{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T08:33:27Z","timestamp":1778747607000,"version":"3.51.4"},"reference-count":48,"publisher":"ASME International","issue":"1","license":[{"start":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T00:00:00Z","timestamp":1730764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Recent advances in robotics and artificial intelligence have highlighted the potential for the integration of computational intelligence in enhancing the functionality and adaptability of robotic systems, particularly in rehabilitation. Designing robotic exoskeletons for the lower limb rehabilitation of post-stroke patients requires frequent adjustments to accommodate individual differences in leg anatomy. This complex engineering challenge necessitates a deep understanding of human physiology, robotics, and optimization to develop adaptive robotic systems and also to swiftly quantify the required adjustments and implement them for each patient. The conventional approaches, which mostly rely on heuristics and manual tuning, often struggle to achieve optimal results. This paper presents a novel method that integrates a genetic algorithm with a deep learning approach to generate a gait trajectory of the ankle joint from a six-bar linkage mechanism of fixed dimensions. Later, using the same approach, the inverse kinematics solution for this mechanism is also devised whereby, the set of the link dimensions of the six-bar linkage mechanism is obtained for the given gait trajectory of an individual to achieve customization. We simulated the kinematic behavior of the six-bar linkage mechanism within defined mechanical constraints and utilized the generated data for training a feedforward neural network and long short-term memory models. The proposed model, when trained, can produce accurate lengths for the desired gait trajectories in the sagittal plane and vice versa, which further validates our proposed approach for inverse kinematics solution. Moreover, to evaluate the efficiency of deep learning models, we have conducted an extensive error-based, comparative, and sensitivity analysis using different performance indices. The results highlight the potential of the proposed deep-learning-driven approach in the design analysis of gait rehabilitation robots.<\/jats:p>","DOI":"10.1115\/1.4066859","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T14:13:32Z","timestamp":1728915212000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":15,"title":["Deep Learning-Driven Analysis of a Six-Bar Mechanism for Personalized Gait Rehabilitation"],"prefix":"10.1115","volume":"25","author":[{"given":"Naveed Ahmad","family":"Khan","sequence":"first","affiliation":[{"name":"University of Canberra School of Information Technology and Systems, , Canberra, ACT 2617 ,","place":["Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shahid","family":"Hussain","sequence":"additional","affiliation":[{"name":"University of Canberra School of Information Technology and Systems, , Canberra, ACT 2617 ,","place":["Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wayne","family":"Spratford","sequence":"additional","affiliation":[{"name":"University of Canberra Research Institute of Sport and Exercise, , Canberra, ACT 2617 ,","place":["Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roland","family":"Goecke","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/03r8z3t63","id-type":"ROR","asserted-by":"publisher"}],"name":"UNSW Sydney School of Systems and Computing, , Canberra, ACT 2612 ,","place":["Australia"]},{"name":"University of New South Wales School of Systems and Computing, , Canberra, ACT 2612 ,","place":["Australia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ketan","family":"Kotecha","sequence":"additional","affiliation":[{"name":"Symbiosis International University Symbiosis Centre for Applied Artificial Intelligence, Symbiosis Institute of Technology, , Pune, Maharashtra 411045 ,","place":["India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prashant K.","family":"Jamwal","sequence":"additional","affiliation":[{"name":"Nazarbayev University Department of Electrical and Computer Engineering, , Astana 010000 ,","place":["Kazakhstan"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"33","published-online":{"date-parts":[[2024,11,5]]},"reference":[{"issue":"10","key":"2024110516040769700_CIT0001","doi-asserted-by":"publisher","first-page":"106022","DOI":"10.1016\/j.jstrokecerebrovasdis.2021.106022","article-title":"Home-Based Interventions May Increase Recruitment, Adherence, and Measurement of Outcomes in Clinical Trials of Stroke Rehabilitation","volume":"30","author":"de Menezes","year":"2021","journal-title":"J. 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