More Like this

1. Real-time gait metric estimation for everyday gait training with wearable devices in people poststroke

   https://doi.org/10.1017/wtc.2020.11  

   Arens, Philipp ; Siviy, Christopher ; Bae, Jaehyun ; Choe, Dabin K. ; Karavas, Nikos ; Baker, Teresa ; Ellis, Terry D. ; Awad, Louis N. ; Walsh, Conor J. ( January 2021 , Wearable Technologies)

   null (Ed.)

   Abstract Hemiparetic walking after stroke is typically slow, asymmetric, and inefficient, significantly impacting activities of daily living. Extensive research shows that functional, intensive, and task-specific gait training is instrumental for effective gait rehabilitation, characteristics that our group aims to encourage with soft robotic exosuits. However, standard clinical assessments may lack the precision and frequency to detect subtle changes in intervention efficacy during both conventional and exosuit-assisted gait training, potentially impeding targeted therapy regimes. In this paper, we use exosuit-integrated inertial sensors to reconstruct three clinically meaningful gait metrics related to circumduction, foot clearance, and stride length. Our method corrects sensor drift using instantaneous information from both sides of the body. This approach makes our method robust to irregular walking conditions poststroke as well as usable in real-time applications, such as real-time movement monitoring, exosuit assistance control, and biofeedback. We validate our algorithm in eight people poststroke in comparison to lab-based optical motion capture. Mean errors were below 0.2 cm (9.9%) for circumduction, −0.6 cm (−3.5%) for foot clearance, and 3.8 cm (3.6%) for stride length. A single-participant case study shows our technique’s promise in daily-living environments by detecting exosuit-induced changes in gait while walking in a busy outdoor plaza. 

   more » « less
2. From Learning Gait Signatures of Many Individuals to Reconstructing Gait Dynamics of One Single Individual

   https://doi.org/10.3389/fams.2020.564935  

   Hsieh, Fushing ; Wang, Xiaodong ( November 2020 , Frontiers in Applied Mathematics and Statistics)

   null (Ed.)
3. 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. 

   more » « less
4. Toward goal-oriented robotic gait training: The effect of gait speed and stride length on lower extremity joint torques

   https://doi.org/10.1109/ICORR.2017.8009258  

   McGrath, Robert L. ; Pires-Fernandes, Margaret ; Knarr, Brian ; Higginson, Jill S. ; Sergi, Fabrizio ( July 2017 , IEEE International Conference on Rehabiliation Robotics)

   Robot-assisted gait training is becoming increasingly common to support recovery of walking function after neurological injury. How to formulate controllers capable of promoting desired features in gait, i.e. goals, is complicated by the limited understanding of the human response to robotic input. A possible method to formulate controllers for goal-oriented gait training is based on the analysis of the joint torques applied by healthy subjects to modulate such goals. The objective of this work is to understand how sagittal plane joint torque is affected by two important gait parameters: gait speed (GS) and stride length (SL). We here present the results obtained from healthy subjects walking on a treadmill at different speeds, and asked to modulate stride length via visual feedback. Via principal component analysis, we extracted the global effects of the two factors on the peak-to-peak amplitude of joint torques. Next, we used a torque pulse approximation analysis to determine optimal timing and amplitude of torque pulses that approximate the SL-specific difference in joint torque profiles measured at different values of GS. Our results show a strong effect of GS on the torque profiles in all joints considered. In contrast, SL mostly affects the torque produced at the knee joint at early and late stance, with smaller effects on the hip and ankle joints. Our analysis generated a set of torque assistance profiles that will be experimentally tested using gait training robots. 

   more » « less
5. Functional resistance training methods for targeting patient-specific gait deficits: A review of devices and their effects on muscle activation, neural control, and gait mechanics

   https://doi.org/10.1016/j.clinbiomech.2022.105629  

   Washabaugh, Edward P. ; Krishnan, Chandramouli ( April 2022 , Clinical Biomechanics)