Can an inertial measurement unit, combined with machine learning, accurately measure ground reaction forces in cricket fast bowling?

This study examined whether an inertial measurement unit (IMU) could measure ground reaction force (GRF) during a cricket fast bowling delivery. Eighteen male fast bowlers had IMUs attached to their upper back and bowling wrist. Each participant bowled 36 deliveries, split into three different intensity zones: low = 70% of maximum perceived bowling effort, medium = 85%, and high = 100%. A force plate was embedded into the bowling crease to measure the ground truth GRF. Three machine learning models were used to estimate GRF from the IMU data. The best results from all models showed a mean absolute percentage error of 22.1% body weights (BW) for vertical and horizontal peak force, 24.1% for vertical impulse, 32.6% and 33.6% for vertical and horizontal loading rates, respectively. The linear support vector machine model had the most consistent results. Although results were similar to other papers that have estimated GRF, the error would likely prevent its use in individual monitoring. However, due to the large differences in raw GRFs between participants, researchers may be able to help identify links among GRF, injury, and performance by categorising values into levels (i.e., low and high).

Lower-limb wearable resistance overloads joint angular velocity during early acceleration sprint running.

Lower-limb wearable resistance (WR) facilitates targeted resistance-based training during sports-specific movement tasks. The purpose of this study was to determine the effect of two different WR placements (thigh and shank) on joint kinematics during the acceleration phase of sprint running. Eighteen participants completed maximal effort sprints while unloaded and with 2% body mass thigh- or shank-placed WR. The main findings were as follows: 1) the increase to 10 m sprint time was small with thigh WR (effect size [ES] = 0.24), and with shank WR, the increase was also small but significant (ES = 0.33); 2) significant differences in peak joint angles between the unloaded and WR conditions were small (ES = 0.23-0.38), limited to the hip and knee joints, and <2° on average; 3) aside from peak hip flexion angles, no clear trends were observed in individual difference scores; and, 4) thigh and shank WR produced similar reductions in average hip flexion and extension angular velocities. The significant overload to hip flexion and extension velocity with both thigh- and shank-placed WR may be beneficial to target the flexion and extension actions associated with fast sprint running.

The relationship between bowling intensity and ground reaction force in cricket pace bowlers.

This study examined the relationship between perceived bowling intensity, ball release speed and ground reaction force (measured by peak force, impulse and loading rate) in male pace bowlers. Twenty participants each bowled 36 deliveries, split evenly across three perceived intensity zones: low = 70% of maximum perceived bowling effort, medium = 85%, and high = 100%. Peak force and loading rate were significantly different across the three perceived intensity zones in the horizontal and vertical directions (Cohen's d range = 0.14-0.45, p < 0.01). When ball release speed increased, peak force and loading rate also increased in the horizontal and vertical directions (ηp2 = 0.04-0.18, p < 0.01). Lastly, bowling at submaximal intensities (i.e., low - medium) was associated with larger decreases in peak horizontal force (7.9-12.3% decrease), impulse (15.8-21.4%) and loading rate (7.4-12.7%) compared to decreases in ball release speed (5.4-8.3%). This may have implications for bowling strategies implemented during training and matches, particularly for preserving energy and reducing injury risk.

The effects of curve registration on linear models of jump performance and classification based on vertical ground reaction forces.

Functional principal components define modes of variation in time series, which represent characteristic movement patterns in biomechanical data. Their usefulness however depends on the prior choices made in data processing. Recent research showed that better curve alignment achieved with registration (dynamic time warping) reduces errors in linear models predicting jump height. However, the efficacy of registration in different preprocessing combinations, including time normalisation, padding and feature extraction, is largely unknown. A more comprehensive analysis is needed, given the potential value of registration to machine learning in biomechanics. We evaluated popular preprocessing methods combined with registration, creating 512 models based on ground reaction force data from 385 countermovement jumps. The models either predicted jump height or classified jumps into those performed with or without arm swing. Our results show that the classification models benefited from registration in various forms, particularly when landmarks were placed at critical points. The best classifier achieved a 5.5 percentage point improvement over the equivalent unregistered model. However, registration was detrimental to the jump height models, although this performance variable may be a special case given its direct relationship with impulse. Our meta-models revealed the relative contributions made by various preprocessing operations, highlighting that registration does not generalise so well to new data. Nonetheless, our analysis shows the potential for registration in further biomechanical applications, particularly in classification, when combined with the other appropriate preprocessing operations.

Determining jumping performance from a single body-worn accelerometer using machine learning.

External peak power in the countermovement jump is frequently used to monitor athlete training. The gold standard method uses force platforms, but they are unsuitable for field-based testing. However, alternatives based on jump flight time or Newtonian methods applied to inertial sensor data have not been sufficiently accurate for athlete monitoring. Instead, we developed a machine learning model based on characteristic features (functional principal components) extracted from a single body-worn accelerometer. Data were collected from 69 male and female athletes at recreational, club or national levels, who performed 696 jumps in total. We considered vertical countermovement jumps (with and without arm swing), sensor anatomical locations, machine learning models and whether to use resultant or triaxial signals. Using a novel surrogate model optimisation procedure, we obtained the lowest errors with a support vector machine when using the resultant signal from a lower back sensor in jumps without arm swing. This model had a peak power RMSE of 2.3 W·kg-1 (5.1% of the mean), estimated using nested cross validation and supported by an independent holdout test (2.0 W·kg-1). This error is lower than in previous studies, although it is not yet sufficiently accurate for a field-based method. Our results demonstrate that functional data representations work well in machine learning by reducing model complexity in applications where signals are aligned in time. Our optimisation procedure also was shown to be robust can be used in wider applications with low-cost, noisy objective functions.

Variability of Multiangle Isometric Force-Time Characteristics in Trained Men.

ABSTRACT: Oranchuk, DJ, Storey, AG, Nelson, AR, Neville, JG, and Cronin, JB. Variability of multiangle isometric force-time characteristics in trained men. J Strength Cond Res 36(1): 284-288, 2022-Measurements of isometric force, rate of force development (RFD), and impulse are widely reported. However, little is known about the variability and reliability of these measurements at multiple angles, over repeated testing occasions in a homogenous, resistance-trained population. Thus, understanding the intersession variability of multiangle isometric force-time characteristics provides the purpose of this article. Three sessions of isometric knee extensions at 40°, 70°, and 100° of flexion were performed by 26 subjects across 51 limbs. All assessments were repeated on 3 occasions separated by 5-8 days. Variability was qualified by doubling the typical error of measurement (TEM), with thresholds of 0.2-0.6 (small), 0.6-1.2 (moderate), 1.2-2.0 (large), 2.0-4.0 (very large), and >4.0 (extremely large). In addition, variability was deemed large when the intraclass correlation coefficient (ICC) was <0.67 and coefficient of variation (CV) >10%; moderate when ICC >0.67 or CV <10% (but not both); and small when both ICC >0.67 and CV <10%. Small to moderate between-session variability (ICC = 0.68-0.95, CV = 5.2-18.7%, TEM = 0.24-0.49) was associated with isometric peak force, regardless of angle. Moderate to large variability was seen in early-stage (0-50 ms) RFD and impulse (ICC = 0.60-0.80, CV = 22.4-63.1%, TEM = 0.62-0.74). Impulse and RFD at 0-100 ms, 0-200 ms, and 100-200 ms were moderately variable (ICC = 0.71-0.89, CV = 11.8-42.1%, TEM = 0.38-0.60) at all joint angles. Isometric peak force and late-stage isometric RFD and impulse measurements were found to have low intersession variability regardless of joint angle. However, practitioners need to exercise caution when making inferences about early-stage RFD and impulse measures due to moderate-large variability.

Quantifying cricket fast bowling volume, speed and perceived intensity zone using an Apple Watch and machine learning.

This study examined whether an inertial measurement unit (IMU) and machine learning models could accurately measure bowling volume (BV), ball release speed (BRS), and perceived intensity zone (PIZ). Forty-four male pace bowlers wore a high measurement range, research-grade IMU (SABELSense) and a consumer-grade IMU (Apple Watch) on both wrists. Each participant bowled 36 deliveries, split into two different PIZs (Zone 1 = 70-85% of maximum bowling effort, Zone 2 = 100% of maximum bowling effort). BRS was measured using a radar gun. Four machine learning models were compared. Gradient boosting models had the best results across all measures (BV: F-score = 1.0; BRS: Mean absolute error = 2.76 km/h; PIZ: F-score = 0.92). There was no significant difference between the SABELSense and Apple Watch on the same hand when measuring BV, BRS, and PIZ. A significant improvement in classifying PIZ was observed for IMUs located on the dominant wrist. For all measures, there was no added benefit of combining IMUs on the dominant and non-dominant wrists.

Can an inertial measurement unit (IMU) in combination with machine learning measure fast bowling speed and perceived intensity in cricket?

This study examined whether an inertial measurement unit (IMU), in combination with machine learning, could accurately predict two indirect measures of bowling intensity through ball release speed (BRS) and perceived intensity zone (PIZ). One IMU was attached to the thoracic back of 44 fast bowlers. Each participant bowled 36 deliveries at two different PIZ zones (Zone 1 = 24 deliveries at 70% to 85% of maximum perceived bowling effort; Zone 2 = 12 deliveries at 100% of maximum perceived bowling effort) in a random order. IMU data (sampling rate = 250 Hz) were downsampled to 125 Hz, 50 Hz, and 25 Hz to determine if model accuracy was affected by the sampling frequency. Data were analysed using four machine learning models. A two-way repeated-measures ANOVA was used to compare the mean absolute error (MAE) and accuracy scores (separately) across the four models and four sampling frequencies. Gradient boosting models were shown to be the most consistent at measuring BRS (MAE = 3.61 km/h) and PIZ (F-score = 88%) across all sampling frequencies. This method could be used to measure BRS and PIZ which may contribute to a better understanding of overall bowling load which may help to reduce injuries.

Variability of concentric angle-specific isokinetic torque and impulse assessments of the knee extensors.

OBJECTIVE: Length-tension relationships are widely reported in research, rehabilitation and performance settings; however, several isometric contractions at numerous angles are needed to understand these muscular outputs. Perhaps a more efficient way to determine torque-angle characteristics is via isokinetic dynamometry; however, little is known about the variability of isokinetic measurements besides peak torque and optimal angle. This paper examines the variability of angle-specific isokinetic torque and impulse measures. APPROACH: Three sessions of concentric (60°·s-1) knee extensions were performed by both limbs of 32 participants. Assessments were repeated on three occasions, separated by 5-8 d. To quantify variability, the standardized typical error of measurement (TEM) was doubled and thresholds of 0.2-0.6 (small), 0.6-1.2 (moderate), 1.2-2.0 (large), 2.0-4.0 (very large) and >4.0 (extremely large) were applied. Additionally, variability was deemed large when the intraclass correlation coefficient (ICC) was <0.67 and coefficient of variation (CV) > 10%; moderate when ICC > 0.67 or CV < 10% (but not both); and small when both ICC > 0.67 and CV < 10%. MAIN RESULTS: Isokinetic torque and angular impulse show small to medium variability (ICC = 0.75-0.96, CV = 6.4%-15.3%, TEM = 0.25-0.53) across all but the longest (100°) and shortest (10°) muscle lengths evaluated. However, moderate to large variability was found for the optimal angle (ICC = 0.58-0.64, CV = 7.3%-8%, TEM = 0.76-0.86), and torque and impulse at the beginning and end of the range of motion (ICC = 0.57-0.85, CV = 11-42.9%, TEM = 0.40-0.89). Intersession variability of isokinetic torque and impulse were small to moderate at medium (90-20°) joint angles. SIGNIFICANCE: Researchers and practitioners can examine the muscle torque-angle relationship and activity-specific torque outputs within these ranges, without resorting to more strenuous and time-consuming isometric evaluations.

Inertial measurement unit-based hip flexion test as an indicator of sprint performance.

This study aimed 1) to examine the validity of inertial measurement unit (IMU)-based hip flexion strength test, and 2) to investigate the hip flexion strength test as an indicator of sprint performance. Eight males performed five repeated hip flexion-extension, while leg motion was recorded using an IMU and a motion capture system (Mocap). As the second experiment, 24 male athletes performed the IMU-based hip flexion strength test and sprinted 50 m, during which step-to-step ground reaction force (GRF) was recorded. The strength test variables were calculated using IMU and Mocap data including angular impulse, mean moment, and positive and negative work and power. Using GRF data, step-to-step spatiotemporal variables were obtained. The results showed high intra-class correlation coefficient and correlation coefficient (both >0.909) between IMU and Mocap for angular impulse, mean moment, positive work and power. The hip flexion mean moment showed significant correlation with running speed from the 5th-8th step section onwards. The angular impulse, mean moment, positive work and power are recommended to be used for the IMU-based hip flexion strength test variables in terms of accuracy and validity. Moreover, the proposed IMU-based hip flexion strength test can be an indicator for better sprinting performance.

Cricket fast bowling detection in a training setting using an inertial measurement unit and machine learning.

Fast bowlers are at a high risk of overuse injuries. There are specific bowling frequency ranges known to have negative or protective effects on fast bowlers. Inertial measurement units (IMUs) can classify movements in sports, however, some commercial products can be too expensive for the amateur athlete. As a large number of the world's population has access to an IMU (e.g. smartphones), a system that works on a range of different IMUs may increase the accessibility of automated workload monitoring in sport. Seventeen elite fast bowlers in a training setting were used to train and/or validate five machine learning models by bowling and performing fielding drills. The accuracy of machine learning models trained using data from all three bowling phases (pre-delivery, delivery and post-delivery) were compared to those trained using only the delivery phase at a sampling rate of 250 Hz. Next, models were trained using data down-sampled to 125 Hz, 50 Hz, and 25 Hz to mimic results from lower specification sensors. Models trained using only the delivery phase showed similar accuracy (> 95%) to those trained using all three bowling phases. When delivery-phase data were down-sampled, the accuracy was maintained across all models and sampling frequencies (>96%).

A Dual-Accelerometer System for Classifying Physical Activity in Children and Adults.

INTRODUCTION: Accurately monitoring 24-h movement behaviors is a vital step for progressing the time-use epidemiology field. Past accelerometer-based measurement protocols are either hindered by lack of wear time compliance, or the inability to accurately discern activities and postures. Recent work has indicated that skin-attached dual-accelerometers exhibit excellent 24-h uninterrupted wear time compliance. This study extends this work by validating this system for classifying various physical activities and sedentary behaviors in children and adults. METHODS: Seventy-five participants (42 children) were equipped with two Axivity AX3 accelerometers; one attached to their thigh, and one to their lower back. Ten activity trials (e.g., sitting, standing, lying, walking, running) were performed while under direct observation in a lab setting. Various time- and frequency-domain features were computed from raw accelerometer data, which were then used to train a random forest machine learning classifier. Model performance was evaluated using leave-one-out cross-validation. The efficacy of the dual-sensor protocol (relative to single sensors) was evaluated by repeating the modeling process with each sensor individually. RESULTS: Machine learning models were able to differentiate between six distinct activity classes with exceptionally high accuracy in both adults (99.1%) and children (97.3%). When a single thigh or back accelerometer was used, there was a pronounced drop in accuracy for nonambulatory activities (up to a 26.4% decline). When examining the features used for model training, those that took the orientation of both sensors into account concurrently were more important predictors. CONCLUSIONS: When previous wear time compliance results are taken together with our findings, it represents a promising step forward for monitoring and understanding 24-h time-use behaviors. The next step will be to examine the generalizability of these findings in a free-living setting.