End-to-End Control of a Powered Knee-Ankle Prosthesis Towards Unified, Tuning-Free Assistance
Overview
This research presents the real-time deployment of an end-to-end controller for a powered knee-ankle prosthesis that eliminates the need for manual tuning and intent classifiers.
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · percent: 0.85 other · Basis not reported · Differing source assertions
“e torque with walking speed (deployed 0.85 Nm/kg per m/s, p = 0.001; training 0.96 Nm/kg per m/s, 95% CI [0.42, 1.50], p = 0.002), after excluding one outlier traced to atypical prosthesis loading. During ramp ascent, the controller scaled knee pre-flexion with grade (deployed 2.92 deg/deg, p = 0.027; training 3.30 deg/deg, 95% CI [1.83, 4.77], p < 0.001). During ramp descent, the controller increased resistive knee”
Exact source · revision 1Source owner not reported
Reported quantity · percent: 0.96 other · Basis not reported · Differing source assertions
“e torque with walking speed (deployed 0.85 Nm/kg per m/s, p = 0.001; training 0.96 Nm/kg per m/s, 95% CI [0.42, 1.50], p = 0.002), after excluding one outlier traced to atypical prosthesis loading. During ramp ascent, the controller scaled knee pre-flexion with grade (deployed 2.92 deg/deg, p = 0.027; training 3.30 deg/deg, 95% CI [1.83, 4.77], p < 0.001). During ramp descent, the controller increased resistive knee”
Exact source · revision 1Source owner not reported
Reported quantity · percent: 2.92 other · Basis not reported · Differing source assertions
“e torque with walking speed (deployed 0.85 Nm/kg per m/s, p = 0.001; training 0.96 Nm/kg per m/s, 95% CI [0.42, 1.50], p = 0.002), after excluding one outlier traced to atypical prosthesis loading. During ramp ascent, the controller scaled knee pre-flexion with grade (deployed 2.92 deg/deg, p = 0.027; training 3.30 deg/deg, 95% CI [1.83, 4.77], p < 0.001). During ramp descent, the controller increased resistive knee”
Exact source · revision 1Source owner not reported
Reported quantity · percent: 3.3 other · Basis not reported · Differing source assertions
“e torque with walking speed (deployed 0.85 Nm/kg per m/s, p = 0.001; training 0.96 Nm/kg per m/s, 95% CI [0.42, 1.50], p = 0.002), after excluding one outlier traced to atypical prosthesis loading. During ramp ascent, the controller scaled knee pre-flexion with grade (deployed 2.92 deg/deg, p = 0.027; training 3.30 deg/deg, 95% CI [1.83, 4.77], p < 0.001). During ramp descent, the controller increased resistive knee”
Exact source · revision 1Source owner not reported
Reported quantity · percent: 0.16 other · Basis not reported · Differing source assertions
“torque relative to level walking (deployed +0.16 Nm/kg, p < 0.001; training +0.16 Nm/kg, p = 0.008). Seamless stair transitions were generated for both intact- and prosthetic-side-leading sequences in ascent and descent, despite the training data containing only one limb-leading sequence. These results provide initial evidence towards end-to-end control that can provide unified, mode-adaptive prosthetic assistance wi”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalEnd-to-End Control of a Powered Knee-Ankle Prosthesis Towards Unified, Tuning-Free Assistance
This research presents the real-time deployment of an end-to-end controller for a powered knee-ankle prosthesis that eliminates the need for manual tuning and intent classifiers.
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