{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T18:31:27Z","timestamp":1789497087935,"version":"build-2803163510"},"reference-count":26,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Proc Inst Mech Eng H"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:p>\n                    This study evaluates the reliability of an underactuated wearable lower-limb exoskeleton designed to assist with gait rehabilitation. Recognizing the complexity of system reliability, a deep learning framework augmented with Long short-term Memory (LSTM) was utilized for the time-dependent reliability analysis of dynamic systems. The research commenced with the development of a lower-limb gait robot, modeled on a Stephenson III six-bar linkage mechanism. Following the mechanical design, computer-aided design (CAD) tools were employed to conceptualize a lower-limb robotic exoskeleton for rehabilitation purposes. The design incorporated two metallic materials (aluminum and steel), and a composite material (carbon fiber) tested using SolidWorks\n                    <jats:sup>\u00ae<\/jats:sup>\n                    . The prototype achieved a lightweight design (~1.63\u2009kg) for carbon fiber material. An LSTM-enhanced deep neural network algorithm was implemented to predict the time-dependent reliability of joint displacements and end-effector trajectories. Finally, conditional probability methods were applied to complete the time-dependent system reliability assessment. The designed mechanical system for gait rehabilitation demonstrated high reliability (\n                    <jats:italic>R<\/jats:italic>\n                    \u2009\u2248\u20090.87). Over 200 simulation runs, reliability trends showed consistent and robust predictions.\n                  <\/jats:p>","DOI":"10.1177\/09544119251349362","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T05:27:56Z","timestamp":1751866076000},"page":"656-665","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep learning based time-dependent reliability analysis of an underactuated lower-limb robot exoskeleton for gait rehabilitation"],"prefix":"10.1177","volume":"239","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6580-7003","authenticated-orcid":false,"given":"Fahad","family":"Hussain","sequence":"first","affiliation":[{"name":"School of Information Technology and Systems, University of Canberra, Canberra, ACT, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tanishka","family":"Goyal","sequence":"additional","affiliation":[{"name":"School of Information Technology and Systems, University of Canberra, Canberra, ACT, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4352-0212","authenticated-orcid":false,"given":"Shahid","family":"Hussain","sequence":"additional","affiliation":[{"name":"School of Information Technology and Systems, University of Canberra, Canberra, ACT, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1330-6186","authenticated-orcid":false,"given":"Prashant","family":"Jamwal","sequence":"additional","affiliation":[{"name":"School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roland","family":"Goecke","sequence":"additional","affiliation":[{"name":"School of Systems and Computing, University of New South Wales, Canberra, ACT, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,7,7]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2006.871087"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11030388"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2020.12.010"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2864352"},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","unstructured":"Hussain F Goecke R Mohammadian M. 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