Assessing bipedal locomotion: towards replicable benchmarks for robotic and robot-assisted locomotion.

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Original Research
13 February 2018
Comparing Gait with Multiple Physical Asymmetries Using Consolidated Metrics
Tyagi Ramakrishnan
1 more and 
Kyle B. Reed
Comparing variation of mean, standard deviation, and CGAM metric among perturbations. (A) Normal walking without any alterations, (B) walking with SS or the variable stiffness and damping knee orthosis, (C) walking with big leg length and no weight addition at the ankles, and (D) walking with big leg length and weight. SL, step length; ST, step time; GRF, ground reaction forces; PF, push off forces; BF, braking forces; KA, knee angle; AA, ankle angle; HA, hip angle; AM, ankle moment; KM, knee moment, and HM, hip moment.

Physical changes such as leg length discrepancy, the addition of a mass at the distal end of the leg, the use of a prosthetic, and stroke frequently result in an asymmetric gait. This paper presents a metric that can potentially serve as a benchmark to categorize and differentiate between multiple asymmetric bipedal gaits. The combined gait asymmetry metric (CGAM) is based on modified Mahalanobis distances, and it utilizes the asymmetries of gait parameters obtained from motion capture and force data recorded during human walking. The gait parameters that were used in this analysis represent spatio-temporal, kinematic, and kinetic parameters. This form of a consolidated metric will help researchers identify overall gait asymmetry by showing them if the overall gait symmetry is improving and avoid the case where one parameter's symmetry is improving while another is getting worse. The CGAM metric successfully served as a measure for overall symmetry with eleven different gait parameters and successfully showed differences among gait with multiple physical asymmetries. The results showed that mass at the distal end had a larger magnitude on overall gait asymmetry compared to leg length discrepancy. It also showed that the combined effects are varied based on the cancelation effect between gait parameters. The metric was also successful in delineating the differences of prosthetic gait and able-bodied gait at three different walking velocities.

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Frontiers in Robotics and AI

Advancements in AI-driven Multimodal Interfaces for Robot-Aided Rehabilitation
Edited by Kevin Langlois, LOREDANA ZOLLO, David Rodriguez Cianca
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