{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T11:56:33Z","timestamp":1779105393329,"version":"3.51.4"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T00:00:00Z","timestamp":1734480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52075177"],"award-info":[{"award-number":["52075177"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2021YFB3301400"],"award-info":[{"award-number":["2021YFB3301400"]}]},{"name":"Research Foundation of Guangdong Province","award":["2019A050505001"],"award-info":[{"award-number":["2019A050505001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Intel Serv Robotics"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11370-024-00576-9","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T12:33:52Z","timestamp":1734525232000},"page":"137-156","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Personalized passive training control strategy for a lower limb rehabilitation robot with specified step lengths"],"prefix":"10.1007","volume":"18","author":[{"given":"Shuoyu","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chupeng","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuangyuan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longhan","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,18]]},"reference":[{"key":"576_CR1","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s11370-024-00512-x","volume":"17","author":"R Barkataki","year":"2024","unstructured":"Barkataki R, Kalita Z, Kirtania S (2024) Anthropomorphic design and control of a polycentric knee exoskeleton for improved lower limb assistance. Intell Serv Robot 17:555\u2013577. https:\/\/doi.org\/10.1007\/s11370-024-00512-x","journal-title":"Intell Serv Robot"},{"key":"576_CR2","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s11370-023-00509-y","volume":"17","author":"MUA Khan","year":"2024","unstructured":"Khan MUA, Ali A, Muneer R, Faisal M (2024) Pneumatic artificial muscle-based stroke rehabilitation device for upper and lower limbs. Intell Serv Robot 17:33\u201342. https:\/\/doi.org\/10.1007\/s11370-023-00509-y","journal-title":"Intell Serv Robot"},{"key":"576_CR3","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1177\/02692155231164092","volume":"37","author":"C Charette","year":"2023","unstructured":"Charette C, Dery J, Blanchette AK, Faure C, Routhier F, Bouyer LJ, Lamontagne ME (2023) A systematic review of the determinants of implementation of a locomotor training program using a powered exoskeleton for individuals with a spinal cord injury. Clin Rehabil 37:1119\u20131138. https:\/\/doi.org\/10.1177\/02692155231164092","journal-title":"Clin Rehabil"},{"key":"576_CR4","doi-asserted-by":"publisher","DOI":"10.1186\/s12984-016-0162-5","author":"DR Louie","year":"2016","unstructured":"Louie DR, Eng JJ (2016) Powered robotic exoskeletons in post-stroke rehabilitation of gait: a scoping review. J Neuroeng Rehabil. https:\/\/doi.org\/10.1186\/s12984-016-0162-5","journal-title":"J Neuroeng Rehabil"},{"key":"576_CR5","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1260\/2040-2295.1.2.197","volume":"1","author":"R Riener","year":"2010","unstructured":"Riener R, Luenenburger L, Maier IC, Colombo G, Dietz V (2010) Locomotor training in subjects with sensori-motor deficits: an overview of the robotic gait orthosis Lokomat. J Healthc Eng 1:197\u2013215. https:\/\/doi.org\/10.1260\/2040-2295.1.2.197","journal-title":"J Healthc Eng"},{"key":"576_CR6","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/TNSRE.2008.2008288","volume":"17","author":"Y Stauffer","year":"2009","unstructured":"Stauffer Y, Allemand Y, Bouri M, Fournier J, Clavel R, Metrailler P, Brodard R, Reynard F (2009) The WalkTrainer-a new generation of walking reeducation device combining orthoses and muscle stimulation. IEEE Trans Neural Syst Rehabil Eng 17:38\u201345. https:\/\/doi.org\/10.1109\/TNSRE.2008.2008288","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"576_CR7","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1109\/TNSRE.2007.903919","volume":"15","author":"JF Veneman","year":"2007","unstructured":"Veneman JF, Kruidhof R, Hekman EEG, Ekkelenkamp R, Van Asseldonk EHF, van der Kooij H (2007) Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation. IEEE Trans Neural Syst Rehabil Eng 15:379\u2013386. https:\/\/doi.org\/10.1109\/TNSRE.2007.903919","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"576_CR8","doi-asserted-by":"publisher","first-page":"1984","DOI":"10.1109\/TNSRE.2020.3009317","volume":"28","author":"R Hidayah","year":"2020","unstructured":"Hidayah R, Bishop L, Jin X, Chamarthy S, Stein J, Agrawal SK (2020) Gait adaptation using a cable-driven active leg exoskeleton (C-ALEX) with post-stroke participants. IEEE Trans Neural Syst Rehabil Eng 28:1984\u20131993. https:\/\/doi.org\/10.1109\/TNSRE.2020.3009317","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"576_CR9","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1097\/PHM.0b013e318269d9a3","volume":"91","author":"A Esquenazi","year":"2012","unstructured":"Esquenazi A, Talaty M, Packel A, Saulino M (2012) The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury. Am J Phys Med Rehabil 91:911\u2013921. https:\/\/doi.org\/10.1097\/PHM.0b013e318269d9a3","journal-title":"Am J Phys Med Rehabil"},{"issue":"10","key":"576_CR10","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.3390\/brainsci12101311","volume":"12","author":"A Abdullahi","year":"2022","unstructured":"Abdullahi A, Wong TWL, Ng SSM (2022) Rehabilitation of severe impairment in motor function after stroke: suggestions for harnessing the potentials of mirror neurons and the mentalizing systems to stimulate recovery. Brain Sci 12(10):1311. https:\/\/doi.org\/10.3390\/brainsci12101311","journal-title":"Brain Sci"},{"key":"576_CR11","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/s00221-007-0930-3","volume":"181","author":"M Schubring-Giese","year":"2007","unstructured":"Schubring-Giese M, Molina-Luna K, Hertler B, Buitrago MM, Hanley DF, Luft AR (2007) Speed of motor re-learning after experimental stroke depends on prior skill. Exp Brain Res 181:359\u2013365. https:\/\/doi.org\/10.1007\/s00221-007-0930-3","journal-title":"Exp Brain Res"},{"key":"576_CR12","doi-asserted-by":"publisher","first-page":"104340","DOI":"10.1016\/j.mechmachtheory.2021.104340","volume":"162","author":"D Shi","year":"2021","unstructured":"Shi D, Zhang WX, Zhang W, Ju LH, Ding XL (2021) Human-centred adaptive control of lower limb rehabilitation robot based on human-robot interaction dynamic model. Mech Mach Theory 162:104340. https:\/\/doi.org\/10.1016\/j.mechmachtheory.2021.104340","journal-title":"Mech Mach Theory"},{"key":"576_CR13","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/s11370-023-00477-3","volume":"16","author":"J Narayan","year":"2023","unstructured":"Narayan J, Abbas M, Patel B, Dwivedy SK (2023) Adaptive RBF neural network-computed torque control for a pediatric gait exoskeleton system: an experimental study. Intell Serv Robot 16:549\u2013564. https:\/\/doi.org\/10.1007\/s11370-023-00477-3","journal-title":"Intell Serv Robot"},{"key":"576_CR14","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1109\/TNSRE.2008.2008280","volume":"17","author":"SK Banala","year":"2009","unstructured":"Banala SK, Kim SH, Agrawal SK, Scholz JP (2009) Robot assisted gait training with active leg exoskeleton (ALEX). IEEE Trans Neural Syst Rehabil Eng 17:2\u20138. https:\/\/doi.org\/10.1109\/TNSRE.2008.2008280","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"576_CR15","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.isatra.2017.01.006","volume":"67","author":"Y Long","year":"2017","unstructured":"Long Y, Du ZJ, Cong L, Wang WD, Zhang ZM, Dong W (2017) Active disturbance rejection control based human gait tracking for lower extremity rehabilitation exoskeleton. Isa Trans 67:389\u2013397. https:\/\/doi.org\/10.1016\/j.isatra.2017.01.006","journal-title":"Isa Trans"},{"key":"576_CR16","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1109\/TMRB.2022.3194360","volume":"4","author":"SS Zhang","year":"2022","unstructured":"Zhang SS, Guan X, Ye J, Chen G, Zhang ZM, Leng YQ (2022) Gait deviation correction method for gait rehabilitation with a lower limb exoskeleton robot. IEEE Trans Med Robot Bionics 4:754\u2013763. https:\/\/doi.org\/10.1109\/TMRB.2022.3194360","journal-title":"IEEE Trans Med Robot Bionics"},{"key":"576_CR17","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/j.gaitpost.2013.08.028","volume":"39","author":"TP Luu","year":"2014","unstructured":"Luu TP, Low KH, Qu XD, Lim HB, Hoon KH (2014) An individual-specific gait pattern prediction model based on generalized regression neural networks. Gait Posture 39:443\u2013448. https:\/\/doi.org\/10.1016\/j.gaitpost.2013.08.028","journal-title":"Gait Posture"},{"key":"576_CR18","doi-asserted-by":"publisher","first-page":"817446","DOI":"10.3389\/fnbot.2021.817446","volume":"15","author":"Z Guo","year":"2022","unstructured":"Guo Z, Ye J, Zhang SS, Xu LS, Chen G, Guan X, Li YQ, Zhang ZM (2022) Effects of individualized gait rehabilitation robotics for gait training on hemiplegic patients: before-after study in the same person. Front Neurorobot 15:817446. https:\/\/doi.org\/10.3389\/fnbot.2021.817446","journal-title":"Front Neurorobot"},{"key":"576_CR19","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1109\/TNSRE.2008.2008278","volume":"17","author":"H Vallery","year":"2009","unstructured":"Vallery H, van Asseldonk EHF, Buss M, van der Kooij H (2009) Reference trajectory generation for rehabilitation robots: complementary limb motion estimation. IEEE Trans Neural Syst Rehabil Eng 17:23\u201330. https:\/\/doi.org\/10.1109\/TNSRE.2008.2008278","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"576_CR20","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.jbiomech.2013.09.032","volume":"47","author":"Y Yun","year":"2014","unstructured":"Yun Y, Kim HC, Shin SY, Lee J, Deshpande AD, Kim C (2014) Statistical method for prediction of gait kinematics with gaussian process regression. J Biomech 47:186\u2013192. https:\/\/doi.org\/10.1016\/j.jbiomech.2013.09.032","journal-title":"J Biomech"},{"key":"576_CR21","doi-asserted-by":"publisher","first-page":"15597","DOI":"10.1007\/s12652-019-01390-3","volume":"14","author":"SX Ren","year":"2023","unstructured":"Ren SX, Wang WQ, Hou ZG, Chen BD, Liang X, Wang JX, Peng L (2023) Personalized gait trajectory generation based on anthropometric features using random forest. J Ambient Intell Humaniz Comput 14:15597\u201315608. https:\/\/doi.org\/10.1007\/s12652-019-01390-3","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"576_CR22","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1109\/TNSRE.2020.3045425","volume":"29","author":"ZK Zhou","year":"2021","unstructured":"Zhou ZK, Liang BH, Huang GW, Liu B, Nong JJ, Xie LH (2021) Individualized gait generation for rehabilitation robots based on recurrent neural networks. IEEE Trans Neural Syst Rehabil Eng 29:273\u2013281. https:\/\/doi.org\/10.1109\/TNSRE.2020.3045425","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"576_CR23","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1109\/TNSRE.2019.2914095","volume":"27","author":"J Hong","year":"2019","unstructured":"Hong J, Chun C, Kim SJ, Park FC (2019) Gaussian process trajectory learning and synthesis of individualized gait motions. IEEE Trans Neural Syst Rehabil Eng 27:1236\u20131245. https:\/\/doi.org\/10.1109\/TNSRE.2019.2914095","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"576_CR24","doi-asserted-by":"publisher","first-page":"33401","DOI":"10.1007\/s11042-023-14733-2","volume":"82","author":"VB Semwal","year":"2023","unstructured":"Semwal VB, Jain R, Maheshwari P, Khatwani S (2023) Gait reference trajectory generation at different walking speeds using LSTM and CNN. Multimed Tools Appl 82:33401\u201333419. https:\/\/doi.org\/10.1007\/s11042-023-14733-2","journal-title":"Multimed Tools Appl"},{"key":"576_CR25","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.1109\/TASE.2018.2841358","volume":"15","author":"XY Wu","year":"2018","unstructured":"Wu XY, Liu DX, Liu M, Chen CJ, Guo HW (2018) Individualized gait pattern generation for sharing lower limb exoskeleton robot. IEEE Trans Autom Sci Eng 15:1459\u20131470. https:\/\/doi.org\/10.1109\/TASE.2018.2841358","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"576_CR26","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1109\/LRA.2020.3006818","volume":"6","author":"CB Zou","year":"2021","unstructured":"Zou CB, Huang R, Cheng H, Qiu J (2021) Learning gait models with varying walking speeds. IEEE Robot Autom Lett 6:183\u2013190. https:\/\/doi.org\/10.1109\/LRA.2020.3006818","journal-title":"IEEE Robot Autom Lett"},{"key":"576_CR27","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.gaitpost.2010.06.013","volume":"32","author":"DD Espy","year":"2010","unstructured":"Espy DD, Yang F, Bhatt T, Pai YC (2010) Independent influence of gait speed and step length on stability and fall risk. Gait Posture 32:378\u2013382. https:\/\/doi.org\/10.1016\/j.gaitpost.2010.06.013","journal-title":"Gait Posture"},{"key":"576_CR28","doi-asserted-by":"publisher","first-page":"110052","DOI":"10.1016\/j.jbiomech.2020.110052","volume":"112","author":"XY Hu","year":"2020","unstructured":"Hu XY, Shen F, Zhao Z, Qu XD, Ye J (2020) An individualized gait pattern prediction model based on the least absolute shrinkage and selection operator regression. J Biomech 112:110052. https:\/\/doi.org\/10.1016\/j.jbiomech.2020.110052","journal-title":"J Biomech"},{"key":"576_CR29","doi-asserted-by":"crossref","unstructured":"McGrath RL, Pires-Fernandes M, Knarr B, Higginson JS, Sergi F (2017) Toward goal-oriented robotic gait training: the effect of gait speed and stride length on lower extremity joint torques. In: 2017 International conference on rehabilitation robotics (ICORR):270\u2013275","DOI":"10.1109\/ICORR.2017.8009258"},{"key":"576_CR30","doi-asserted-by":"publisher","first-page":"102974","DOI":"10.1016\/j.bspc.2021.102974","volume":"70","author":"K Racz","year":"2021","unstructured":"Racz K, Kiss RM (2021) Marker displacement data filtering in gait analysis: a technical note. Biomed Signal Process Control 70:102974. https:\/\/doi.org\/10.1016\/j.bspc.2021.102974","journal-title":"Biomed Signal Process Control"},{"key":"576_CR31","doi-asserted-by":"publisher","first-page":"101029","DOI":"10.1016\/j.seta.2021.101029","volume":"44","author":"B Du","year":"2021","unstructured":"Du B, Lund PD, Wang J, Kolhe M, Hu E (2021) Comparative study of modelling the thermal efficiency of a novel straight through evacuated tube collector with MLR, SVR, BP and RBF methods. Sustain Energy Technol Assess 44:101029. https:\/\/doi.org\/10.1016\/j.seta.2021.101029","journal-title":"Sustain Energy Technol Assess"},{"key":"576_CR32","doi-asserted-by":"publisher","first-page":"1599","DOI":"10.1007\/s00704-020-03484-x","volume":"143","author":"M Taki","year":"2021","unstructured":"Taki M, Rohani A, Yildizhan H (2021) Application of machine learning for solar radiation modeling. Theor Appl Climatol 143:1599\u20131613. https:\/\/doi.org\/10.1007\/s00704-020-03484-x","journal-title":"Theor Appl Climatol"},{"key":"576_CR33","doi-asserted-by":"publisher","first-page":"103115","DOI":"10.1016\/j.bspc.2021.103115","volume":"71","author":"WT Li","year":"2022","unstructured":"Li WT, Liu KP, Sun ZB, Li CX, Chai YY, Gu J (2022) A neural network-based model for lower limb continuous estimation against the disturbance of uncertainty*. Biomed Signal Process Control 71:103115. https:\/\/doi.org\/10.1016\/j.bspc.2021.103115","journal-title":"Biomed Signal Process Control"},{"key":"576_CR34","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s11370-023-00459-5","volume":"16","author":"JH Wang","year":"2023","unstructured":"Wang JH, Kim JY (2023) Development of a whole-body walking rehabilitation robot and power assistive method using EMG signals. Intell Serv Robot 16:139\u2013153. https:\/\/doi.org\/10.1007\/s11370-023-00459-5","journal-title":"Intell Serv Robot"},{"key":"576_CR35","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.gaitpost.2019.07.500","volume":"73","author":"CA Fukuchi","year":"2019","unstructured":"Fukuchi CA, Fukuchi RK, Duarte M (2019) Test of two prediction methods for minimum and maximum values of gait kinematics and kinetics data over a range of speeds. Gait Posture 73:269\u2013272. https:\/\/doi.org\/10.1016\/j.gaitpost.2019.07.500","journal-title":"Gait Posture"},{"key":"576_CR36","doi-asserted-by":"publisher","first-page":"7367","DOI":"10.1109\/TCYB.2023.3253181","volume":"53","author":"GX Li","year":"2023","unstructured":"Li GX, Li ZJ, Su CY, Xu T (2023) Active human-following control of an exoskeleton robot with body weight support. IEEE T Cybern 53:7367\u20137379. https:\/\/doi.org\/10.1109\/TCYB.2023.3253181","journal-title":"IEEE T Cybern"},{"key":"576_CR37","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/TCST.2023.3305913","volume":"32","author":"J Casas","year":"2024","unstructured":"Casas J, Chang CH, Duenas VH (2024) Switched concurrent learning adaptive control for treadmill walking using a lower limb hybrid exoskeleton. IEEE Trans Control Syst Technol 32:174\u2013188. https:\/\/doi.org\/10.1109\/TCST.2023.3305913","journal-title":"IEEE Trans Control Syst Technol"}],"container-title":["Intelligent Service Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11370-024-00576-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11370-024-00576-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11370-024-00576-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T21:35:59Z","timestamp":1739309759000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11370-024-00576-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,18]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["576"],"URL":"https:\/\/doi.org\/10.1007\/s11370-024-00576-9","relation":{},"ISSN":["1861-2776","1861-2784"],"issn-type":[{"value":"1861-2776","type":"print"},{"value":"1861-2784","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,18]]},"assertion":[{"value":"17 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The ethical approval and informed consent of all participants have been obtained, and the Ethics Committee of the Guangzhou First People\u2019s Hospital Department has approved all ethical and experimental procedures for the research. All studies strictly adhered to the relevant provisions of the Helsinki Declaration.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}