Wearable　motion　capture　systems　offer　promising　avenues　for　human　lower　limb　rehabilitation.However,　unstable　data　transmission　and　attitude　estimation　challenge　their　practical　application.Aiming　at　this　problem,a　reliable　method　utilizing　wearable　inertial　sensors　for　rehabilitation　applications　is　innovatively　proposed　and　implemented　within　our　designed　wearable　motion　capture　system　tailored　to　patients　with　impaired　lower　limbs.Stable　data　transmission　process　based　on　star-type　bluetooth　body　sensor　networks　is　designed　by　establishing　a　connection　parameter　setting　method　to　guarantee　reliable　attitude　estimation.Then,　a　robust　attitude　estimating　method　based　on　improved　gradient　descent　method　is　proposed　to　promote　the　anti-interference　capability　of　the　algorithm　by　introducing　trust　coefficients.Lower　limb　motion　capture　experiments　are　conducted　and　results　show　that　the　proposed　method　enables　the　system　to　maintain　a　package　loss　rate　of　no　more　than　0.24%　and　has　a　maximum　coefficient　of　variation　of　5.9%　during　data　transmission　process.Attitude　estimation　reliability　experiments　reveal　that　the　proposed　algorithm　substantially　enhances　anti-interference　capabilities　while　preserving　estimation　accuracy.　Compared　to　the　state-of-the-art　method,　under　acceleration　shock,　estimation　errors　decrease　by　up　to　39.1%(roll),　42.9%(pitch),　and20.2%(yaw).　When　exposed　to　external　magnetic　field　interference,　conventional　estimation　algorithms　falter,　whereas　the　proposed　method　maintains　an　average　error　within　2°.　Significance　analysis　underscores　the　method’s　distinctiveness　at　the　0.05%　significance　level　(p<0.05).　This　study　effectively　bridges　the　gap　between　wearable　inertial　motion　capture　systems　and　their　application　in　clinical　lower　limb　rehabilitation.　IEEE