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1. A Data-Driven Predictive Model of Individual-Specific Effects of FES on Human Gait Dynamics

   Drnach L., Allen ( May 2019 , International Conference on Robotics and Automation (ICRA))

   Modeling individual-specific gait dynamics based on kinematic data could aid development of gait rehabilitation robotics by enabling robots to predict the user’s gait kinematics with and without external inputs, such as mechanical or electrical perturbations. Here we address a current limitation of data driven gait models, which do not yet predict human responses to perturbations. We used Switched Linear Dynamical Systems (SLDS) to model joint angle kinematic data from healthy individuals walking on a treadmill during normal gait and during gait perturbed by functional electrical stimulation (FES) to the ankle muscles. Our SLDS models were able to predict the time-evolution of joint kinematics in each of four gait phases, as well as across an entire gait cycle. Because the SLDS dynamics matrices encoded significant coupling across joints, we compared the SLDS predictions to that of a kinematic model, where the joint angles were independent. Gait kinematics predicted by SLDS and kinematic models were similar over time horizons of a few milliseconds, but SLDS models provided better predictions of gait kinematics over time horizons of up to a second. We also demonstrated that SLDS models can infer and predict individual-specific responses to FES during swing phase. As such, SLDS models may be a promising approach for online estimation and control of and human gait dynamics, allowing robotic control strategies to be tailored to an individual’s specific gait coordination patterns. 

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2. Identifying Gait Phases from Joint Kinematics during Walking with Switched Linear Dynamical Systems*

   https://doi.org/10.1109/BIOROB.2018.8487216  

   Drnach, Luke ; Essa, Irfan ; Ting, Lena H. ( October 2018 , 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob))

   Human-robot interaction (HRI) for gait rehabilitation would benefit from models of data-driven gait models that account for gait phases and gait dynamics. Here we address the current limitation in gait models driven by kinematic data, which do not model interlimb gait dynamics and have not been shown to precisely identify gait events. We used Switched Linear Dynamical Systems (SLDS) to model joint angle kinematic data from healthy individuals walking on a treadmill with normal gaits and with gaits perturbed by electrical stimulation. We compared the model-inferred gait phases to gait phases measured externally via a force plate. We found that SLDS models accounted for over 88% of the variation in each joint angle and labeled the joint kinematics with the correct gait phase with 84% precision on average. The transitions between hidden states matched measured gait events, with a median absolute difference of 25ms. To our knowledge, this is the first time that SLDS inferred gait phases have been validated by an external measure of gait, instead of against predefined gait phase durations. SLDS provide individual-specific representations of gait that incorporate both gait phases and gait dynamics. SLDS may be useful for developing control policies for HRI aimed at improving gait by allowing for changes in control to be precisely timed to different gait phases. 

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3. Kinematic Trajectories in Response to Speed Perturbations in Walking Suggest Modular Task-Level Control of Leg Angle and Length

   https://doi.org/10.1093/icb/icac057  

   Schwaner, M. J. ; Nishikawa, K. C. ; Daley, M. A. ( May 2022 , Integrative And Comparative Biology)

   Navigating complex terrains requires dynamic interactions between the substrate, musculoskeletal, and sensorimotor systems. Current perturbation studies have mostly used visible terrain height perturbations, which do not allow us to distinguish among the neuromechanical contributions of feedforward control, feedback-mediated, and mechanical perturbation responses. Here, we use treadmill-belt speed perturbations to induce a targeted perturbation to foot speed only, and without terrain-induced changes in joint posture and leg loading at stance onset. Based on previous studies suggesting a proximo-distal gradient in neuromechanical control, we hypothesized that distal joints would exhibit larger changes in joint kinematics, compared to proximal joints. Additionally, we expected birds to use feedforward strategies to increase the intrinsic stability of gait. To test these hypotheses, seven adult guinea fowl were video recorded while walking on a motorized treadmill, during both steady and perturbed trials. Perturbations consisted of repeated exposures to a deceleration and acceleration of the treadmill-belt speed. Surprisingly, we found that joint angular trajectories and center of mass fluctuations remain very similar, despite substantial perturbation of foot velocity by the treadmill belt. Hip joint angular trajectories exhibit the largest changes, with the birds adopting a slightly more flexed position across all perturbed strides. Additionally, we observed increased stride duration across all strides, consistent with feedforward changes in the control strategy. The speed perturbations mainly influenced the timing of stance and swing, with the largest kinematic changes in the strides directly following a deceleration. Our findings do not support the general hypothesis of a proximo-distal gradient in joint control, as distal joint kinematics remain largely unchanged. Instead, we find that leg angular trajectory and the timing of stance and swing are most sensitive to this specific perturbation, and leg length actuation remains largely unchanged. Our results are consistent with modular task-level control of leg length and leg angle actuation, with different neuromechanical control and perturbation sensitivity in each actuation mode. Distal joints appear to be sensitive to changes in vertical loading but not foot fore-aft velocity. Future directions should include in vivo studies of muscle activation and force–length dynamics to provide more direct evidence of the sensorimotor control strategies for stability in response to belt-speed perturbations.

    

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4. NONAN GaitPrint: An IMU gait database of healthy young adults

   https://doi.org/10.1038/s41597-023-02704-z  

   Wiles, Tyler M. ; Mangalam, Madhur ; Sommerfeld, Joel H. ; Kim, Seung Kyeom ; Brink, Kolby J. ; Charles, Anaelle Emeline ; Grunkemeyer, Alli ; Kalaitzi Manifrenti, Marilena ; Mastorakis, Spyridon ; Stergiou, Nick ; et al ( December 2023 , Scientific Data)

   An ongoing thrust of research focused on human gait pertains to identifying individuals based on gait patterns. However, no existing gait database supports modeling efforts to assess gait patterns unique to individuals. Hence, we introduce the Nonlinear Analysis Core (NONAN) GaitPrint database containing whole body kinematics and foot placement during self-paced overground walking on a 200-meter looping indoor track. Noraxon Ultium Motion^TMinertial measurement unit (IMU) sensors sampled the motion of 35 healthy young adults (19–35 years old; 18 men and 17 women;mean ± 1 s.d. age: 24.6 ± 2.7 years; height: 1.73 ± 0.78 m; body mass: 72.44 ± 15.04 kg) over 18 4-min trials across two days. Continuous variables include acceleration, velocity, position, and the acceleration, velocity, position, orientation, and rotational velocity of each corresponding body segment, and the angle of each respective joint. The discrete variables include an exhaustive set of gait parameters derived from the spatiotemporal dynamics of foot placement. We technically validate our data using continuous relative phase, Lyapunov exponent, and Hurst exponent—nonlinear metrics quantifying different aspects of healthy human gait.

    

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5. There are unique kinematics during locomotor transitions between level ground and stair ambulation that persist with increasing stair grade

   https://doi.org/10.1038/s41598-023-34857-7  

   Neuman, Ross M. ; Fey, Nicholas P. ( December 2023 , Scientific Reports)

   Human ambulation is typically characterized during steady-state isolated tasks (e.g., walking, running, stair ambulation). However, general human locomotion comprises continuous adaptation to the varied terrains encountered during activities of daily life. To fill an important gap in knowledge that may lead to improved therapeutic and device interventions for mobility-impaired individuals, it is vital to identify how the mechanics of individuals change as they transition between different ambulatory tasks, and as they encounter terrains of differing severity. In this work, we study lower-limb joint kinematics during the transitions between level walking and stair ascent and descent over a range of stair inclination angles. Using statistical parametric mapping, we identify where and when the kinematics of transitions are unique from the adjacent steady-state tasks. Results show unique transition kinematics primarily in the swing phase, which are sensitive to stair inclination. We also train Gaussian process regression models for each joint to predict joint angles given the gait phase, stair inclination, and ambulation context (transition type, ascent/descent), demonstrating a mathematical modeling approach that successfully incorporates terrain transitions and severity. The results of this work further our understanding of transitory human biomechanics and motivate the incorporation of transition-specific control models into mobility-assistive technology.

    

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