tears in female alpine ski racers. An athlete competing at a higher level of competitive ski racing is more likely to tear a knee ligament than those at lower levels, and larger Q- angles influence the risk of injury as well as injury to specific ligaments within the knee.

This study assessed the concurrent validity and between-device reliability of the Catapult Vector S8 GNSS device for measuring distance, speed, acceleration and banded distance metrics. Twelve male sub-elite team sport athletes completed a testing protocol consisting of linear sprints, change of direction drills and a modified small-sided game. Validity was evaluated against criterion reference systems, a VICON motion capture system for most trials and a Stalker ATS radar for 50 m sprints, evaluated using root mean square error (RMSE) and mean bias. Between-device reliability was assessed using interclass correlation coefficients (ICC) and typical error (TE%). The Vector S8 demonstrated good validity with minimal errors for instantaneous distance (RMSE: 0.03 {+/-} 0.01 m), speed (RMSE: 0.14 {+/-} 0.05 ms-1) and acceleration (RMSE: 0.29 {+/-} 0.14 ms-2). No overall bias was detected for instantaneous distance and speed, and the bias for acceleration (-0.016) was minimal. Accumulated distance showed a small underestimation across trials (mean bias -1.42%), with consistently low RMSE values (0.26-3.06 m), indicating high measurement precision. The 50 m sprint results showed similar validity, with minimal RMSE for instantaneous speed (0.14 {+/-} 0.15 ms-1) and acceleration (0.22 {+/-} 0.22 ms-2). Between device reliability demonstrated excellent agreement across all measured variables (ICC [&ge;] 0.90) with good precision (TE as CV <5%). No significant systematic bias was observed between devices for any variable (p > 0.05). This is the first study to validate the Catapult Vector S8 GNSS device, demonstrating that it is valid and reliable for measuring distance, speed and acceleration during sport-specific movements.

Balancing intense training with adequate recovery is key for optimal athlete performance. While insufficient or excessive training can adversely impact performance, advancements in wearable technology facilitate more effective monitoring of training and readiness level. This study combined data from Garmin watches and a 9-item questionnaire (Readiness Advisor) application to evaluate the readiness during a 39-day long training period. Two groups were studied: one with daily adaptive modifications of the training according to their readiness level and another with a static regimen where no changes were made regardless of readiness level. Results indicated that the adaptive group maintained a more consistent readiness score and showed improved physiological responses and better performance metrics. In contrast, the static group displayed non-significant improvements. This suggests that adaptive training plans, driven by individualized data and AI analytics, can significantly enhance performance outcomes and physiological adaptations for runners.

BackgroundMeteorological factors such as barometric pressure, humidity, and temperature have been linked to weather-related symptoms in the general population, yet little is known about their influence on athletes daily well-being and performance. Individual variability in weather sensitivity has been reported in biometeorology research, suggesting that only certain individuals exhibit pronounced physiological responses to environmental fluctuations. However, no studies have examined within-person associations between multiple meteorological factors and daily condition or performance in competitive athletes. MethodsCollegiate baseball players were monitored over 10 randomly selected days during July-August 2025. Subjective condition and performance were assessed daily using a 3-point Likert scale (1 = poor, 2 = normal, 3 = good). Barometric pressure, humidity, and temperature were recorded hourly and summarized for each day using mean values, day-to-day changes, daily ranges, and rapid fluctuation indices. For each player, multivariable linear regression models were constructed to examine within-person associations between the three meteorological variables and daily condition or performance. Model fit (R2), regression coefficients ({beta}), and dominant meteorological factors were extracted. ResultsEighty players were included in the condition model and eighty-six in the performance model. High weather sensitivity (R2 [&ge;] 0.60) was observed in 22.5% of players for condition and 14.0% for performance, whereas low sensitivity (R2 [&le;] 0.20) was found in 26.3% and 16.3%, respectively. Temperature was the dominant explanatory factor in more than 80% of players, although subsets showed dominance of barometric pressure or humidity. Directionality varied across individuals: decreases in barometric pressure were associated with worsening conditions in 62.5% of players but improvement in 37.5%; similar bidirectional patterns were observed for humidity and temperature. ConclusionDaily meteorological fluctuations explain a meaningful proportion of within-person variation in condition and performance for a subset of collegiate baseball players. The substantial individual variability and diverse directional responses highlight weather sensitivity as a personalized characteristic rather than a uniform effect. These findings suggest that meteorological factors may represent a relevant contextual variable for daily readiness monitoring in susceptible athletes.

The seated medicine ball throw (SMBT) is a field test intended to assess upper-body muscular power by measuring the maximal distance an individual can throw a medicine ball from an isolated, seated position (25). The SMBT has been used to assess upper-body power in various populations and to establish concurrent validity for other measures of upper-body power such as the bench press power test and the plyometric push-up. The SMBT is less costly and simpler to incorporate into a field test battery than other upper body power assessments. While the SMBT is a valid, reliable field test for upper-body power, normative reference standards for most populations, including adolescent (12-15 years old) physical education students, do not exist. PurposeThis study reports distances thrown in the SMBT to establish normative reference values in male and female physical education students, ages 12-15 years old. MethodsOne hundred thirteen untrained male and female physical education students aged 12-15 years performed the SMBT field test three times on a single testing day. Participants threw a 2kg medicine ball with a 19.5 cm diameter while seated at a 90{degrees} after recording height, body mass, and BMI. ResultsParticipant data was separated by age gender for analysis. Mean and standard deviation for the SMBT for males was 4.3{+/-}0.7m and 5.2{+/-}0.8 m for ages 12-13 and 14-15, respectively, and for females was 3.4{+/-}0.5m and 3.7{+/-}0.5m for ages 12-13 and 14-15, respectively. Pearson correlation coefficients for between-trials comparisons for males and females ranged from r=0.85-0.97. Pearson correlation coefficients for average SMBT and age of participants was r=0.93. Normative reference values as percentile ranks for the SMBT scores for age groups 12-13 and 14-15 among males and females, respectively, were also established. ConclusionThe data presented provides an initial set of normative reference standards for coaches and students to determine upper-body muscular power using the SMBT.

Conventional sports anticipation studies have primarily relied on hypothesis-testing paradigms that target predetermined cues. However, such approaches risk overlooking unanticipated sources of predictive information. This study addresses this limitation by introducing data-driven analysis using machine learning (ML) models as a complementary approach to conventional experimental research. Given that predictive cues embedded within movements can enhance the prediction accuracy of ML models, the proposed analysis identified spatiotemporal cues for prediction and quantified the effects of accumulating opponent-specific information across trials. Motion-capture data were collected from eight collegiate baseball pitchers, and joint-angle time series were analyzed using logistic regression models to predict pitch type (fastball vs. breaking ball). Specifically, two analyses were conducted: (1) a sliding time-window analysis to identify when and where predictive cues emerged within target motions and (2) a set-size analysis to evaluate how prediction accuracy varied with dataset size. The main results revealed that (1) predictive cues were distributed across the entire body, but models integrating whole-body information achieved the highest accuracy; (2) informative cues emerged in most body regions around the initiation of the pitchers weight shift; (3) the accumulation of opponent-specific information had a pronounced effect up to approximately 30 pitches; and (4) substantial individual differences existed in when and which cues were effective for pitch-type prediction. Although alignment with human athletes must be carefully examined in future work, these findings highlight the utility of the proposed analysis as a complementary approach to conventional hypothesis-testing experiments.

ObjectiveAchilles tendon ruptures lead to long-term structural and functional deficits. Prior research that sought to identify optimal rehabilitation techniques was fundamentally limited by the inability to continuously monitor Achilles tendon loading during rehabilitation. Our objective was to develop a data-driven model that predicts per-step peak Achilles tendon loading from only a single, boot-mounted accelerometer. MethodsNineteen patients recovering from an acute Achilles tendon rupture completed in-lab walking trials while wearing an instrumented immobilizing boot. A boot-mounted inertial measurement unit provided acceleration signals used for prediction, while a force-sensing insole provided ground truth tendon-loading data through a validated ankle moment balance. We developed a stance-detection algorithm, as well as a personalized one-dimensional convolutional neural network (1D-CNN) to estimate per-step peak Achilles tendon load. Our training framework incorporated a small patient-specific personalization sample and was evaluated on held-out steps. ResultsThe stance detection algorithm identified stance phases with 99.8% precision and mean timing errors of 27.3 ms for heel strike and 61.9 ms for toe-off. The CNN estimated per-step peak Achilles tendon load with a mean absolute error of 0.14 bodyweights (R2=0.68) across rupture patients. ConclusionContinuous, objective estimation of Achilles tendon loading during early rehabilitation is feasible using a single, boot-mounted accelerometer. Model errors were small (9%) relative to the wide range of tendon loading exhibited during immobilizing boot walking. Our proposed approach enables clinicians to continuously monitor mechanical loading during a previously unobservable rehabilitation period and provides a foundation for personalized rehabilitation guidance after Achilles rupture.

The aim of this study was to determine which physiological variables could predict performance during a CrossFit competition. Fifteen male CrossFit athletes (35 {+/-} 9 years) participated and performed a series of tests (incremental load test for full squat and bench press, jump tests, incremental running test, and Wingate test) that were used as potential predictors of CrossFit performance. Thereafter, they performed the five Workouts of the Day (WODs) corresponding to the CrossFit Games Open 2019, and the relationship between each variable and CrossFit performance was analyzed. Overall Crossfit performance (i.e., final ranking considering all WODs) was significantly related to jump ability, mean and peak power output during the Wingate test, relative maximum strength for the full squat and the bench press, and maximum oxygen uptake and maximum speed during an incremental running test (all p<0.05, r=0.58-0.75), although the relationship of most markers varied depending on the analyzed WOD. Multiple linear regression analysis showed that the combination of maximum oxygen uptake, squat jump ability, and reactive strength index accounted for 81% of the variance in overall CrossFit performance (p=0.0003). CrossFit performance seems dependent on a variety of power-, strength-, and aerobic-related markers, which reflects the complexity of this sport. Improvements in aerobic capacity may help people and athletes in CrossFit performance and well-being. Also, focus on lower body power could be the key to obtain better performance markers.