Relationship between training load and recovery rate in artificial intelligence-based sports rehabilitation training
Abstract
In the contemporary era characterized by rapid advancements in science and technology, artificial intelligence (AI) has emerged as a transformative force across various domains, particularly in the realm of sports rehabilitation training. Its unique advantages position AI as a pivotal element in enhancing training efficacy and facilitating recovery from injuries. The objective of this study is to thoroughly investigate the intricate relationship between training load and recovery rate within the context of AI-driven sports rehabilitation training, thereby offering robust theoretical foundations and practical guidance for the optimization of rehabilitation training programs. This paper commences with an overview of the latest developments in the application of AI in sports rehabilitation training, highlighting critical components such as data-driven monitoring of training loads, the formulation of personalized rehabilitation programs, and the assessment of recovery outcomes. Subsequently, the paper delves into a detailed analysis of the interaction mechanisms between training load and recovery rate, examining the impact of varying training loads on the recovery rates of both athletes and patients, as well as strategies for optimizing the recovery process through precise control of training loads. Finally, the paper emphasizes innovative strategies employed by artificial intelligence technology to adjust training loads and enhance recovery rates. This includes the utilization of machine learning algorithms for predicting individual recovery potential and the application of deep learning techniques for real-time monitoring and adaptive modification of training programs. Through this comprehensive investigation, the paper aspires to offer novel insights and methodologies for the scientific and individualized advancement of sports rehabilitation training, thereby contributing valuable knowledge and support to the swift recovery of athletes and patients.
References
1. Brenner JS, Watson A, Council on sports medicine and fitness. Overuse Injuries, Overtraining, and Burnout in Young Athletes. Pediatrics. 2024; 153(2). doi: 10.1542/peds.2023-065129
2. Wu S, Luo X. Prevention and Treatment of Sports Injuries and Rehabilitative Physical Training of Wushu Athletes. Algalil FA, ed. Applied Bionics and Biomechanics. 2022; 2022: 1-9. doi: 10.1155/2022/2870385
3. Haddad M, Stylianides G, Djaoui L, et al. Session-RPE Method for Training Load Monitoring: Validity, Ecological Usefulness, and Influencing Factors. Frontiers in Neuroscience. 2017; 11. doi: 10.3389/fnins.2017.00612
4. Miller S, Mandrusiak A, Adsett J. Getting to the Heart of the Matter: What is the Landscape of Exercise Rehabilitation for People With Heart Failure in Australia? Heart, Lung and Circulation. 2018; 27(11): 1350-1356. doi: 10.1016/j.hlc.2017.08.016
5. Song B, Tuo P. Application of Artificial Intelligence and Virtual Reality Technology in the Rehabilitation Training of Track and Field Athletes. Kumar A, ed. Wireless Communications and Mobile Computing. 2022; 2022: 1-11. doi: 10.1155/2022/9828199
6. Li L, Wei Y, Xiang S. Infrared thermal image monitoring based on artificial intelligence application in the prevention of sports injuries in aerobics: Computational thermal modeling. Thermal Science and Engineering Progress. 2025; 57: 103126. doi: 10.1016/j.tsep.2024.103126
7. Kakavas G, Malliaropoulos N, Pruna R, et al. Artificial intelligence: A tool for sports trauma prediction. Injury. 2020; 51: S63-S65. doi: 10.1016/j.injury.2019.08.033
8. Liu J, Mei J, Zhang X, et al. Augmented reality-based training system for hand rehabilitation. Multimedia Tools and Applications. 2016; 76(13): 14847-14867. doi: 10.1007/s11042-016-4067-x
9. Kay J, Naji L, de SA D, et al. Graft choice has no significant influence on the rate of return to sport at the preinjury level after revision anterior cruciate ligament reconstruction: a systematic review and meta-analysis. Journal of ISAKOS. 2017; 2(1): 21-30. doi: 10.1136/jisakos-2016-000113
10. Zhou T, Wu X, Wang Y, et al. Application of artificial intelligence in physical education: a systematic review. Education and Information Technologies. 2023; 29(7): 8203-8220. doi: 10.1007/s10639-023-12128-2
11. Chang MC, Kim JK, Park D. The Application of Artificial Intelligence in the Field of Rehabilitation. American Journal of Physical Medicine & Rehabilitation. 2022; 102(4): e58-e59. doi: 10.1097/phm.0000000000002121
12. Guo Y, Zhang H, Fang L, et al. A self-powered flexible piezoelectric sensor patch for deep learning-assisted motion identification and rehabilitation training system. Nano Energy. 2024; 123: 109427. doi: 10.1016/j.nanoen.2024.109427
13. Hammes F, Hagg A, Asteroth A, et al. Artificial Intelligence in Elite Sports—A Narrative Review of Success Stories and Challenges. Frontiers in Sports and Active Living. 2022; 4. doi: 10.3389/fspor.2022.861466
14. Hatamzadeh M, Sharifnezhad A, Hassannejad R, et al. Discriminative sEMG-based features to assess damping ability and interpret activation patterns in lower-limb muscles of ACLR athletes. Biomedical Signal Processing and Control. 2023; 83: 104665. doi: 10.1016/j.bspc.2023.104665
15. Kwak JM, Ha TH, Sun Y, et al. Motion quality in rotator cuff tear using an inertial measurement unit: new parameters for dynamic motion assessment. Journal of Shoulder and Elbow Surgery. 2020; 29(3): 593-599. doi: 10.1016/j.jse.2019.07.038
16. Che C. [Retracted] Optimization of Interactive Animation Capture System for Human Upper Limb Movement Based on XSENS Sensor. Shi G, ed. Journal of Sensors. 2021; 2021(1). doi: 10.1155/2021/5246438
17. Tsinganos P, Cornelis B, Cornelis J, et al. Hilbert sEMG data scanning for hand gesture recognition based on deep learning. Neural Computing and Applications. 2020; 33(7): 2645-2666. doi: 10.1007/s00521-020-05128-7
18. T P, Elumalai VK, E B, Sandhiya D. A surface electromyography based hand gesture recognition framework leveraging variational mode decomposition technique and deep learning classifier. Engineering Applications of Artificial Intelligence. 2024; 130: 107669. doi: 10.1016/j.engappai.2023.107669
19. Madore B, Preiswerk F, Bredfeldt JS, et al. Ultrasound‐based sensors to monitor physiological motion. Medical Physics. 2021; 48(7): 3614-3622. doi: 10.1002/mp.14949
20. Wei Z. RETRACTED ARTICLE: Simulation of Artificial Intelligence Algorithm Based on Network Anomaly Detection and Wireless Sensor Network in Sports Cardiopulmonary Monitoring System. In: Mobile Networks and Applications. Springer; 2024. doi: 10.1007/s11036-024-02409-6
21. Zhang X, Shi Y, Bai H. [Retracted] Immersive Virtual Reality Physical Education Instructional Patterns on the Foundation of Vision Sensor. Lv H, ed. Journal of Sensors. 2021; 2021(1). doi: 10.1155/2021/7752447
22. Barry DT. Adaptation, Artificial Intelligence, and Physical Medicine and Rehabilitation. PM&R. 2018; 10(9S2). doi: 10.1016/j.pmrj.2018.04.013
23. Li W, Lou S, Sun Q. Robot-assisted upper limb rehabilitation training pose capture based on optical motion capture. In: The International Journal of Advanced Manufacturing Technology. Springer; 2024.
24. Li Y, Wang Q, Liu XL, et al. Effect of the physical rehabilitation program based on self-care ability in patients with acute ischemic stroke: a quasi-experimental study. Frontiers in Neurology. 2023; 14. doi: 10.3389/fneur.2023.1181651
25. Downing L, Ramjist JK, Tyrrell A, et al. Development of a five point enhanced recovery protocol for pectus excavatum surgery. Journal of Pediatric Surgery. 2023; 58(5): 822-827. doi: 10.1016/j.jpedsurg.2023.01.028
26. Faria AL, Almeida Y, Branco D, et al. NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation. Frontiers in Neurology. 2024; 14. doi: 10.3389/fneur.2023.1258323
27. Hernandez-Boussard T, Bozkurt S, Ioannidis JPA, et al. MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care. Journal of the American Medical Informatics Association. 2020; 27(12): 2011-2015. doi: 10.1093/jamia/ocaa088
28. Gordo A, Santos Silva I dos, Nicolau H, et al. On the potential of virtual reality for locomotion rehabilitation. Annals of Medicine. 2021; 53(sup1). doi: 10.1080/07853890.2021.1896637
29. Hu Y, Yuan X, Ye P, et al. Virtual Reality in Clinical Nursing Practice Over the Past 10 Years: Umbrella Review of Meta-Analyses. JMIR Serious Games. 2023; 11: e52022-e52022. doi: 10.2196/52022
30. Tobler P, Cyriac J, Kovacs BK, et al. AI-based detection and classification of distal radius fractures using low-effort data labeling: evaluation of applicability and effect of training set size. European Radiology. 2021; 31(9): 6816-6824. doi: 10.1007/s00330-021-07811-2
31. Ben Chaabane N, Conze PH, Lempereur M, et al. Quantitative gait analysis and prediction using artificial intelligence for patients with gait disorders. Scientific Reports. 2023; 13(1). doi: 10.1038/s41598-023-49883-8
32. Cheng C, Liu T, Zhang B, et al. Effects of robot-assisted hand function therapy on brain functional mechanisms: a synchronized study using fNIRS and sEMG. Frontiers in Medicine. 2024; 11. doi: 10.3389/fmed.2024.1411616
33. Mahler M, Rossin B, Kubassova O. Augmented versus artificial intelligence for stratification of patients with myositis. Annals of the Rheumatic Diseases. 2020; 79(12): e162. doi: 10.1136/annrheumdis-2019-216000
34. Wang C, Kong J, Qi H. Areas of Research Focus and Trends in the Research on the Application of VR in Rehabilitation Medicine. Healthcare. 2023; 11(14): 2056. doi: 10.3390/healthcare11142056
35. Kazimierczak W, Kazimierczak N, Issa J, et al. Endodontic Treatment Outcomes in Cone Beam Computed Tomography Images—Assessment of the Diagnostic Accuracy of AI. Journal of Clinical Medicine. 2024; 13(14): 4116. doi: 10.3390/jcm13144116
36. Ekambaram D, Ponnusamy V. Real-time AI-assisted visual exercise pose correctness during rehabilitation training for musculoskeletal disorder. Journal of Real-Time Image Processing. 2023; 21(1). doi: 10.1007/s11554-023-01385-6
37. Xie P, Wang Z, Li Z, et al. Research on Rehabilitation Training Strategies Using Multimodal Virtual Scene Stimulation. Frontiers in Aging Neuroscience. 2022; 14. doi: 10.3389/fnagi.2022.892178
38. Yan H. Construction and Application of Virtual Reality-Based Sports Rehabilitation Training Program. Bin S, ed. Occupational Therapy International. 2022; 2022: 1-13. doi: 10.1155/2022/4364360
39. Lockhart T, Frames C, Olson M, et al. Effects of protective step training on proactive and reactive motor adaptations in Parkinson’s disease patients. Frontiers in Neurology. 2023; 14. doi: 10.3389/fneur.2023.1211441
40. Mani Bharathi V, Manimegalai P, George ST, et al. A systematic review of techniques and clinical evidence to adopt virtual reality in post-stroke upper limb rehabilitation. Virtual Reality. 2024; 28(4). doi: 10.1007/s10055-024-01065-1
41. Mehraram R, De Clercq P, Kries J, et al. Functional connectivity of stimulus-evoked brain responses to natural speech in post-stroke aphasia. Journal of Neural Engineering. 2024; 21(6): 066010. doi: 10.1088/1741-2552/ad8ef9
42. Sarasso E, Gardoni A, Zenere L, et al. Action observation and motor imagery improve motor imagery abilities in patients with Parkinson’s disease – A functional MRI study. Parkinsonism & Related Disorders. 2023; 116: 105858. doi: 10.1016/j.parkreldis.2023.105858
43. Wang B, Huang H. Effects of various exercise interventions on motor function in cerebral palsy patients: a systematic review and network meta-analysis. Neurological Sciences. 2024; 45(12): 5915-5927. doi: 10.1007/s10072-024-07741-z
44. Aderinto N, Olatunji G, Kokori E, et al. The Effectiveness of Virtual Reality Therapy in Improving Motor Function and Quality of Life among Children with Cerebral Palsy. Current Treatment Options in Pediatrics. 2024; 11(1). doi: 10.1007/s40746-024-00317-1
45. Ai QS, Chen L, Liu Q, et al. Rehabilitation assessment for lower limb disability based on multi-disciplinary approaches. Australasian Physical & Engineering Sciences in Medicine. 2014; 37(2): 355-365. doi: 10.1007/s13246-014-0268-7
46. Lee SH, Cui J, Liu L, et al. An Evidence-Based Intelligent Method for Upper-Limb Motor Assessment via a VR Training System on Stroke Rehabilitation. IEEE Access. 2021; 9: 65871-65881. doi: 10.1109/access.2021.3075778
47. Ma W, Guo B. Construction of neural network model for exercise load monitoring based on yoga training data and rehabilitation therapy. Heliyon. 2024; 10(12): e32679. doi: 10.1016/j.heliyon.2024.e32679
48. Allard P, Martinez R, Deguire S, et al. In-Season Session Training Load Relative to Match Load in Professional Ice Hockey. Journal of Strength and Conditioning Research. 2020; 36(2): 486-492. doi: 10.1519/jsc.0000000000003490
49. Hoppeler H. Moderate Load Eccentric Exercise; A Distinct Novel Training Modality. Frontiers in Physiology. 2016; 7. doi: 10.3389/fphys.2016.00483
50. Lombardi G, Ziemann E, Banfi G. Physical Activity and Bone Health: What Is the Role of Immune System? A Narrative Review of the Third Way. Frontiers in Endocrinology. 2019; 10. doi: 10.3389/fendo.2019.00060
51. Jin N, Tian J, Li Y, et al. A Validation Study of Heart Rate Variability Index in Monitoring Basketball Training Load. Frontiers in Physiology. 2022; 13. doi: 10.3389/fphys.2022.881927
52. Flatt AA, Esco MR, Allen JR, et al. Heart Rate Variability and Training Load Among National Collegiate Athletic Association Division 1 College Football Players Throughout Spring Camp. Journal of Strength and Conditioning Research. 2018; 32(11): 3127-3134. doi: 10.1519/jsc.0000000000002241
53. Sandbakk Ø, Haugen T, Ettema G. The Influence of Exercise Modality on Training Load Management. International Journal of Sports Physiology and Performance. 2021; 16(4): 605-608. doi: 10.1123/ijspp.2021-0022
54. Pillitteri G, Rossi A, Simonelli C, et al. Association between internal load responses and recovery ability in U19 professional soccer players: A machine learning approach. Heliyon. 2023; 9(4): e15454. doi: 10.1016/j.heliyon.2023.e15454
55. Mandorino M, Tessitore A, Lacome M. Loading or Unloading? This Is the Question! A Multi-Season Study in Professional Football Players. Sports. 2024; 12(6): 148. doi: 10.3390/sports12060148
56. He R, Sun X, Yu X, et al. [Retracted] Static Model of Athlete’s Upper Limb Posture Rehabilitation Training Indexes. Tang M, ed. BioMed Research International. 2022; 2022(1). doi: 10.1155/2022/9353436
57. Alzakerin HM, Halkiadakis Y, Morgan KD. Force and Rate Metrics Provide Return-to-Sport Criterion after ACL Reconstruction. Medicine & Science in Sports & Exercise. 2020; 53(2): 275-279. doi: 10.1249/mss.0000000000002472
58. Lynch N, Sweeney G, Cradock K, et al. An investigation into nutritional knowledge of Irish rugby coaches. Proceedings of the Nutrition Society. 2024; 83(OCE4). doi: 10.1017/s0029665124005652
59. Soler-López A, Moreno-Villanueva A, Gómez-Carmona CD, et al. The Role of Biomarkers in Monitoring Chronic Fatigue Among Male Professional Team Athletes: A Systematic Review. Sensors. 2024; 24(21): 6862. doi: 10.3390/s24216862
60. Tipton KD, Hamilton DL, Gallagher IJ. Assessing the Role of Muscle Protein Breakdown in Response to Nutrition and Exercise in Humans. Sports Medicine. 2018; 48(S1): 53-64. doi: 10.1007/s40279-017-0845-5
61. Kaspy MS, Hannaian SJ, Bell ZW, et al. The effects of branched-chain amino acids on muscle protein synthesis, muscle protein breakdown and associated molecular signalling responses in humans: an update. Nutrition Research Reviews. 2023; 37(2): 273-286. doi: 10.1017/s0954422423000197
62. Denwood G, Tarasov A, Salehi A, et al. Glucose stimulates somatostatin secretion in pancreatic δ-cells by cAMP-dependent intracellular Ca2+ release. Journal of General Physiology. 2019; 151(9). doi: 10.1085/jgp.201912351
63. Vergroesen PPA, Emanuel KS, Peeters M, et al. Are axial intervertebral disc biomechanics determined by osmosis? Journal of Biomechanics. 2018; 70: 4-9. doi: 10.1016/j.jbiomech.2017.04.027
64. Long RG, Zderic I, Gueorguiev B, et al. Effects of Level, Loading Rate, Injury and Repair on Biomechanical Response of Ovine Cervical Intervertebral Discs. Annals of Biomedical Engineering. 2018; 46(11): 1911-1920. doi: 10.1007/s10439-018-2077-8
65. Marshall PW, Forward T, Enoka RM. Fatigability of the knee extensors following high- and low-load resistance exercise sessions in trained men. European Journal of Applied Physiology. 2021; 122(1): 245-254. doi: 10.1007/s00421-021-04832-z
66. Andrade DM, Fernandes G, Miranda R, et al. Training Load and Recovery in Volleyball During a Competitive Season. Journal of Strength and Conditioning Research. 2021; 35(4): 1082-1088. doi: 10.1519/jsc.0000000000002837
67. Sansone P, Tschan H, Foster C, et al. Monitoring Training Load and Perceived Recovery in Female Basketball: Implications for Training Design. Journal of Strength and Conditioning Research. 2020; 34(10): 2929-2936. doi: 10.1519/jsc.0000000000002971
68. Daly E, Pearce AJ, Esser P, et al. Evaluating the relationship between neurological function, neuromuscular fatigue, and subjective performance measures in professional rugby union players. Frontiers in Sports and Active Living. 2022; 4. doi: 10.3389/fspor.2022.1058326
69. Padua DA, Oñate JA. Training Load, Recovery, and Injury: A Simple or Complex Relationship? Journal of Athletic Training. 2020; 55(9): 873-873. doi: 10.4085/1062-6050-055.09
70. Luo S, Xiao Y, Zhang X, et al. PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training. ACM Transactions on Intelligent Systems and Technology. 2024; 15(5): 1-24. doi: 10.1145/3664927
71. Nguyen TT, Nguyen TT. PERSONA: A personalized model for code recommendation. Son LH, ed. PLOS ONE. 2021; 16(11): e0259834. doi: 10.1371/journal.pone.0259834
72. Wang C, Wang X, Li Q, et al. Recognizing and predicting muscular fatigue of biceps brachii in motion with novel fabric strain sensors based on machine learning. Biomedical Signal Processing and Control. 2024; 96: 106647. doi: 10.1016/j.bspc.2024.106647
73. Li S, Su C, Huang L, et al. Personalized passive training control strategy for a lower limb rehabilitation robot with specified step lengths. Intelligent Service Robotics. 2024; 18(1): 137-156. doi: 10.1007/s11370-024-00576-9
74. Sarirete A, Balfagih Z, Brahimi T, et al. Artificial intelligence and machine learning research: towards digital transformation at a global scale. Journal of Ambient Intelligence and Humanized Computing. 2021; 13(7): 3319-3321. doi: 10.1007/s12652-021-03168-y
75. Suchomel TJ, Nimphius S, Bellon CR, et al. Training for Muscular Strength: Methods for Monitoring and Adjusting Training Intensity. Sports Medicine. 2021; 51(10): 2051-2066. doi: 10.1007/s40279-021-01488-9
76. Gallant TL, Ong LF, Wong L, et al. Low Energy Availability and Relative Energy Deficiency in Sport: A Systematic Review and Meta-analysis. Sports Medicine; 2024.
77. Belbasis A, Fuss FK. Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG. Frontiers in Physiology. 2018; 9. doi: 10.3389/fphys.2018.00408
78. Gan L, Yang Z, Shen Y, et al. Heart rate variability analysis method for exercise-induced fatigue monitoring. Biomedical Signal Processing and Control. 2024; 92: 105966. doi: 10.1016/j.bspc.2024.105966
79. Yiiong SP, Ting HY, Tan DYW, et al. Investigation of Relation between Sport’s Motion and Heart Rate Variability (HRV) Based on Biometric Parameters. IOP Conference Series: Materials Science and Engineering. 2019; 495: 012015. doi: 10.1088/1757-899x/495/1/012015
80. Arakawa T, Tomoto K, Nitta H, et al. A Wearable Cellulose Acetate-Coated Mouthguard Biosensor for In Vivo Salivary Glucose Measurement. Analytical Chemistry. 2020; 92(18): 12201-12207. doi: 10.1021/acs.analchem.0c01201
81. Russo I, Della Gatta PA, Garnham A, et al. The Effects of an Acute “Train-Low” Nutritional Protocol on Markers of Recovery Optimization in Endurance-Trained Male Athletes. International Journal of Sports Physiology and Performance. 2021; 16(12): 1764-1776. doi: 10.1123/ijspp.2020-0847
82. Yang Y. Research on Active–Passive Training Control Strategies for Upper Limb Rehabilitation Robot. Machines. 2024; 12(11): 784. doi: 10.3390/machines12110784
83. Ravé G, Granacher U, Boullosa D, et al. How to Use Global Positioning Systems (GPS) Data to Monitor Training Load in the “Real World” of Elite Soccer. Frontiers in Physiology. 2020; 11. doi: 10.3389/fphys.2020.00944
84. Gronwald T, Schaffarczyk M, Hoos O. Orthostatic testing for heart rate and heart rate variability monitoring in exercise science and practice. European Journal of Applied Physiology. 2024; 124(12): 3495-3510. doi: 10.1007/s00421-024-05601-4
Copyright (c) 2025 Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
