{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:55:48Z","timestamp":1783526148480,"version":"3.55.0"},"reference-count":68,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioeng. Biotechnol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study develops a particle swarm optimization (PSO)-based assessment system for evaluating upper extremity and shoulder joint muscle strength with potential application to stroke rehabilitation. This study validates the system on healthy adult volunteers using surface electromyography and joint motion data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The system comprises a multimodal data acquisition module and a computational analysis pipeline. sEMG signals were collected non-invasively from the anterior, medial, and posterior deltoid muscles using bipolar electrode arrays. These signals are subjected to noise reduction and feature extraction. Simultaneously, triaxial kinematic data of the glenohumeral joint were obtained via an MPU6050 inertial measurement unit, processed through quaternion-based orientation estimation. Machine learning models, including Backpropagation Neural Network (BPNN), Support Vector Machines (SVM), and particle swarm optimization algorithms (PSO-BPNN, PSO-SVR), were applied for regression analysis. Model performance was evaluated using R-squared (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Bias Error (MBE).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The system successfully collected electromyographic and kinematic data. PSO-SVR achieved the best predictive performance (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> = 0.8600, RMSE = 0.3122, MAE = 0.2453, MBE = 0.0293), outperforming SVR, PSO-BPNN, and BPNN.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The PSO-SVR model demonstrated the highest accuracy, which can better facilitate therapists in conducting muscle strength rehabilitation assessments.<\/jats:p><\/jats:sec><jats:sec><jats:title>Significance<\/jats:title><jats:p>This system enhances quantitative assessment of muscle strength in stroke patients, providing a reliable tool for rehabilitation monitoring and personalized therapy adjustments.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fbioe.2025.1619411","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T05:47:22Z","timestamp":1752126442000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Development of an upper limb muscle strength rehabilitation assessment system using particle swarm optimisation"],"prefix":"10.3389","volume":"13","author":[{"given":"Chuangan","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siqi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meiyi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Lai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyu","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyue","family":"Gou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Yi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"83","DOI":"10.4316\/aece.2022.04010","article-title":"A comparative analysis of two approaches for estimation of upper limb orientation using inertial and kinect sensors","volume":"22","author":"Acharya","year":"2022","journal-title":"Adv. Electr. and Comput. Eng."},{"key":"B2","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1123\/jsr.2023-0253","article-title":"Isokinetic peak torque improvement and shoulder muscle ratios imbalance correction after specific strength training on a new ballistic throwing device: a randomized controlled trial","volume":"1","author":"Agrebi","year":"2024","journal-title":"J. Sport Rehabilitation"},{"key":"B3","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1093\/ije\/dyac210","article-title":"Epidemiology and trends in stroke mortality in the usa, 1975\u20132019","volume":"52","author":"Ananth","year":"2023","journal-title":"Int. J. Epidemiol."},{"key":"B4","doi-asserted-by":"publisher","first-page":"67","DOI":"10.3389\/fspor.2020.00067","article-title":"External responsiveness of the superoptm device to assess recovery after exercise: a pilot study","volume":"2","author":"Ardig\u00f2","year":"2020","journal-title":"Front. sports Act. living"},{"key":"B5","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1109\/msp.2016.2602099","article-title":"Compressive video sensing: Algorithms, architectures, and applications","volume":"34","author":"Baraniuk","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"B6","doi-asserted-by":"publisher","first-page":"1142","DOI":"10.1016\/j.apmr.2024.01.020","article-title":"A novel, wearable inertial measurement unit for stroke survivors: validity, acceptability, and usability","volume":"105","author":"Bishop","year":"2024","journal-title":"Archives Phys. Med. rehabilitation"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/8194537","article-title":"Considerations and practical options for measuring muscle strength: a narrative review","volume":"2019","author":"Bohannon","year":"2019","journal-title":"BioMed Res. Int."},{"key":"B8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12938-016-0291-x","article-title":"Real-time inverse kinematics for the upper limb: a model-based algorithm using segment orientations","volume":"16","author":"Borb\u00e9ly","year":"2017","journal-title":"Biomed. Eng. online"},{"key":"B9","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1007\/s10916-020-01639-x","article-title":"Upper limb movement classification via electromyographic signals and an enhanced probabilistic network","volume":"44","author":"Burns","year":"2020","journal-title":"J. Med. Syst."},{"key":"B10","doi-asserted-by":"publisher","first-page":"3099","DOI":"10.1109\/jiot.2020.3033473","article-title":"Rfid reader anticollision based on distributed parallel particle swarm optimization","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE internet things J."},{"key":"B11","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1007\/s00415-020-10143-8","article-title":"Age is negatively associated with upper limb recovery after conventional but not robotic rehabilitation in patients with stroke: a secondary analysis of a randomized-controlled trial","volume":"268","author":"Cecchi","year":"2021","journal-title":"J. Neurology"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1422","DOI":"10.1016\/j.apmr.2022.03.012","article-title":"Effects of virtual reality rehabilitation training on cognitive function and activities of daily living of patients with poststroke cognitive impairment: a systematic review and meta-analysis","volume":"103","author":"Chen","year":"","journal-title":"Archives Phys. Med. Rehabilitation"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/5227955","article-title":"Analyzing human muscle state with flexible sensors","volume":"2022","author":"Chen","year":"","journal-title":"J. Sensors"},{"key":"B14","doi-asserted-by":"publisher","first-page":"100825","DOI":"10.1016\/j.lanepe.2023.100825","article-title":"Efficacy and safety of transcranial direct current stimulation to the ipsilesional motor cortex in subacute stroke (nets): a multicenter, randomized, double-blind, placebo-controlled trial","volume":"38","author":"Cordes","year":"2024","journal-title":"Lancet Regional Health\u2013Europe"},{"key":"B15","doi-asserted-by":"publisher","first-page":"487","DOI":"10.3390\/s17030487","article-title":"Parameter selection and performance comparison of particle swarm optimization in sensor networks localization","volume":"17","author":"Cui","year":"2017","journal-title":"Sensors"},{"key":"B16","doi-asserted-by":"publisher","first-page":"108532","DOI":"10.1016\/j.asoc.2022.108532","article-title":"A multi-objective particle swarm optimization algorithm based on two-archive mechanism","volume":"119","author":"Cui","year":"2022","journal-title":"Appl. soft Comput."},{"key":"B17","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1519\/jsc.0000000000002694","article-title":"Reliability, sensitivity, and minimal detectable change of a new specific climbing test for assessing asymmetry in reach technique","volume":"35","author":"\u010cular","year":"2021","journal-title":"J. Strength and Cond. Res."},{"key":"B18","doi-asserted-by":"publisher","first-page":"1850025","DOI":"10.1142\/s0129065718500259","article-title":"Extracting and classifying spatial muscle activation patterns in forearm flexor muscles using high-density electromyogram recordings","volume":"29","author":"Dai","year":"2019","journal-title":"Int. J. neural Syst."},{"key":"B19","doi-asserted-by":"publisher","first-page":"1545","DOI":"10.1016\/s0140-6736(21)00475-x","article-title":"Vagus nerve stimulation paired with rehabilitation for upper limb motor function after ischaemic stroke (Vns-rehab): a randomised, blinded, pivotal, device trial","volume":"397","author":"Dawson","year":"2021","journal-title":"lancet"},{"key":"B20","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/s12193-023-00406-9","article-title":"The cognitive basis for virtual reality rehabilitation of upper-extremity motor function after neurotraumas","volume":"17","author":"Dewil","year":"2023","journal-title":"J. Multimodal User Interfaces"},{"key":"B21","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1123\/ijspp.2016-0063","article-title":"The effect of variation of plyometric push-ups on force-application kinetics and perception of intensity","volume":"12","author":"Dhahbi","year":"2017","journal-title":"Int. J. sports physiology Perform."},{"key":"B22","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.irbm.2013.12.005","article-title":"Reference database of the gait cycle for young healthy Tunisian adults","volume":"35","author":"Dhahbi","year":"2014","journal-title":"IRBM"},{"key":"B23","doi-asserted-by":"publisher","first-page":"e2026676118","DOI":"10.1073\/pnas.2026676118","article-title":"Critical period after stroke study (cpass): a phase ii clinical trial testing an optimal time for motor recovery after stroke in humans","volume":"118","author":"Dromerick","year":"2021","journal-title":"Proc. Natl. Acad. Sci."},{"key":"B24","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1161\/01.str.29.4.785","article-title":"Effect of a therapeutic intervention for the hemiplegic upper limb in the acute phase after stroke: a single-blind, randomized, controlled multicenter trial","volume":"29","author":"Feys","year":"1998","journal-title":"Stroke"},{"key":"B25","doi-asserted-by":"publisher","first-page":"1211220","DOI":"10.3389\/fpubh.2024.1211220","article-title":"Prediction and the influencing factor study of colorectal cancer hospitalization costs in China based on machine learning-random forest and support vector regression: a retrospective study","volume":"12","author":"Gao","year":"2024","journal-title":"Front. Public Health"},{"key":"B26","doi-asserted-by":"publisher","first-page":"1950025","DOI":"10.1142\/s0129065719500254","article-title":"Tangent space features-based transfer learning classification model for two-class motor imagery brain\u2013computer interface","volume":"29","author":"Gaur","year":"2019","journal-title":"Int. J. neural Syst."},{"key":"B27","doi-asserted-by":"publisher","first-page":"1850039","DOI":"10.1142\/s0129065718500399","article-title":"Neuromechanical modelling of articulatory movements from surface electromyography and speech formants","volume":"29","author":"G\u00f3mez-Vilda","year":"2019","journal-title":"Int. J. Neural Syst."},{"key":"B28","doi-asserted-by":"publisher","first-page":"1850058","DOI":"10.1142\/s0129065718500582","article-title":"A machine learning approach to reveal the neurophenotypes of autisms","volume":"29","author":"G\u00f3rriz","year":"2019","journal-title":"Int. J. neural Syst."},{"key":"B29","first-page":"1113","article-title":"Control of a lower limb exoskeleton robot by upper limb semg signal","author":"Guo","year":"2021"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.22514\/ejgo.2024.006","article-title":"Diagnostic value of dynamic enhanced multi-slice spiral ct in lymph node metastasis of cervical cancer and analysis of the causes of missed diagnosis","volume":"45","author":"Han","year":"2024","journal-title":"Eur. J. Gynaecol. Oncol."},{"key":"B31","doi-asserted-by":"publisher","first-page":"3052","DOI":"10.1161\/strokeaha.107.490730","article-title":"Development and validation of a short form of the fugl-meyer motor scale in patients with stroke","volume":"38","author":"Hsieh","year":"2007","journal-title":"Stroke"},{"key":"B32","doi-asserted-by":"publisher","first-page":"657","DOI":"10.3390\/s19030657","article-title":"The use of wearable sensors for the movement assessment on muscle contraction sequences in post-stroke patients during sit-to-stand","volume":"19","author":"Hsu","year":"2019","journal-title":"Sensors"},{"key":"B33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/5694265","article-title":"Identification of upper-limb movements based on muscle shape change signals for human-robot interaction","volume":"2020","author":"Huang","year":"2020","journal-title":"Comput. Math. methods Med."},{"key":"B34","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1177\/1545968314562647","article-title":"A randomized controlled trial of the effect of early upper-limb training on stroke recovery and brain activation","volume":"29","author":"Hubbard","year":"2015","journal-title":"Neurorehabilitation neural repair"},{"key":"B35","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.asoc.2016.02.018","article-title":"An enhanced particle swarm optimization with levy flight for global optimization","volume":"43","author":"Jensi","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"B36","doi-asserted-by":"publisher","first-page":"6618407","DOI":"10.1155\/2021\/6618407","article-title":"Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest","volume":"2021","author":"Khan","year":"2021","journal-title":"Adv. Civ. Eng."},{"key":"B37","doi-asserted-by":"publisher","first-page":"1622","DOI":"10.3390\/s20061622","article-title":"Analysis of machine learning-based assessment for elbow spasticity using inertial sensors","volume":"20","author":"Kim","year":"2020","journal-title":"Sensors"},{"key":"B38","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1007\/s11709-021-0742-8","article-title":"Evaluation and prediction of slope stability using machine learning approaches","volume":"15","author":"Lin","year":"2021","journal-title":"Front. Struct. Civ. Eng."},{"key":"B39","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1631\/jzus.b2100999","article-title":"Research and application advances in rehabilitation assessment of stroke","volume":"23","author":"Liu","year":"2022","journal-title":"J. Zhejiang University-Science B"},{"key":"B40","doi-asserted-by":"publisher","first-page":"103771","DOI":"10.1016\/j.engappai.2020.103771","article-title":"An adaptive switchover hybrid particle swarm optimization algorithm with local search strategy for constrained optimization problems","volume":"95","author":"Liu","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"B41","doi-asserted-by":"publisher","first-page":"784120","DOI":"10.3389\/fdgth.2021.784120","article-title":"Occupational therapy assessment for upper limb rehabilitation: a multisensor-based approach","volume":"3","author":"Mahmoud","year":"2021","journal-title":"Front. Digital Health"},{"key":"B42","doi-asserted-by":"publisher","first-page":"7168","DOI":"10.1109\/jsen.2022.3233344","article-title":"An inertial-based upper-limb motion assessment model: performance validation across various motion tasks","volume":"23","author":"Meng","year":"2023","journal-title":"IEEE Sensors J."},{"key":"B43","doi-asserted-by":"publisher","first-page":"554639","DOI":"10.3389\/frobt.2020.554639","article-title":"Inertial-robotic motion tracking in end-effector-based rehabilitation robots","volume":"7","author":"Passon","year":"2020","journal-title":"Front. Robotics AI"},{"key":"B44","volume-title":"Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke","author":"Prange","year":"2009"},{"key":"B45","doi-asserted-by":"publisher","first-page":"860","DOI":"10.1109\/tnsre.2020.2978381","article-title":"Estimating fugl-meyer upper extremity motor score from functional-connectivity measures","volume":"28","author":"Riahi","year":"2020","journal-title":"IEEE Trans. neural Syst. rehabilitation Eng."},{"key":"B46","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/s0140-6736(19)31055-4","article-title":"Robot assisted training for the upper limb after stroke (ratuls): a multicentre randomised controlled trial","volume":"394","author":"Rodgers","year":"2019","journal-title":"Lancet"},{"key":"B47","doi-asserted-by":"publisher","first-page":"3293","DOI":"10.1109\/tbme.2024.3416908","article-title":"Quantifying muscle co-activation for impaired finger Independence in stroke survivors","volume":"71","author":"Ruan","year":"2024","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"B48","first-page":"133","article-title":"Slope stability predictions using machine learning techniques","author":"Sahoo","year":"2022"},{"key":"B49","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1093\/jcde\/qwac139","article-title":"Multi-objective particle swarm optimization with dynamic population size","volume":"10","author":"Shu","year":"2023","journal-title":"J. Comput. Des. Eng."},{"key":"B50","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/s1474-4422(19)30415-6","article-title":"Advances and challenges in stroke rehabilitation","volume":"19","author":"Stinear","year":"2020","journal-title":"Lancet Neurology"},{"key":"B51","doi-asserted-by":"publisher","first-page":"3140","DOI":"10.3390\/s19143140","article-title":"Using step size and lower limb segment orientation from multiple low-cost wearable inertial\/magnetic sensors for pedestrian navigation","volume":"19","author":"Tjhai","year":"2019","journal-title":"Sensors"},{"key":"B52","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1177\/1545968320909796","article-title":"Machine learning methods predict individual upper-limb motor impairment following therapy in chronic stroke","volume":"34","author":"Tozlu","year":"2020","journal-title":"Neurorehabilitation neural repair"},{"key":"B53","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1007\/s00500-016-2474-6","article-title":"Particle swarm optimization algorithm: an overview","volume":"22","author":"Wang","year":"","journal-title":"Soft Comput."},{"key":"B54","doi-asserted-by":"publisher","first-page":"1750036","DOI":"10.1142\/s0129065717500368","article-title":"Classification of diffusion tensor metrics for the diagnosis of a myelopathic cord using machine learning","volume":"28","author":"Wang","year":"","journal-title":"Int. J. neural Syst."},{"key":"B55","doi-asserted-by":"publisher","first-page":"1470759","DOI":"10.3389\/fneur.2024.1470759","article-title":"Design of upper limb muscle strength assessment system based on surface electromyography signals and joint motion","volume":"15","author":"Wang","year":"","journal-title":"Front. Neurology"},{"key":"B56","doi-asserted-by":"publisher","first-page":"107775","DOI":"10.1016\/j.bspc.2025.107775","article-title":"Rehabilitation evaluation method and application for upper limb post-stroke based on improved dtw","volume":"106","author":"Wang","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"B57","doi-asserted-by":"publisher","first-page":"3722","DOI":"10.3390\/s24123722","article-title":"A method for predicting inertial navigation system positioning errors using a back propagation neural network based on a particle swarm optimization algorithm","volume":"24","author":"Wang","year":"","journal-title":"Sensors"},{"key":"B58","doi-asserted-by":"publisher","first-page":"7667","DOI":"10.3390\/s23187667","article-title":"The application of wearable sensors and machine learning algorithms in rehabilitation training: a systematic review","volume":"23","author":"Wei","year":"2023","journal-title":"Sensors"},{"key":"B59","doi-asserted-by":"publisher","first-page":"9275801","DOI":"10.1155\/2022\/9275801","article-title":"Prediction of bronchopneumonia inpatients\u2019 total hospitalization expenses based on bp neural network and support vector machine models","volume":"2022","author":"Wu","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"B60","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/s1474-4422(18)30500-3","article-title":"Stroke in China: advances and challenges in epidemiology, prevention, and management","volume":"18","author":"Wu","year":"2019","journal-title":"Lancet Neurology"},{"key":"B61","doi-asserted-by":"publisher","first-page":"e27941","DOI":"10.1016\/j.heliyon.2024.e27941","article-title":"Risk factors and prediction models for cardiovascular complications of hypertension in older adults with machine learning: a cross-sectional study","volume":"10","author":"Wu","year":"2024","journal-title":"Heliyon"},{"key":"B62","doi-asserted-by":"publisher","first-page":"411","DOI":"10.3390\/bios11110411","article-title":"An ultra-low power surface emg sensor for wearable biometric and medical applications","volume":"11","author":"Wu","year":"2021","journal-title":"Biosensors"},{"key":"B63","doi-asserted-by":"publisher","first-page":"4218","DOI":"10.1007\/s12035-020-02021-1","article-title":"A review of exercise-induced neuroplasticity in ischemic stroke: pathology and mechanisms","volume":"57","author":"Xing","year":"2020","journal-title":"Mol. Neurobiol."},{"key":"B64","doi-asserted-by":"publisher","first-page":"1606","DOI":"10.1111\/jch.14597","article-title":"Application of back propagation neural network model optimized by particle swarm algorithm in predicting the risk of hypertension","volume":"24","author":"Yan","year":"2022","journal-title":"J. Clin. Hypertens."},{"key":"B65","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1111\/mice.12315","article-title":"Prediction of bus travel time using random forests based on near neighbors","volume":"33","author":"Yu","year":"2018","journal-title":"Computer-Aided Civ. Infrastructure Eng."},{"key":"B66","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1111\/mice.12480","article-title":"Probability and interval hybrid reliability analysis based on adaptive local approximation of projection outlines using support vector machine","volume":"34","author":"Zhang","year":"2019","journal-title":"Computer-Aided Civ. Infrastructure Eng."},{"key":"B67","doi-asserted-by":"publisher","first-page":"1750015","DOI":"10.1142\/s0129065717500150","article-title":"A scalable weight-free learning algorithm for regulatory control of cell activity in spiking neuronal networks","volume":"28","author":"Zhang","year":"2018","journal-title":"Int. J. neural Syst."},{"key":"B68","doi-asserted-by":"publisher","first-page":"115096","DOI":"10.1016\/j.sna.2024.115096","article-title":"Accelerometer calibration based on improved particle swarm optimization algorithm of support vector machine","volume":"369","author":"Zhao","year":"2024","journal-title":"Sensors Actuators A Phys."}],"container-title":["Frontiers in Bioengineering and Biotechnology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbioe.2025.1619411\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T13:37:41Z","timestamp":1752154661000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbioe.2025.1619411\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,9]]},"references-count":68,"alternative-id":["10.3389\/fbioe.2025.1619411"],"URL":"https:\/\/doi.org\/10.3389\/fbioe.2025.1619411","relation":{},"ISSN":["2296-4185"],"issn-type":[{"value":"2296-4185","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,9]]},"article-number":"1619411"}}