Analytics in Sports Medicine: Implications and Responsibilities That Accompany the Era of Big Data

Who are the best passing players in professional soccer? A machine learning approach for classifying passes with different levels of difficulty and discriminating the best passing players

The present study aimed to assess the use of technical-tactical variables and machine learning (ML) classifiers in the automatic classification of the passing difficulty (DP) level in soccer matches and to illustrate the use of the model with the best performance to distinguish the best passing players. We compared eight ML classifiers according to their accuracy performance in classifying passing events using 35 technical-tactical variables based on spatiotemporal data. The Support Vector Machine (SVM) algorithm achieved a balanced accuracy of 0.70 ± 0.04%, considering a multi-class classification. Next, we illustrate the use of the best-performing classifier in the assessment of players. In our study, 2,522 pass actions were classified by the SVM algorithm as low (53.9%), medium (23.6%), and high difficulty passes (22.5%). Furthermore, we used successful rates in low-DP, medium-DP, and high-DP as inputs for principal component analysis (PCA). The first principal component (PC1) showed a higher correlation with high-DP (0.80), followed by medium-DP (0.73), and low-DP accuracy (0.24). The PC1 scores were used to rank the best passing players. This information can be a very rich performance indication by ranking the best passing players and teams and can be applied in offensive sequences analysis and talent identification.

The Fundamentals and Applications of Wearable Sensor Devices in Sports Medicine: A Scoping Review

PURPOSE

To (1) characterize the various forms of wearable sensor devices (WSDs) and (2) review the peer-reviewed literature of applied wearable technology within sports medicine.

METHODS

A systematic search of PubMed and EMBASE databases, from inception through 2023, was conducted to identify eligible studies using WSDs within sports medicine. Data extraction was performed of study demographics and sensor specifications. Included studies were categorized by application: athletic training, rehabilitation, and research.

RESULTS

In total, 43 studies met criteria for inclusion in this review. Forms of WSDs include pedometers, accelerometers, encoders (consisting of magnetometers and gyroscopes), force sensors, global positioning system trackers, and inertial measurement units. Outcome metrics include step counts; gait, limb motion, and angular positioning; foot and skin pressure; change of direction and inclination, including analysis of both body parts and athletes on a field; displacement and velocity of body segments and joints; heart rate; plethysmography; sport-specific kinematics; range of motion, symmetry, and alignment; head impact; sleep; throwing biomechanics; and kinetic and spatiotemporal running metrics. WSDs are used in athletic training to assess sport-specific biomechanics and workload with a goal of injury prevention and training optimization, as well as for rehabilitation monitoring and research such as for risk predicting and aiding diagnosis.

CONCLUSIONS

WSDs enable real-time monitoring of human performance across a variety of implementations and settings, allowing collection of metrics otherwise not achievable. WSDs are powerful tools with multiple applications within athletic training, patient rehabilitation, and orthopaedic and sports medicine research.

CLINICAL RELEVANCE

Wearable technology may represent the missing link to quantitatively addressing return to play and previous performance. WSDs are commercially available and portable adjuncts that allow clinicians, trainers, and individual athletes to monitor biomechanical parameters, workload, and recovery status to better contextualize personalized training, injury risk, and rehabilitation.

BAYESIAN HIERARCHICAL MODELING AND ANALYSIS FOR ACTIGRAPH DATA FROM WEARABLE DEVICES

The majority of Americans fail to achieve recommended levels of physical activity, which leads to numerous preventable health problems such as diabetes, hypertension, and heart diseases. This has generated substantial interest in monitoring human activity to gear interventions toward environmental features that may relate to higher physical activity. Wearable devices, such as wrist-worn sensors that monitor gross motor activity (actigraph units) continuously record the activity levels of a subject, producing massive amounts of high-resolution measurements. Analyzing actigraph data needs to account for spatial and temporal information on trajectories or paths traversed by subjects wearing such devices. Inferential objectives include estimating a subject's physical activity levels along a given trajectory; identifying trajectories that are more likely to produce higher levels of physical activity for a given subject; and predicting expected levels of physical activity in any proposed new trajectory for a given set of health attributes. Here, we devise a Bayesian hierarchical modeling framework for spatial-temporal actigraphy data to deliver fully model-based inference on trajectories while accounting for subject-level health attributes and spatial-temporal dependencies. We undertake a comprehensive analysis of an original dataset from the Physical Activity through Sustainable Transport Approaches in Los Angeles (PASTA-LA) study to ascertain spatial zones and trajectories exhibiting significantly higher levels of physical activity while accounting for various sources of heterogeneity.

Training patterns associated with injury in New York City Marathon runners

OBJECTIVE

Training patterns are commonly implicated in running injuries. The purpose of this study was to measure the incidence of injury and illness among marathon runners and the association of injuries with training patterns and workload.

METHODS

Runners registered for the New York City Marathon were eligible to enrol and prospectively monitored during the 16 weeks before the marathon, divided into 4-week 'training quarters' (TQ) numbered TQ1-TQ4. Training runs were tracked using Strava, a web and mobile platform for tracking exercise. Runners were surveyed at the end of each TQ on injury and illness, and to verify all training runs were recorded. Acute:chronic workload ratio (ACWR) was calculated by dividing the running distance in the past 7 days by the running distance in the past 28 days and analysed using ratio thresholds of 1.3 and 1.5.

RESULTS

A total of 735 runners participated, mean age 41.0 (SD 10.7) and 46.0% female. Runners tracked 49 195 training runs. The incidence of injury during training was 40.0% (294/735), and the incidence of injury during or immediately after the marathon was 16.0% (112/699). The incidence of illness during training was 27.2% (200/735). Those reporting an initial injury during TQ3 averaged less distance/week during TQ2 compared with uninjured runners, 27.7 vs 31.9 miles/week (p=0.018). Runners reporting an initial injury during TQ1 had more days when the ACWR during TQ1 was ≥1.5 compared with uninjured runners (injured IQR (0-3) days vs uninjured (0-1) days, p=0.009). Multivariable logistic regression for training injuries found an association with the number of days when the ACWR was ≥1.5 (OR 1.06, 95% CI (1.02 to 1.10), p=0.002).

CONCLUSION

Increases in training volume ≥1.5 ACWR were associated with more injuries among runners training for a marathon. These findings can inform training recommendations and injury prevention programmes for distance runners.

[Artificial intelligence and novel approaches for treatment of non-union in bone : From established standard methods in medicine up to novel fields of research]

Methods of artificial intelligence (AI) have found applications in many fields of medicine within the last few years. Some disciplines already use these methods regularly within their clinical routine. However, the fields of application are wide and there are still many opportunities to apply these new AI concepts. This review article gives an insight into the history of AI and defines the special terms and fields, such as machine learning (ML), neural networks and deep learning. The classical steps in developing AI models are demonstrated here, as well as the iteration of data rectification and preparation, the training of a model and subsequent validation before transfer into a clinical setting are explained. Currently, musculoskeletal disciplines implement methods of ML and also neural networks, e.g. for identification of fractures or for classifications. Also, predictive models based on risk factor analysis for prevention of complications are being initiated. As non-union in bone is a rare but very complex disease with dramatic socioeconomic impact for the healthcare system, many open questions arise which could be better understood by using methods of AI in the future. New fields of research applying AI models range from predictive models and cost analysis to personalized treatment strategies.

Machine learning in orthopaedic surgery

Artificial intelligence and machine learning in orthopaedic surgery has gained mass interest over the last decade or so. In prior studies, researchers have demonstrated that machine learning in orthopaedics can be used for different applications such as fracture detection, bone tumor diagnosis, detecting hip implant mechanical loosening, and grading osteoarthritis. As time goes on, the utility of artificial intelligence and machine learning algorithms, such as deep learning, continues to grow and expand in orthopaedic surgery. The purpose of this review is to provide an understanding of the concepts of machine learning and a background of current and future orthopaedic applications of machine learning in risk assessment, outcomes assessment, imaging, and basic science fields. In most cases, machine learning has proven to be just as effective, if not more effective, than prior methods such as logistic regression in assessment and prediction. With the help of deep learning algorithms, such as artificial neural networks and convolutional neural networks, artificial intelligence in orthopaedics has been able to improve diagnostic accuracy and speed, flag the most critical and urgent patients for immediate attention, reduce the amount of human error, reduce the strain on medical professionals, and improve care. Because machine learning has shown diagnostic and prognostic uses in orthopaedic surgery, physicians should continue to research these techniques and be trained to use these methods effectively in order to improve orthopaedic treatment.

Technology Used in the Prevention and Treatment of Shoulder and Elbow Injuries in the Overhead Athlete

PURPOSE OF REVIEW

To review the current technology available for the prevention and treatment of shoulder and elbow injuries in the overhead athlete.

RECENT FINDINGS

Shoulder and elbow injuries are common in recreational and high-level overhead athletes. Injury prevention in these athletes include identifying modifiable risk factors, offering effective preventative training programs, and establishing safe return-to-sport criteria. The advent and use of technologies and wearable devices with concomitant development of software and data analytic programs has significantly changed the role of sports technology in injury identification and prevention. Over the last few decades, leveraging new technologies to better understand and treat patients has become an increasing focus of healthcare. Technologies currently being applied to the treatment of the overhead athlete include kinesiotaping, heart rate monitors, accelerometers/gyroscopes, dynamometers/force plates, camera-based monitoring systems (optical motion analysis), and inertial sensor monitoring units. Advances in technology have made it possible to acquire large amounts of data on athletes that may be used to guide treatment and injury prevention programs; however, literature validating the clinical efficacy of many of these technologies is limited. Further research is needed to continue to allow team physicians to provide better, cost-efficient, and individualized care to the overhead athlete using technology.

Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics

Orthopaedics as a surgical discipline requires a combination of good clinical acumen, good surgical skill, a reasonable physical strength and most of all, good understanding of technology. The last few decades have seen rapid adoption of new technologies into orthopaedic practice, power tools, new implants, CAD-CAM design, 3-D printing, additive manufacturing just to name a few. The new disruption in orthopaedics in the current time and era is undoubtedly the advent of artificial intelligence and robotics. As these technologies take root and innovative applications continue to be incorporated into the main-stream orthopedics, as we know it today, it is imperative to look at and understand the basics of artificial intelligence and what work is being done in the field today. This article takes the form of a loosely structured narrative review and will introduce the reader to key concepts in the field of artificial intelligence as well as some of the directions in application of the same in orthopaedics. Some of the recent work has been summarised and we present our viewpoint at the conclusion as to why we must consider artificial intelligence as a disrupting positive influence on orthopaedic surgery.

Wearable systems for shoulder kinematics assessment: a systematic review

BACKGROUND

Wearable sensors are acquiring more and more influence in diagnostic and rehabilitation field to assess motor abilities of people with neurological or musculoskeletal impairments. The aim of this systematic literature review is to analyze the wearable systems for monitoring shoulder kinematics and their applicability in clinical settings and rehabilitation.

METHODS

A comprehensive search of PubMed, Medline, Google Scholar and IEEE Xplore was performed and results were included up to July 2019. All studies concerning wearable sensors to assess shoulder kinematics were retrieved.

RESULTS

Seventy-three studies were included because they have fulfilled the inclusion criteria. The results showed that magneto and/or inertial sensors are the most used. Wearable sensors measuring upper limb and/or shoulder kinematics have been proposed to be applied in patients with different pathological conditions such as stroke, multiple sclerosis, osteoarthritis, rotator cuff tear. Sensors placement and method of attachment were broadly heterogeneous among the examined studies.

CONCLUSIONS

Wearable systems are a promising solution to provide quantitative and meaningful clinical information about progress in a rehabilitation pathway and to extrapolate meaningful parameters in the diagnosis of shoulder pathologies. There is a strong need for development of this novel technologies which undeniably serves in shoulder evaluation and therapy.