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1. Human Gait Analysis Metric for Gait Retraining

   https://doi.org/10.1155/2019/1286864  

   Ramakrishnan, Tyagi; Kim, Seok Hun; Reed, Kyle B. (November 2019, Applied Bionics and Biomechanics)

   The combined gait asymmetry metric (CGAM) provides a method to synthesize human gait motion. The metric is weighted to balance each parameter’s effect by normalizing the data so all parameters are more equally weighted. It is designed to combine spatial, temporal, kinematic, and kinetic gait parameter asymmetries. It can also combine subsets of the different gait parameters to provide a more thorough analysis. The single number quantifying gait could assist robotic rehabilitation methods to optimize the resulting gait patterns. CGAM will help define quantitative thresholds for achievable balanced overall gait asymmetry. The study presented here compares the combined gait parameters with clinical measures such as timed up and go (TUG), six-minute walk test (6MWT), and gait velocity. The comparisons are made on gait data collected on individuals with stroke before and after twelve sessions of rehabilitation. Step length, step time, and swing time showed a strong correlation to CGAM, but the double limb support asymmetry has nearly no correlation with CGAM and ground reaction force asymmetry has a weak correlation. The CGAM scores were moderately correlated with TUG and strongly correlated to 6MWT and gait velocity. 

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2. Interaction of Multiple Gait Rehabilitation Techniques

   https://doi.org/10.1016/j.apmr.2019.10.074  

   Rasouli, Fatemeh; Kim, Seok Hun; Reed, Kyle (December 2019, Archives of Physical Medicine and Rehabilitation)
3. Consensus Paper: Ataxic Gait

   https://doi.org/10.1007/s12311-022-01373-9  

   Cabaraux, Pierre; Agrawal, Sunil K.; Cai, Huaying; Calabro, Rocco Salvatore; Casali, Carlo; Damm, Loic; Doss, Sarah; Habas, Christophe; Horn, Anja K.; Ilg, Winfried; et al (April 2022, The Cerebellum)
4. Robot-Assisted Targeted Gait Training

   https://doi.org/10.5604/01.3001.0053.9679  

   Chambers, Vaughn; Johnson, Madison; Artemiadis, Panagiotis (October 2023, Journal of Kinesiology and Exercise Sciences)

   Background: Millions of people are affected yearly by “runner’s knee” and osteoarthritis, which is thought to be related to impact force. Millions are also affected by chronic falling, who are usually both difficult to identify and train. While at first glance, these topics seem to be entirely disconnected, there appears to be a need for a device that would address both issues. This paper proposes and investigates the use of the Variable Stiffness Treadmill (VST) as a targeted training device for the different populations described above. Materials and Methods: The VST is the authors’ unique robotic split-belt treadmill that can reduce the vertical ground stiffness of the left belt, while the right belt remains rigid. In this work, heart rate and energy expenditure are measured for healthy subjects in the challenging asymmetric environment created by the VST and compared to a traditional treadmill setting. Results: This study shows that this asymmetric environment results in an increase in heart rate and energy expenditure, an increase in activity in the muscles about the hip and knee, and a decrease in impact force at heel strike. Conclusions: Compliant environments, like those created on the VST, may be a beneficial tool as they can: reduce high-impact forces during running and walking, significantly engage the muscles surrounding the hip and knee allowing for targeted training and rehabilitation, and assist in identifying and training high fall-risk individuals. 

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5. Enhancing Maneuverability via Gait Design

   https://doi.org/10.1109/ICRA46639.2022.9812061  

   Deng, Siming; Hatton, Ross L.; Cowan, Noah J. (January 2022, International Conference on Robotics and Automation)

   The gaits of locomoting systems are typically designed to maximize some sort of efficiency, such as cost of transport or speed. Equally important is the ability to modulate such a gait to effect turning maneuvers. For drag-dominated systems, geometric mechanics provides an elegant and practical framework for both ends—gait design and gait modulation. Within this framework, “constraint curvature” maps can be used to approximate the net displacement of robotic systems over cyclic gaits. Gait optimization is made possible under a previously reported “soap-bubble” algorithm. In this work, we propose both local and global gait morphing algorithms to modify a nominal gait to provide single-parameter steering control. Using a simplified swimmer, we numerically compare the two approaches and show that for modest turns, the local approach, while suboptimal, nevertheless proves effective for steering control. A potential advantage of the local approach is that it can be readily applied to soft robots or other systems where local approximations to the constraint curvature can be garnered from data, but for which obtaining an exact global model is infeasible. 

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