As wearables are becoming an increasingly important part of wellness and everyday life for many people, their potential in healthcare is also expanding, particularly in personalized and remote healthcare. However, many wearables lack sophistication, relying on simple sensors such as accelerometers and pulse meters to measure heart rate, body composition, and daily activity. Such basic metrics are insufficient for musculoskeletal disease diagnosis, which requires more detailed, multimodal neuromusculoskeletal monitoring. A major challenge in wearables development is the need for precise electromechanical signal measurements, which are difficult to obtain with low-cost systems. Artificial intelligence (AI) holds promise in addressing these analytical challenges and enabling the creation of affordable, sophisticated wearables. While AI has been used for decades in engineering, its clinical application is still emerging, creating an opportunity for the development of AI-enhanced wearables capable of clinical diagnosis. AI can enhance data generated by various sensor types in wearable devices (such as accelerometers, electrical, optical, and acoustic sensors), enabling clinicians to monitor and diagnose complex conditions that require multiple sensing modalities. This review explores current wearable technologies, ongoing research in AI-enhanced wearables, the potential for AI to advance wearable technologies in healthcare, and the future directions in the development of multimodal wearables.
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How AI Is Helping Build Smarter Wearable Devicesfor Muscle and Joint Health
Wearable devices like fitness trackers and smartwatches are becoming more common in everyday life and show promise for use in healthcare. But right now, most of these devices aren’t advanced enough to help doctors diagnose medical conditions—especially complex ones like muscle, nerve, and joint disorders. These conditions require more detailed data than just steps taken or heart rate. Developing wearables that can collect this kind of information is challenging, especially when trying to keep them affordable. That’s where artificial intelligence (AI) comes in. AI can help make sense of complex data from different types of sensors—like ones that track movement, electrical signals, light, or sound—making it possible to build smarter, more useful devices. This review looks at the current state of wearable technology, how AI is being used to improve it, and what the future might hold for creating affordable, AI-powered wearables that can actually help diagnose and monitor health conditions.
Afandizadeh ZargariAHAqajariSAHKhodabandehHRahmaniAKurdahiF.An accurate non-accelerometer-based PPG motion artifact removal technique using CycleGAN. ACM Trans Comput Healthc. 2023;4(1):1:1–14.
2.
AjčevićMDeodatoMMurenaLMiladinovićAMezzarobbaSAccardoA.Assessment of mobility deficit and treatment efficacy in adhesive capsulitis by measurement of kinematic parameters using IMU sensors.In: 2020 IEEE International Symposium on Medical Measurements and Applications [MeMeA] (preprint). 2020. pp. 1–5. Available at: http://arxiv.org/abs/2010.16169. Accessed March 26, 2025.
3.
AlmarshadMAIslamMSAl-AhmadiSBaHammamAS.Diagnostic features and potential applications of PPG signal in healthcare: a systematic review. Healthcare. 2022;10(3):547.
4.
AndrésDRivensIMouratidisPJiménezNCamarenaFTer HaarG.Holographic focused ultrasound hyperthermia system for uniform simultaneous thermal exposure of multiple tumor spheroids. Cancers. 2023;15(9):2540.
5.
AroraAAldermanJPalmerJ, et al. The value of standards for health datasets in artificial intelligence-based applications. Nat Med. 2023;29(11):2929–2938. https://doi.org/10.1038/s41591-023-02608-w
6.
AskariMRRashidMSevilM, et al. Artifact removal from data generated by nonlinear systems: heart rate estimation from blood volume pulse signal. Ind Eng Chem Res. 2020;59(6):2318–2327.
7.
BaidyaSAhadMA.Assessment of optimized electrode configuration for electrical impedance myography using genetic algorithm via finite element model. J Med Eng. 2016;2016:1–7.
8.
BestTMMooreBJaritPMoormanCTLewisGK.Sustained acoustic medicine: wearable, long duration ultrasonic therapy for the treatment of tendinopathy. Phys Sportsmed. 2015;43(4):366–374.
9.
CaoMBaileyBZhangW, et al. Range of motion sensors for monitoring recovery of total knee arthroplasty (preprint). arXiv; 2022 . Available at: http://arxiv.org/abs/2207.00190. Accessed March 26, 2025.
10.
CharltonPKyriazouPMantJMarozasVChowienczykPAlastruey. Wearable photoplethysmography for cardiovascular monitoring. Proc IEEE Inst Electr Electron Eng. 2022;110(3):355–381.
11.
CharltonPHPaliakaitėBPiltK, et al. Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet. Am J Physiol Heart Circ Physiol. 2022;322(4):H493–H522.
12.
ChenSQiJFanSQiaoZYeoJCLimCT.Flexible wearable sensors for cardiovascular health monitoring. Adv Healthc Mater. 2021;10(17):2100116.
13.
FantiniSBigioI.Quantitative Biomedical Optics: Theory, Methods, and Applications. Cambridge, England: Cambridge University Press; 2016: 703.
14.
FitzPatrickAJRodgersGWHooperGJWoodfieldTBF. Development and validation of an acoustic emission device to measure wear in total hip replacements in-vitro and in-vivo. Biomed Sig Process Control. 2017;33:281–288.
15.
GhaedniaHFourmanMSLansA, et al. Augmented and virtual reality in spine surgery, current applications and future potentials. Spine J. 2021;21:1617–1625.
GhaedniaHOwensCEKeiderlingLE, et al. Is Machine learning able to detect and classify failure in piezoresistive bone cement based on electrical signals (preprint)?arXiv:201012147 [eess]. 2020. Availabe at: http://arxiv.org/abs/2010.12147. Accesed August 25, 2021.
18.
GohCHTanLKLovellNHNgSCTanMPLimE.Robust PPG motion artifact detection using a 1-D convolution neural network. Comput Methods Prog Biomed. 2020;196(C). https://doi.org/10.1016/j.cmpb.2020.105596
JinYAlvarezJTSuitorEL, et al. Estimation of joint torque in dynamic activities using wearable A-mode ultrasound. Nat Commun. 2024;15(1):5756.
21.
KarhadeAVBonoCMFerroneM, et al. 77. Development of predictive models for 90-day and 1-year mortality in spinal metastatic disease. Spine J. 2019;19(9, Supplement):S38.
22.
KarhadeAVSchwabJHBedairHS.Development of machine learning algorithms for prediction of sustained postoperative opioid prescriptions after total hip arthroplasty. J Arthroplasty. 2019;34(10):2272-2277.e1.
23.
KeiderlingLRosendorfJOwensCE, et al. Comparing machine learning algorithms for non-invasive detection and classification of failure in piezoresistive bone cement via electrical impedance tomography. Rev Sci Instrum. 2023;94(12):124103.
24.
KhalifaMAlbadawyM.AI in diagnostic imaging: Revolutionising accuracy and efficiency. Computer Methods Programs Biomed Update. 2024;5:100146.
25.
KimDHLuNMaR, et al. Epidermal electronics. Science. 2011;333(6044):838–843.
26.
LansAKanbierLBernsteinDN, et al. Social determinants of health in prognostic machine learning models for orthopaedic outcomes: a systematic review. J Eval Clin Pract. 2023;29(2):292-299. https://doi.org/10.1111/jep.13765.
27.
LeeYJParkCKimHChoSJYeoWH.Artificial intelligence on biomedical signals: technologies, applications, and future directions. Med-X. 2024;2(1):25.
28.
LewisGKLangerMDHendersonCROrtizR.Design and evaluation of a wearable self-applied therapeutic ultrasound device for chronic myofascial pain. Ultrasound Med Biol. 2013;39(8):1429–1439.
29.
LiYLiuG.A review on the coupled method of using the magnetic and acoustic fields for biological tissue imaging. Chin J Electr Eng. 2023;9(1):47–60.
30.
LiuHDingSLinX, et al. Bone fracture healing under the intervention of a stretchable ultrasound array. ACS Nano. 2024;18(30):19549–19560.
31.
MaityAKVeeraraghavanASabharwalA.PPGMotion: model-based detection of motion artifacts in photoplethysmography signals. Biomed Sig Process Control. 2022;75:103632.
MeduriCVlaisavljevichEBrolinsonPGWangVM.Ultrasound stimulation of tendon healing: current strategies and opportunities for novel therapeutic approaches. In: GreisingSMCallJA, eds. Regenerative Rehabilitation: From Basic Science to the Clinic. Cham: Springer International Publishing; 2022: 331–58. https://doi.org/10.1007/978-3-030-95884-8_10
34.
MobbsRJFonsekaRDNatarajanP.Wearable sensor technology in spine care. J Spine Surg. 2022;8(1):84–86.
35.
MurzinDMappsDJLevadaK, et al. Ultrasensitive magnetic field sensors for biomedical applications. Sensors. 2020;20(6):1569.
36.
NeshevDAharonovichI.Optical metasurfaces: new generation building blocks for multi-functional optics. Light Sci Appl. 2018;7(1):58.
37.
NevalainenMTVeikkolaOThevenotJ, et al. Acoustic emissions and kinematic instability of the osteoarthritic knee joint: comparison with radiographic findings. Sci Rep. 2021;11(1):19558.
38.
OginkPTGrootOQKarhadeAV, et al. Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review. Acta Orthop. 2021;92(5):526–531.
39.
OlsenRJHasanSSWooJJNawabiDHRamkumarPN.The fundamentals and applications of wearable sensor devices in sports medicine: a scoping review. Arthroscopy. 2025;41(2):473–492.
40.
OosterhoffJHFJeonSAkhbariB, et al. A deep learning approach using an ensemble model to autocreate an image-based hip fracture registry. OTA Int. 2024;7(1 Suppl):e283.
Paulino PereiraNRJanssenSJVan DijkE, et al. Development of a prognostic survival algorithm for patients with metastatic spine disease. J Bone Joint Surg. 2016;98(21):1767–1776.
43.
PrillRWalterMKrólikowskaABeckerR.A systematic review of diagnostic accuracy and clinical applications of wearable movement sensors for knee joint rehabilitation. Sensors. 2021;21(24):8221.
44.
RehanH.Artificial intelligence and machine learning: the impact of machine learning on predictive analytics in healthcare. Innov Comput Sci J. 2023;9(1):1–20.
45.
ShahAASchwabJH.Predictive modeling for spinal metastatic disease. Diagnostics. 2024;14(9):962.
VavrinskyEEsfahaniNEHausnerM, et al. The current state of optical sensors in medical wearables. Biosensors. 2022;12(4):217.
51.
WanHZhangLWitzS, et al. A literature review of preventable hospital readmissions: preceding the readmissions reduction act. IIE Trans Healthc Syst Eng. 2016 Oct 1;6[4]:193–211.
52.
WangCJWangFSYangKD.Biological effects of extracorporeal shockwave in bone healing: a study in rabbits. Arch Orthop Trauma Surg. 2008;128(8):879–884.
53.
WuSShaZWuL, et al. Recent advances in multimodal skin-like wearable sensors. Appl Phys Rev. 2024;11(4):041323.
54.
YangSChengJShangJ, et al. Stretchable surface electromyography electrode array patch for tendon location and muscle injury prevention. Nat Commun. 2023;14(1):6494.
55.
YazdkhastiAHughesENortonJS, et al. A novel concept of an acoustic ultrasound wearable for early detection of implant failure. Sci Rep. 2024;14(1):31326.
56.
YazdkhastiAKlerkHDLucaciuARMoeinzadRGhaedniaHSchwabJH.A novel acoustic wearable for assessment of tendon health and loading condition (preprint). arXiv; 2024. Available at: http://arxiv.org/abs/2411.00184. Accessed March 27, 2025.
57.
YazdkhastiALloydSSchwabJHYuMGhaedniaH.A novel ultrasonic device for monitoring implant condition (preprint). arXiv; 2024. Available at: http://arxiv.org/abs/2410.00997. Accessed March 27, 2025.
58.
YetisenAKMartinez-HurtadoJLÜnalBKhademhosseiniAButtH.Wearables in medicine. Adv Mater. 2018;30(33):1706910.
ZhengYWuCCaiPZhongZHuangHJiangY.Tiny-PPG: a lightweight deep neural network for real-time detection of motion artifacts in photoplethysmogram signals on edge devices (preprint). arXiv; 2023. Available at: http://arxiv.org/abs/2305.03308. Accessed February 3, 2025.
61.
ZhuJLeiYShahA, et al. MuscleRehab: improving unsupervised physical rehabilitation by monitoring and visualizing muscle engagement. In: Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology. Bend, OR: ACM; 2022: 1–14. https://dl.acm.org/doi/10.1145/3526113.3545705
62.
ZhuJSnowdenJCVerdejoJ, et al. EIT-kit: an electrical impedance tomography toolkit for health and motion sensing. In: The 34th Annual ACM Symposium on User Interface Software and Technology. (Virtual Event) ACM; 2021: 400–13. Available at: https://dl.acm.org/doi/10.1145/3472749.3474758. Accessed March 27, 2025.
The Food and Drug Administration (FDA). Guidance for Industry FDA. 2017.
66.
The Food and Drug Administration (FDA). General wellness: policy for low risk devices —guidance for industry and food and drug administration staff. FDA. 2019.
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