Auto detecting deliveries in elite cricket fast bowlers using microsensors and machine learning

An investigation of an athlete injury likelihood monitoring system using the random forest algorithm and DWT

BACKGROUND

The main goal of sports science is to monitor sports injuries. Nevertheless, the existing sports injury monitoring projects have many expensive instruments and excessively extended monitoring periods, which makes it difficult to expand sports injury monitoring on a large scale.

OBJECTIVE

The advancement of machine learning algorithms opens up new avenues for the tracking of sports injuries.

METHODS

A training set of sports injuries was created using the Discrete Wavelet Transform (DWT) and Random Forest algorithms. Next, a basic analytic framework was created based on the lower-body movement of runners, and an athlete's injury likelihood monitoring system was established. First off, the wearable gyroscope device can efficiently plot the motion displacement curve and monitor the three-dimensional mechanics of the athlete's hips, thighs, and calves. Secondly, the system has a higher computational efficiency and an advantage over other classifier-based systems in terms of testing and training times.

RESULTS

The suggested system framework identifies athletes' injury propensity, providing preventive recommendations based on displacement curves, and offering a low total cost and high testing accuracy, making it easy to implement and cost-effective.

CONCLUSION

All things considered, the sports injury monitoring device is very accurate and reasonably priced, making it appropriate for widespread use.

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).

Information communication and technology in sports: a meticulous review

Introduction

Sports of all kinds even though have an alluring property of keeping their onlookers stuck to their place, the introduction of Technology, however, revolutionized it all together. Not only in legal sports but also the training and teaching methods have been reformed. The use of Information Communication and Technology (ICT) based technologies [Convolutional Neural Networks (CNN), Hawkeye, Computer vision, Artificial intelligence, etc.] has moderately increased the interactive nature of sports. Employing ICT-driven technologies have continuously been increasing performance levels due to which high effective performance levels have been achieved. In addition to offering information to the users, it also acts as a means for connecting and interacting with the remaining world. In this article, we provide a review of the studies considering the developments and impact of employing ICT technology on sports, especially cricket. The study has focussed on domain-specific developments in cricket sports: developments in the batting domain, bowling domain, and wicketkeeping as well.

Methods

For the study, the analysis has been done following the PRISMA guidelines.

Results

The study found that even though the researchers have done justifiable work in employing technology in sports as a whole but the domain-specific contribution in sports like cricket is not at the level as is need of the hour. In addition to the mentioned domains in the study, the research should gain speed in other domains like domain-specific Talent Identification for both genders, different age groups, diverse sports, etc.

Discussion

undoubtedly, the sports domain is employing technology at a vast level but a few domains like sports talent identification especially related to the most famous games like cricket require an immediate and intense focus of the researchers. Since this domain is still carrying out a traditional coach-oriented approach. There is an acute need to revolutionize the domain by incorporating modern technologies into it.

Prototype Machine Learning Algorithms from Wearable Technology to Detect Tennis Stroke and Movement Actions

This study evaluated the accuracy of tennis-specific stroke and movement event detection algorithms from a cervically mounted wearable sensor containing a triaxial accelerometer, gyroscope and magnetometer. Stroke and movement data from up to eight high-performance tennis players were captured in match-play and movement drills. Prototype algorithms classified stroke (i.e., forehand, backhand, serve) and movement (i.e., "Alert", "Dynamic", "Running", "Low Intensity") events. Manual coding evaluated stroke actions in three classes (i.e., forehand, backhand and serve), with additional descriptors of spin (e.g., slice). Movement data was classified according to the specific locomotion performed (e.g., lateral shuffling). The algorithm output for strokes were analysed against manual coding via absolute (n) and relative (%) error rates. Coded movements were grouped according to their frequency within the algorithm's four movement classifications. Highest stroke accuracy was evident for serves (98%), followed by groundstrokes (94%). Backhand slice events showed 74% accuracy, while volleys remained mostly undetected (41-44%). Tennis-specific footwork patterns were predominantly grouped as "Dynamic" (63% of total events), alongside successful linear "Running" classifications (74% of running events). Concurrent stroke and movement data from wearable sensors allows detailed and long-term monitoring of tennis training for coaches and players. Improvements in movement classification sensitivity using tennis-specific language appear warranted.

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 (η_p² = 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.

Validating an algorithm from a trunk-mounted wearable sensor for detecting stroke events in tennis

This study analysed the accuracy of a prototype algorithm for tennis stroke detection from wearable technology. Strokes from junior-elite tennis players over 10 matches were analysed. Players wore a GPS unit containing an accelerometer, gyroscope and magnetometer. Manufacturer-developed algorithms determined stoke type and count (forehands, backhands, serves and other). Matches were video recorded to manually code ball contacts and shadow swing events for forehands, backhands and serves and further by stroke classifications (i.e., drive, volley, slice, end-range). Comparisons between algorithm and coding were analysed via ANOVA and Bland-Altman plots at the match-level and error rates for specific stroke-types. No significant differences existed for stroke count between the algorithm and manual coding (p > 0.05). Significant (p < 0.0001) overestimation of "Other" strokes were observed from the algorithm, with no difference in groundstrokes and serves (p > 0.05). Serves had the highest accuracy of all stroke types (≥98%). Forehand and backhand "drives" were the most accurate (>86%), with volleys mostly undetected (58-60%) and slices and end-range strokes likely misclassified (49-51%). The prototype algorithm accurately quantifies serves and forehand and backhand "drives" and serves. However, underestimations of shadow swings and overestimations of "other" strokes suggests strokes with reduced trunk rotation have poorer detection accuracy.

Automatic detection of passing and shooting in water polo using machine learning: a feasibility study

There is currently no efficient way to quantify overhead throwing volume in water polo. Therefore, this study aimed to test the feasibility of a method to detect passes and shots in water polo automatically using inertial measurement units (IMU) and machine-learning algorithms. Eight water polo players wore one IMU sensor on the wrist (dominant hand) and one on the sacrum during six practices each. Sessions were filmed with a video camera and manually tagged for individual shots or passes. Data were synchronised between video tagging and IMU sensors using a cross-correlation approach. Support vector machine (SVM) and artificial neural networks (ANN) were compared based on sensitivity and specificity for identifying shots and passes. A total of 7294 actions were identified during the training sessions, including 945 shots and 5361 passes. Using SVM, passes and shots together were identified with 94.4% (95%CI = 91.8-96.4) sensitivity and 93.6% (95%CI = 91.4-95.4) specificity. Using ANN yielded similar sensitivity (93.0% [95%CI = 90.1-95.1]) and specificity (93.4% [95%CI = 91.1 = 95.2]). The results suggest that this method of identifying overhead throwing motions with IMU has potential for future field applications. A set-up with one single sensor at the wrist can suffice to measure these activities in water polo.

Objective assessment of fast bowling delivery intensity in amateur male cricketers

Wearable microtechnology is effective in detecting fast deliveries in cricket, however methods to quantify delivery intensity have not been established. This study aimed to investigate the utility of wearable sensors in quantifying cricket fast bowling intensity.Fifteen sub-elite male fast bowlers performed deliveries at warm-up, match, and maximal intensities. A principal component analysis resulted in the selection of perceived exertion and seven variables of bowling exertion derived from trunk- (PlayerLoad™, trunk flexion velocity, trunk forward rotation velocity) and tibia-mounted (tibial acceleration at back foot contact, front foot contact, back foot re-contact and front foot re-contact) inertial measurement units for further analysis. Repeated measures ANOVAs were used to investigate the effect of intensity on outcome variables. Significant main effects of intensity and large effect sizes were identified for all variables (p < .05, n_p² > 0.14). Measures from the match and maximal conditions were significantly larger compared with the warm-up condition (P_holm < .05). No differences were observed between the match and maximal conditions (p > .05). Inertial measurement metrics can distinguish between a warm-up effort and both match and maximal fast bowling delivery intensity. These devices provide a unique, time-efficient approach to cricket fast bowling exertion quantification.

External workload intensity in cricket fast bowlers across maximal and submaximal intensities: Modifying PlayerLoad and IMU location

Workload is a commonly accepted risk factor for injury among fast bowlers, however many methods exist to characterise workload. Recently, automated intensity-sensitive measures like PlayerLoad have been used to improve the estimation of workload in fast bowlers. The purpose of this study was to determine whether similar variables could be extracted from a single inertial measurement unit (IMU) that highly correlate with intensity, according to release speed. Eight elite and pre-elite bowlers participated in the study, with each bowler bowling one over each at 60%, 80% and 100% intensity and repeating this across two sessions (36 balls per participant). IMUs were placed on the upper-back and non-bowling wrist and maximum PlayerLoad from each delivery (PL_max) was compared to the accumulated value across each delivery (PL_acc). The strongest correlation with release speed was with PL_acc from the non-bowling wrist (R = 0.74), followed by PL_acc from the upper-back (R = 0.65) and PL_max from the upper back (R = 0.60). Consequently, an improved estimation of the intensity at which bowlers are working at could be gained by examining accumulated PlayerLoad values from an IMU on the non-bowling wrist.

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.

Quantification of the demands of cricket bowling and the relationship to injury risk: a systematic review

BACKGROUND

Bowling in cricket is a complex sporting movement which, despite being well characterised, still produces a significant number of injuries each year. Fast bowlers are more likely to be injured than any other playing role. Frequency, duration, intensity and volume of bowling, which have been generalised as measurements of workload, are thought to be risk factors for injuries. Injury rates of fast bowlers have not reduced in recent years despite the implementation of various workload monitoring practices.

OBJECTIVE

To identify the variables used to quantify frequency, intensity, time and volume of bowling; and evaluate relationships between these variables and injury risk.

METHODS

Six online databases were systematically searched for studies on fast bowling that included terms related to workload. Population characteristics, variables relating to demand and their relationship to standardised definitions of physical activity were extracted from all included studies.

RESULTS

Bowling workload is typically quantified through measures of frequency, duration, or indirect intensity, with few studies reporting on bowling volume.

CONCLUSIONS

When reported on, volume was often described using imprecise or insufficient measures of intensity. There is a need to develop more appropriate measures of intensity during bowling and improve the quality of evidence to inform on bowling programme management practices.

Misuse of the term 'load' in sport and exercise science

Despite the International System of Units (SI), as well as several publications guiding researchers on correct use of terminology, there continues to be widespread misuse of mechanical terms such as 'work' in sport and exercise science. A growing concern is the misuse of the term 'load'. Terms such as 'training load' and 'PlayerLoad' are popular in sport and exercise science vernacular. However, a 'load' is a mechanical variable which, when used appropriately, describes a force and therefore should be accompanied with the SI-derived unit of the newton (N). It is tempting to accept popular terms and nomenclature as scientific. However, scientists are obliged to abide by the SI and must pay close attention to scientific constructs. This communication presents a critical reflection on the use of the term 'load' in sport and exercise science. We present ways in which the use of this term breaches principles of science and provide practical solutions for ongoing use in research and practice.

Can Machine Learning with IMUs Be Used to Detect Different Throws and Estimate Ball Velocity in Team Handball?

Injuries in handball are common due to the repetitive demands of overhead throws at high velocities. Monitoring workload is crucial for understanding these demands and improving injury-prevention strategies. However, in handball, it is challenging to monitor throwing workload due to the difficulty of counting the number, intensity, and type of throws during training and competition. The aim of this study was to investigate if an inertial measurement unit (IMU) and machine learning (ML) techniques could be used to detect different types of team handball throws and predict ball velocity. Seventeen players performed several throws with different wind-up (circular and whip-like) and approach types (standing, running, and jumping) while wearing an IMU on their wrist. Ball velocity was measured using a radar gun. ML models predicted peak ball velocity with an error of 1.10 m/s and classified approach type and throw type with 80-87% accuracy. Using IMUs and ML models may offer a practical and automated method for quantifying throw counts and classifying the throw and approach types adopted by handball players.

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 and physical demands of international seam bowlers in one-day and Twenty20 international matches across five years

OBJECTIVES

To quantify and compare the match demands and variability of international One-Day (ODI) with Twenty20 (T20) cricket matches and to compare ODI match demands when competing home and away.

DESIGN

Single cohort, longitudinal observation.

METHODS

Thirteen international male seam bowlers across 204matches (ODI=160; T20=44) were investigated over five-years (2015-2019). Using global positioning sensors and accelerometers, physical demands were quantified using distance covered at different velocities and the number of entries into high and low intensity acceleration and deceleration bands. Variability was quantified using coefficient of variation (CV) and smallest worthwhile change.

RESULTS

Significantly greater (p<0.05) match demands were found for all physical variables relative to minutes played for T20 against ODI matches, except for distance covered 20-25kmh^-1 which was greater for ODI. Distance covered between 0-7km∙h^-1 showed no significance difference (p=0.60). The number of moderate decelerations (2-4m∙s²) were greater (p=0.04) away compared to home in ODI. All other variables showed no significance. Relative to minutes played, decelerations ≤4m∙s² (within-player ODI CV=75.5%. T20=72.0%) accelerations >4m∙s² (within-player ODI CV=79.2%. T20 CV=77.2%. Between-player ODI CV=84.7%. T20=38.8%) and distance covered >25kmh^-1 (within-player ODI CV=65.5%. T20=64.1%) showed the greatest variability.

CONCLUSIONS

Players are exposed to different physical demands in ODI Vs T20 matches, but not for home Vs away ODI matches. Practitioners should be aware of the large variability in high-speed/intensity accelerations and decelerations across matches.

Biomechanical risk factors of lower back pain in cricket fast bowlers using inertial measurement units: a prospective and retrospective investigation

Objectives

To investigate spinal kinematics, tibial and sacral impacts during fast bowling, among bowlers with a history of low back pain (LBP) (retrospective) and bowlers who developed LBP in the follow-up season (prospective).

Methods

35 elite male fast bowlers; senior (n=14; age=24.1±4.3 years; height=1.89±0.05 m; weight=89.2±4.6 kg) and junior (n=21; age=16.9±0.7; height=1.81±0.05; weight=73.0±9.2 kg) were recruited from professional county cricket clubs. LBP history was gathered by questionnaire and development of LBP was monitored for the follow-up season. Spinal kinematics, tibial and sacral impacts were captured using inertial measurement units placed over S1, L1, T1 and anteromedial tibia. Bonferroni corrected pairwise comparisons and effect sizes were calculated to investigate differences in retrospective and prospective LBP groups.

Results

Approximately 38% of juniors (n=8) and 57% of seniors (n=8) reported a history of LBP. No differences were evident in spinal kinematics or impacts between those with LBP history and those without for seniors and juniors. Large effect sizes suggest greater rotation during wind-up (d=1.3) and faster time-to-peak tibial impacts (d=1.5) in those with no history of LBP. One junior (5%) and four (29%) seniors developed LBP. No differences were evident in spinal kinematics or impacts between those who developed LBP and those who did not for seniors. In seniors, those who developed LBP had lower tibial impacts (d=1.3) and greater lumbar extension (d=1.9) during delivery.

Conclusion

Retrospective analysis displayed non-significant differences in kinematics and impacts. It is unclear if these are adaptive or impairments. Prospective analysis demonstrated large effect sizes for lumbar extension during bowling suggesting a target for future coaching interventions.