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Technology and Shoulder Injury Prevention and Rehabilitation

Original Editor - Angeliki Chorti Top Contributors - Angeliki Chorti, Malisha van der Berg, Alexandra Stead and Vidya Acharya

Introduction

The shoulder joint depends on muscular and connective tissue support for stability, leaving it susceptible to both sudden trauma and cumulative overload.[1][2] Shoulder injuries account for 4.35% of all injuries presented to emergency departments,[3] affecting athletes, occupational populations and wheelchair users, each characterised by a distinct biomechanical loading pattern.[1][4][5]

Faulty movement patterns typically develop well before any symptomatic pathology appears.[6] Thus, continuous evaluation of the shoulder is essential for prevention.[7] Traditional shoulder assessment tends to be episodic, only confirming shoulder injury after it has occurred rather than anticipating it and infrequent therapist-led sessions provides limited insight into shoulder movement between clinical appointments.[8]

Technologies such as wearable sensors, motion capture and artificial intelligence (AI)-driven analytical approaches offer continuous and objective monitoring not afforded by conventional clinical observations.[8] Enabling movement quality, loading and adherence to be monitored throughout the recovery process, enabling more individualised and responsive care.[9][10][11]

Types of Shoulder Injuries

Shoulder injuries are broadly categorised as either acute or chronic in onset.[1]

Acute Injuries

Acute injuries result from a discrete traumatic event, such as a fall or collision, and include:[1][12]

Chronic injuries

Chronic injuries develop from recurrent microtrauma over time and typically present as:[1][14]

Loading pattern behind shoulder injuries varies across populations and can produce either mechanism above.

Shoulder Injuries in Sports

Shoulder injuries in recreational or high-level athletes are quite common.[3]

Contact sports

Contact sports such as wrestling, lacrosse, rugby and American football expose the shoulder to direct trauma through tackling, grappling and collision.[6] This burden is substantial: shoulder injuries account for 99.1% of wrestling injuries and 71.5% of lacrosse injuries among the more than 1.3 million upper extremity sports injuries presented to US emergency departments between 2014 and 2023.[15]

Overhead sports

Overhead sports such as handball, volleyball, swimming and baseball load it repeatedly through throwing and stroke actions.[6][16]

Racquet, Throwing, Artistic and Extreme sports

Racquet and throwing sports (golf, tennis, handball and volleyball), individual and artistic sports (gymnastics, dancing and rowing) and other contact or extreme sports (judo, mixed martial arts, bodybuilding, weightlifting, motocross and downhill mountain biking), have their own injury pattern.[1] Most of this load is chronic but contact and extreme sports also carry a substantial acute injury burden through collision and impact.

Little league

Young baseball players (4–16 year old) are prone to an overuse shoulder injury known as "Little league shoulder", a proximal humeral growth plate stress fracture.[15]

Wheelchair sport

Wheelchair sport loads the shoulder as a functional weight-bearing joint rather than purely as a mobile limb. Reduced acromion-humeral distance and repeated subacromial compression are strongly linked to supraspinatus tendinosis, alongside notable glenohumeral and acromioclavicular osteoarthritis.[5] This chronic pattern reflects propulsion and transfer demand rather than a single sporting movement, though falls and collisions during play can also cause acute injury.[17]

Shoulder Injuries in Occupation and Lifestyle

Occupational and lifestyle loading is most often chronic, arising through repetitive overhead reaching, sustained poor posture and prolonged static loading.[4] It can also be acute, through manual handling incidents, falls or sudden overload during lifting or transfer tasks.

Nursing and other physically demanding occupations illustrate this well, with musculoskeletal disorder prevention technologies increasingly applied to address both patterns of work-related shoulder loading.[18]

Assessing the Shoulder

Before considering the technologies that capture, measure and interpret shoulder movement, it is first necessary to understand what normal movement looks like and what clinicians should be looking out for when assessing it.

Normal Pattern of Shoulder Movement

Normal shoulder function is not the product of a single joint but a coordinated interaction across the cervical spine, trunk and glenohumeral joint together.[11] See Shoulder page for more detail.

The shoulder complex comprises three bones (clavicle, scapula and humerus) and four joints (glenohumeral, acromioclavicular, sternoclavicular and scapulothoracic). The shoulder has three main degrees of freedom,[19] moving through three principal rotational planes:[11]

  • Sagittal plane: flexion/extension
  • Frontal/Coronal Plane: abduction/adduction
  • Transverse Plane: internal/external rotation and horizontal abduction/adduction

Scapulohumeral rhythm describes the coordinated ratio of glenohumeral to scapulothoracic movement during arm elevation and appropriate rotator cuff and scapular stabiliser activation is required to centre the humeral head throughout this arc.[1]

The balance between agonist and antagonist muscle groups is a further essential component of normal shoulder function, particularly for overhead athletes. The external rotation to internal rotation (ER:IR) strength ratio is commonly used to characterise this balance, with an ideal ratio of 66% widely cited.[20]

Proprioception is also important in keeping the humeral head centred throughout its range of motion. Deficits here may lead to reduced shoulder stability, altered motor control and, eventually, injury, pain and disability.[2]

Manual Evaluation

Historically, evaluation relied on goniometry, visual observation and manual muscle testing.[21] These methods are subject to inter-operator variability and subjectivity.[19] For example the reliability of manual muscle testing is highly dependent on the size and strength of the examiner, a limitation that does not apply to dynamometry.[20]

Manual evaluation also offer limited ability to capture speed, coordination and subtle compensatory strategies involved in dynamic shoulder movement.[21] That said, manual testing still retains genuine value for specific pathologies. A GRADE-appraised systematic review found high diagnostic accuracy for several clinical tests:[14]

Thus evaluation technologies should complement rather than replace clinical judgement, providing quantifiable, repeatable data alongside expert assessment.

Evolution of Technology Use in Shoulder Injury Prevention and Rehabilitation

The application of technology to shoulder care has developed considerably over recent decades and can be categorised in three broad phases:

Laboratory-based Motion Capture and Dynamometry

Early efforts centred on laboratory-based three-dimensional (3D) motion capture systems. These systems offered high accuracy but needed technical expertise and were confined to specialist research settings due to cost and space requirements.[22]

Isokinetic dynamometry emerged as the gold standard for measuring muscle strength and endurance, offering dynamic assessment capabilities that static methods such as manual muscle testing could not match.[20]

Wearable and Mobile Devices

Advances in microelectromechanical sensor technology, ensured that devices such as inertial measurement units (IMU’s) became smaller, cheaper and more accessible, allowing movement data to be captured on the sports field itself.[23]

The widespread availability of smartphones has driven a further shift towards decentralised assessment, placing monitoring capability directly in the hands of clinicians and athletes.[24] This same drive towards accessibility has extended to smart textiles: additive manufacturing techniques such as 3D printing now allow wearable sensors to be produced as low-cost, customisable alternatives to structured motion capture environments.[19]

Portable hand-held dynamometry for muscle strength assessment has followed a similar trajectory, offering a more accessible alternative to fixed-frame isokinetic systems, though this accessibility has come with trade-offs in reliability that are only recently being formally characterised.[2]

Artificial Intelligence Driven Multimodal Fusion

The most recent phase is the shift from single-sensor systems towards multimodal sensor fusion combined with artificial intelligence (AI). Rather than relying on one data stream, current research increasingly combines inertial, electromyographic, visual and physiological data.[22] This fusion underpins a shift within sport and occupational biomechanics from reactive treatment, applied after injury has occurred, towards proactive, predictive and individualised injury management.[25]

Looking ahead, emerging concepts such as explainable AI modelling and digital twin modelling (which simulates an individual athlete's recovery trajectory) aims to make model decisions interpretable to clinicians, though both remain largely developmental.[21]

Technology in Shoulder Injury Prevention

Motion Capture (Marker-Based and Markerless)

Motion capture of the shoulder involves the use of cameras.[26] This is a non-invasive system using optoelectronic motion analysis techniques to provide objective kinematic analytic methods that may convey useful range of motion and functional information for clinical practitioners.[22] [26]

3D Motion Capture Systems

3D motion capture systems track shoulder and scapular movement using either reflective markers placed on anatomical landmarks or, markerless video-based systems tracking anatomical landmarks only.[27][28] These systems remain the reference standard for kinematic accuracy but have traditionally been restricted to laboratory or specialist clinical environments.[22] The video below demonstrates motion capture using the Qualisys marker-based and markerless analysis system to determine baseball biomechanics in elite athletes:

[29]

2D Video Analysis

2D video analysis is used to asses shoulder motion in certain planes of movement, however, a key limitation is that joint angle calculations depend heavily on camera viewing angle. In a worked example, trunk flexion angle varied from close to 0° to 67° purely as a result of camera position.[30] This limitation has motivated recent development of AI-based motion capture, which extracts human movement from ordinary 2D video and matches it to an anthropometric model like OpenPose.[28][30] Video below demonstrates a single video camera utilising OpenPose for pose estimation of a fast paced swing dance routine outdoors:

[31]

Commercially available markerless systems

Commercially available markerless systems using computer vision and machine learning have also driven the ability to generate 3D postures from single-camera (2D) footage, technology originally developed for film and games production but now applied to ergonomic and sports assessment.[24] The following video is an example of how 2D video and pictures can be transformed into 3D animated avatars using DeepMotion software:

[32]

Inertial Measurement Units (IMUs)

IMUs combine accelerometers and gyroscopes, sometimes with magnetometers, to estimate body segment position and orientation over time.[23] When attached to the upper arm, scapula or forearm, IMUs can estimate joint angle and movement velocity in real time, making them suitable to capture shoulder movement outside the laboratory.[23][33]

A recognised limitation of this category is that fixed skin positioning is typically required, which can result in complex calibration procedures and wearing discomfort.[34] Several named commercial systems have nonetheless been validated specifically for shoulder measurement and are listed in the table below, with demonstration videos where available.

System Validation
Xsens MVN

[35]

Validated against optical motion capture for shoulder, elbow and wrist angles during tennis strokes, with a known limitation in shoulder anteroposterior plane accuracy.[33]
motusBASEBALL/motusTHROW

[36]

Validated in baseball pitchers with shoulder rotation RMSE of 0.8 to 3.3° against marker-based capture, and correlation coefficients up to r = 0.75 against optical motion capture in a separate validation study.[33]
Xsens MTw Awinda synchronised with Delsys Trigno surface EMG

[37]

Achieving angular accuracy below 1.2° following static T-pose calibration in a research-grade laboratory.[25]

Accelerometers and gyroscopes alone can be used for 3-axis movement analysis of the shoulder joint.[38] With the use of transducers and electrodes, some of these devices can analyse in a simple, user-friendly, non invasive and reproducible way and provide feedback on individual profiles, different phases, follow-up assessments and specific techniques in sport.[39] [40] [41] This information can be useful when introducing personalised coaching techniques and injury prevention plans. Caution however should be paid because of the Centre of Rotation changes in the shoulder joint at flexion ranges higher than 90° and with repeated measurements.[40] For general information on accelerometers in rehabilitation, you can click here.

Surface Electromyography (sEMG)

sEMG measures electrical activity generated by muscle contraction, providing insight into the timing, sequencing and intensity of muscle activation during shoulder movement.[25] This is particularly useful for identifying altered activation patterns in the rotator cuff or scapular stabilisers.

Features and parameters looked for in sEMG data includes:

  • Time-domain parameters, including Root Mean Square (RMS) amplitude that quantify overall muscle activation intensity, and onset-offset timing that identifies recruitment patterns and neuromuscular coordination.[42][43]
  • Frequency-domain features, including mean and median frequency, providing indices of muscle fatigue and motor control strategies during sustained or repetitive contractions.[43][44]

Beyond diagnostic assessment, sEMG in conjunction with IMU’s have also been applied directly within rehabilitation and functional settings as real-time biofeedback tools: real-time deltoid muscle activity feedback via sEMG and shoulder range of motion via IMU, produced faster and greater improvement in shoulder pain and sport-specific performance compared with an unmonitored control group following a four-week rehabilitation programme in wheelchair basketball players.[5]

The following video demonstrates how functional tasks are monitored with sEMG and IMU, computer modelling and AI renders an image based on biofeedback results.

[45]

Dynamometry

Dynamometers have traditionally been used to objectively measure muscle force of specific muscle groups in order to guide strengthening programmes for injury prevention and rehabilitation.[46] Two distinct dynamometry approaches are used clinically, each with different strength and are tabulated below.

Dynamometer Approach Device Strengths
Hand-held dynamometers (HHD) Consist of a portable device with a hand-held force sensor that is held by the tester as the patient exerts force against the tester. This is done in order to measure the force produced by the specific muscle group being tested.[46] HHD offers a portable and accessible alternative, and strength values obtained with HHD have been shown to correlate highly with isokinetic assessment for shoulder internal and external rotation.[20]

Its reliability is more variable when applied to more complex proprioceptive tasks, however. A cross-sectional study found low agreement between HHD and isokinetic dynamometer measurements.[2]

Isokinetic dynamometers (ID) Machine based device that adjusts its resistance to match muscle force produced during all shoulder range of motion.[20] ID is widely regarded as the gold standard for measuring shoulder muscle strength and endurance, since it provides dynamic strength assessment that surpasses static methods such as manual muscle testing and hand-held dynamometry. Capturing torque output, endurance deficits and ER:IR strength ratio imbalances that are recognised predictors of shoulder injury.[20]

HHD and ID quantify muscle strength by measuring force or torque produced during static or dynamic muscle contraction, with outcomes including;[47]

  • Peak torque
  • Maximal Voluntary Contraction (MVC)
  • Peak force produced
  • Rate of force development

Pressure Garments and Smart Textiles

Pressure-sensing garments incorporate embedded sensors to detect force distribution and compensatory loading during functional movement.[39] Smart textiles use two distinct sensing principles.

Strain-based fabric sensors

Converts movement of conductive particles into measurable electrical signals. It works by changing the electrical resistance when these particles (carbon black, metal nanowires, or conductive polymers) move closer together or further apart.[48] This approach is offered as a customisable alternative to structured motion capture environments, suited to both clinical and home-based rehabilitation monitoring.[19]

An example includes a 3D-printed fibre Bragg grating (FBG) sensor system, developed specifically to identify the shoulder's plane of motion during flexion-extension. It consists of two thermoplastic polyurethane sensor variants that were metrologically characterised for strain and temperature sensitivity.[19]

The video below demonstrates a Tailored Textile Sensor-based Wrap for Shoulder Complex Angles Monitoring using woven resistive textile sensor incorporating 5 silver fibres.[49]

[50]

Textile capacitive sensing

Detecting movement through deformation of conductive fabric patches embedded in a garment rather than through direct strain.[34]

An example includes a capacitive sensing sportswear jacket with eight patches tracking arm and upper body movement with continuous 3D upper body joint prediction modelling.[34] Another example developed to capture complex rotational movement of the shoulder joint is a garment designed specifically with capacitive patches wired to separate sensing channel to isolate shoulder-region movement only.[34]

The video below shows a concept model where instead of patches, capacitive sensing thread was used in seams of a long sleeve garment, detecting pose.

[51]

Machine Learning Classification Approaches

A systematic review of home-based shoulder rehabilitation monitoring identified a range of machine learning models applied to shoulder exercise classification:[9]

  • Decision Trees
  • Support Vector Machines
  • k-Nearest Neighbour
  • Random Forest algorithms
  • Deep learning Convolutional Recurrent Neural Networks (CRNN): used in a named Smart Physiotherapy Activity Recognition System (SPARS) to classify exercises and monitor home adherence.[9]     
  • Fully Convolutional Networks.

Reported evaluation metrics across these models included accuracy, sensitivity, and specificity, though cross-validation methodology and reporting varied considerably between studies.[9]

In overhead sport specifically, comparable approaches have been applied to shoulder-relevant injury prediction. A CNN-based system analysing tennis serve technique from standard video reached 87% accuracy in identifying injurious movement patterns, and unsupervised clustering combined with CNN-based technique analysis in cricket fast bowling achieved 87% sensitivity and 82% specificity for predicting impending injury, including shoulder injury and lumbar stress fracture.[22]

The Table below brings together the type of evaluation technologies and commercially available devises.

Evaluation Technologies: Types and Commercially Available Examples
Technology Type Commercial/Named Systems Examples
3D Motion Capture (marker-based) Vicon; Qualisys; Motion Analysis BaSix©; OptiTrack; Codamotion
3D Motion Capture (markerless) NOKOV Mars series; SimiShape; OpenCap; Canon PowerShot A490; Polhemus FASTRAK; Intel RealSense Kinect (Xbox 360); Microsoft Kinect v2 RGB-D; Asus Xtion PRO; Biosyn FAB
IMU’s Xsens; Axivity; Noraxon; DorsaVi ViMove2; Shimmer3 GSR+; MetaMotion RL; motusBASEBALL/motusTHROW; Polar Vantage V2
Accelerometers and Gyroscope devices Apple Watch and My Shoulder Injury App, Fitbit, Microsoft band, and Nike+ FuelBand.
sEMG Delsys; Noraxon; BTS (FREEEMG); Biometrics Ltd.; Biopac; Cometa Systems; MIOTEC
Dynamometry (hand-held) Walfront NK-500; Smedley (TKK5401, Takei Kiki Kogyo); Hoggan MicroFET; JTech Commander; Lafayette; Jamar®; Kinvent (K-Pull); STT systems STT-iSen (IWS)
Dynamometry (fixed-frame) Biodex; BTE Primus RS; Cybex-Humac Norm; IsoMed 2000
Wearable technology Myontec

Shoulder Injury Prevention and Management

Several attempts have been made to identify risk factors and preventative measures, accurate assessment methods and optimal prevention and management strategies for shoulder injuries.[52][53][54]

Shoulder injury prevention programmes have been highlighted as an appropriate measure for athletes of all levels.[54] Prevention programmes such as the Oslo Sports Trauma Research Center, the Shoulder Control, the FIFA 11+ shoulder injury prevention programmes, and a baseball-specific programme (range of motion, stretching, dynamic stability and strengthening exercises) have demonstrated a moderate to large effect size in reducing the risk of shoulder injuries compared with no intervention.[55] However, the risk of shoulder injury is not the same for every sport and shoulder injury risk has not been studied across diverse samples.[55] Furthermore, shoulder injuries often do not involve only a single structure, but multiple, and real-time feedback is often necessary for the diagnosis, management and documentation of progress of these injuries. Knowledge about sport-specific load profiles as well as optimal management options is acknowledged as a prerequisite for successful treatment of these injuries.[1]

Generic management recommendations for specific shoulder conditions have been provided in reports, but again, these do not necessarily apply to the individual athlete lacking personalisation and optimisation; it is suggested that future high-quality research[55] and technological advances[56] or innovative approaches[57] may alter these recommendations for clinical practice.

For an overview of evidence-based interventions for shoulder pain, click here.

Medical Technologies

BioEntheses

Biological tissue implants that can re-create a tissue junction between the tendon and bone are known as BioEntheses. These implants may allow orthopaedic sports surgeons to better restore the natural structure of the shoulder joint after a complete rupture of the rotator cuff group. This may lead to injuries requiring less time to heal after surgery and longer-lasting repairs after surgery.

A video on such an enthesis is provided here.

Technology in Shoulder Injury Rehabilitation

The main shoulder rehabilitation technologies described in the literature can be summarised according to their function, named examples and clinical relevance, as shown in the table below.

Technology Function Named examples Clinical relevance
Continuous passive motion (CPM) devices Deliver passive shoulder mobilisation without requiring active patient effort. Continuous passive shoulder mobiliser; low-cost CPM shoulder machine. Reduces therapist workload and may improve accessibility across diverse clinical settings.[24]
Robotics and exoskeletons Assist, augment or support shoulder movement during rehabilitation. MEDARM; ArmAssist; ANYexo; MERLIN; CLEVERarm; NESM; servo-motor shoulder exoskeleton mechanisms. Useful where weakness, restricted active range or need for guided repetitive movement limits conventional therapy; home-based reviews identify robotic and wearable systems as the most developed device categories.[10][11][24]
Haptics Provide tactile, force-based or resistance feedback during exercise. Force-controlled resistance device for shoulder rotation. Guides correct movement pathways, discourages compensation and promotes active muscle engagement while increasing range of motion.[24]
Virtual reality (VR) Uses immersive or game-based environments to support shoulder rehabilitation exercises. Oculus Quest 2; Nintendo Wii-based VR exergaming. May improve engagement and home-based movement assessment, with evidence of effectiveness in trials against conventional home exercise, though independent use still requires further development.[9]
Wearable devices, machine learning and sensor-based feedback Monitor movement quality, muscle activation, adherence and exercise accuracy using sensors, cameras or automated analytics. IMUs and M-IMUs; smartwatch-based systems; Patient App and Doctor App systems; surface EMG activation bands; Microsoft Kinect; video-based rehabilitation interfaces. Supports remote monitoring, exercise correction, progression grading and dual-avatar feedback, with systematic review evidence across wearable and camera-based home rehabilitation systems.[9]
Combined rehabilitation platforms Integrate multiple technologies into one monitoring or rehabilitation system. IMU-based tracking with gamified VR and haptic feedback; ReMoVES; multimodal sensor fusion combining IMU, camera and EMG data. Improves monitoring robustness and patient engagement by combining complementary data streams and feedback mechanisms.[10][11][21]

Practice Guidance in the Use of Technologies

Contraindications

Contraindications specific to individual devices remain sparsely documented in the literature reviewed to date. General caution is warranted around pacemaker interference with certain sensor systems and the unsuitability of high-intensity VR or gaming-based rehabilitation in specific acute presentations, though device-specific contraindication data should be confirmed against manufacturer guidance and individual clinical presentation.[39]

Precautions

Several factors need attention when using technology for evaluation and rehabilitation.

  • Loading during rehabilitation should be progressed gradually and individually. Evidence from wheelchair athlete rehabilitation cautions that daily, high-intensity exercise protocols can produce an initial worsening of shoulder pain, and that performance-phase strengthening exercises should not be introduced until low-level sport activity can be performed pain-free.[5]
  • Skin sensitivity should be considered with prolonged use of wearable sensors and garments, and devices should be calibrated and validated prior to clinical use.
  • Data privacy and security should also be treated as a genuine precaution rather than an afterthought: home-based systems that collect biomechanical and physiological data should have clear informed consent procedures, secure storage protocols and defined data retention policies, with patients given meaningful control over how their data is used.[10]

Challenges in the Use of Technology for Shoulder Prevention and Rehabilitation

Challenges in the use of these technologies in shoulder injury prevention and rehabilitation involve general issues such as usability, cost and accessibility in the community; workability within the context of the treatment pathway without interfering with standard processes; and increasing digital literacy and acceptance among clinicians working in this field. [58]

Under-represented Populations

A significant evidence gap exists for Para and wheelchair athletes. Despite shoulder injury being the most common injury among wheelchair athletes, no dedicated prevention studies and only one modifiable risk factor study have been conducted specifically in female, woman or girl Para athletes.[7]

Another gap noted is that traditional injury surveillance methods are poorly suited to populations with limb differences.[8]

Summary

Shoulder technology has moved from laboratory-confined, marker-based systems towards portable, AI-enhanced wearables, markerless video capture, smart textiles and multimodal sensor fusion.[19][22][23][34] Reflecting a broader shift from reactive treatment towards proactive, individualised management.[25]

These tools complement rather than replace manual clinical tests and strength assessment.[14][20]

Technology for shoulder rehabilitation includes CPM devices, robotics, exoskeletons, haptics, VR and sensor-driven home monitoring.[9][10][21][24]

Future work would benefit from standardised outcome measures,[16] stronger interdisciplinary design,[11] and research extending current technologies to underserved populations.[7][19][34]

Key Resources

Diagnosis, prevention and treatment of common shoulder injuries in sport: grading the evidence - a statement paper commissioned by the Danish Society of Sports Physical Therapy (DSSF)

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

Emerging Technologies in Rehabilitation for Complex Injuries and Conditions

Data-driven rehabilitation: Charting the future of physiotherapy with predictive insights

The future of physiotherapy: Integrating technologies to transform rehabilitation and recovery

References

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  15. ↑ 15.0 15.1 Ramey MD, Swaminathan SJ, Locke AR, et al. Trends in Sports-Related Upper Extremity Injuries Presenting to United States Emergency Departments: A Retrospective Analysis of National Injury Data. Journal of Clinical Medicine. 2025;14(17):6208.
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  20. ↑ 20.0 20.1 20.2 20.3 20.4 20.5 20.6 Leahy I, Florkiewicz E, Shotwell MP. Isokinetic Dynamometry for External and Internal Rotation Shoulder Strength in Youth Athletes: A Scoping Review. IJSPT. 2024;19(12).
  21. ↑ 21.0 21.1 21.2 21.3 21.4 Wang P, Wang A, Wang S. Integrating multimodal AI technologies for sports injury prediction and rehabilitation: Systematic review. Journal of Human Sport and Exercise. 2026;21(1):22-37.
  22. ↑ 22.0 22.1 22.2 22.3 22.4 22.5 Souaifi M, Dhahbi W, Jebabli N, et al. Artificial Intelligence in Sports Biomechanics: A Scoping Review on Wearable Technology, Motion Analysis, and Injury Prevention. Bioengineering. 2025;12(8):887.
  23. ↑ 23.0 23.1 23.2 23.3 Thakkar P, Sharma D, Hodakowski A, Bonadiman JA, Westrick J, Gustafson JA. Accuracy and reliability of inertial measurement units to estimate shoulder joint kinematics for clinical application: a systematic review. Computer Methods in Biomechanics and Biomedical Engineering. 2026;0(0):1-27.
  24. ↑ 24.0 24.1 24.2 24.3 24.4 24.5 Sojitra NL, Sharma A. Narrative Review of Shoulder Rehabilitation Devices: Current Trends and Future Directions. J Soc Indian Physiother. Published online June 25, 2025.
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  29. ↑ Qualisys. Using Motion Capture for Baseball Biomechanics in Elite Athletes: A Game-Changing Approach. Available from: https://www.youtube.com/watch?v=dwYSYy4gqOA [last accessed 16/09/2026]
  30. ↑ 30.0 30.1 Butler T, Young M, Gillette JC. Artificial Intelligence for Injury Prevention. Prof Safety. 2024;69(12):24-30.
  31. ↑ BioMechanic. Swing Dancing with Markerless Motion Tracking | OpenPose. Available from: https://www.youtube.com/watch?v=U5_o8yb0NPw [last accessed 16/09/2026]
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  34. ↑ 34.0 34.1 34.2 34.3 34.4 34.5 Geißler D, Zhou B, Bello H, et al. Embedding textile capacitive sensing into smart wearables as a versatile solution for human motion capturing. Sci Rep. 2024;14(1):15797.
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  38. ↑ Penning L, Guldemond N, de Bie R, Walenkamp G. Reproducibility of a 3-dimensional gyroscope in measuring shoulder anteflexion and abduction. BMC Musculoskelet Disord. 2012; 13: 135.
  39. ↑ 39.0 39.1 39.2 Vellios EE, Pinnamaneni S, Camp CL, Dines JS. Technology Used in the Prevention and Treatment of Shoulder and Elbow Injuries in the Overhead Athlete. Curr Rev Musculoskelet Med. 2020 Aug;13(4):472-78.
  40. ↑ 40.0 40.1 El-Zayat BF, Efe T, Heidrich A, Wolf U, Timmesfeld N, Heyse TJ, Lakemeier S, Fuchs-Winkelmann S, Schofer MD. Objective assessment of shoulder mobility with a new 3D gyroscope--a validation study. BMC Musculoskelet Disord. 2011 Jul 21;12:168.
  41. ↑ El-Zayat BF, Efe T, Heidrich A, Anetsmann R, Timmesfeld N, Fuchs-Winkelmann S, Dietmar Schofer M. Objective assessment, repeatability, and agreement of shoulder ROM with a 3D gyroscope. BMC Musculoskelet Disord 2013; 14: 72.
  42. ↑ Liang T, Miao H, Wang H, Liu X, Liu X. Surface Electromyography-Based Analysis of the Lower Limb Muscle Network and Muscle Synergies at Various Gait Speeds. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2023;31:1230-1237.
  43. ↑ 43.0 43.1 Liang D, Yarossi M, Jacobs-Skolik SL, et al. Synergistic activation patterns of hand muscles in left- And right-hand dominant individuals. J Hum Kinet. 2021;76(1):89-100.
  44. ↑ Guo X, He B, Lau KYS, et al. Age-Related Modifications of Muscle Synergies and Their Temporal Activations for Overground Walking. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2022;30:2700-2709.
  45. ↑ The MotionMonitor. Monitoring Functional Tasks with Inertial Sensors and EMG. Available from: https://www.youtube.com/watch?v=BxTOlh18pD0 [last accessed 15/09/2026]
  46. ↑ 46.0 46.1 Vellios EE, Pinnamaneni S, Camp CL, Dines JS. Technology Used in the Prevention and Treatment of Shoulder and Elbow Injuries in the Overhead Athlete. Curr Rev Musculoskelet Med. 2020 Aug;13(4):472-78.
  47. ↑ O’Sullivan P, Menolotto M, O’Flynn B, Komaris DS. Estimation of Maximum Shoulder and Elbow Joint Torques Based on Demographics and Anthropometrics. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2022:4346-4349.
  48. ↑ Keum K, Cho SS, Jo JW, Park SK, Kim YH. Mechanically robust textile-based strain and pressure multimodal sensors using metal nanowire/polymer conducting fibers. iScience. 2022;25(4).
  49. ↑ Zhang R, Hu Y, He Y, et al. A Tailored Textile Sensor-based Wrap for Shoulder Complex Angles Monitoring. In: Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing. UbiComp/ISWC ’23 Adjunct. Association for Computing Machinery; 2023:137-141.
  50. ↑ ACM SIGCHI. A Tailored Textile Sensor-based Wrap for Shoulder Complex Angles Monitoring. Available from: https://www.youtube.com/watch?v=EGRmP85EO1A [last accessed 18/09/2026]
  51. ↑ Catherine Yu. SeamPose: Repurposing Seams as Capacitive Sensors in a Shirt for Upper-Body Pose Tracking. Available from: https://www.youtube.com/watch?v=KBBBOOAVwes [last accessed 17/09/2026]
  52. ↑ Asker M, Brooke HL, Waldén M, Tranaeus U, Johansson F, Skillgate E, Holm L. Risk factors for, and prevention of, shoulder injuries in overhead sports: a systematic review with best-evidence synthesis. Br J Sports Med. 2018;52:1312-19.
  53. ↑ Wright AA, Ness BM, Donaldson M, Hegedus EJ, Salamh P, Cleland JA. Effectiveness of shoulder injury prevention programs in an overhead athletic population: A systematic review. Phys Ther Sport. 2021 Nov;52:189-193.
  54. ↑ 54.0 54.1 Schwank A, Blazey P, Asker M, Møller M, Hägglund M, Gard S, Skazalski C, Haugsbø Andersson S, Horsley I, Whiteley R, Cools AM, Bizzini M, Ardern CL. 2022 Bern Consensus Statement on Shoulder Injury Prevention, Rehabilitation, and Return to Sport for Athletes at All Participation Levels. J Orthop Sports Phys Ther. 2022 Jan;52(1):11-28.
  55. ↑ 55.0 55.1 55.2 Liaghat B, Pedersen JR, Husted RS, Pedersen LL, Thorborg K, Juhl CB. Diagnosis, prevention and treatment of common shoulder injuries in sport: grading the evidence - a statement paper commissioned by the Danish Society of Sports Physical Therapy (DSSF). Br J Sports Med. 2023 Apr;57(7):408-16.
  56. ↑ Mackay F. Revolutionising Shoulder Rehabilitation: The Role of Wearable Tech. (published 14/3/24). Available from: https://mskdoctors.com/doctors/philip-mackay-48bgy/articles/revolutionising-shoulder-rehabilitation-the-role-of-wearable-tech [accessed 19/7/2024]
  57. ↑ Gibson ES, Cairo A, Räisänen AM, Kuntze C, Emery CA, Pasanen K. The Epidemiology of Youth Sport-Related Shoulder Injuries: A Systematic Review. Transl Sports Med. 2022 Aug 23;2022:8791398.
  58. ↑ Gilbert AW, Hauptmannova I, Jaggi A. The use of assistive technology in shoulder exercise rehabilitation - a qualitative study of acceptability within a pilot project. BMC Musculoskelet Disord. 2018 May 2;19(1):133.
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