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1. A neuromuscular model of human locomotion combines spinal reflex circuits with voluntary movements

   https://doi.org/10.1038/s41598-022-11102-1  

   Ramadan, Rachid; Geyer, Hartmut; Jeka, John; Schöner, Gregor; Reimann, Hendrik (May 2022, Scientific Reports)

   Abstract Existing models of human walking use low-level reflexes or neural oscillators to generate movement. While appropriate to generate the stable, rhythmic movement patterns of steady-state walking, these models lack the ability to change their movement patterns or spontaneously generate new movements in the specific, goal-directed way characteristic of voluntary movements. Here we present a neuromuscular model of human locomotion that bridges this gap and combines the ability to execute goal directed movements with the generation of stable, rhythmic movement patterns that are required for robust locomotion. The model represents goals for voluntary movements of the swing leg on the task level of swing leg joint kinematics. Smooth movements plans towards the goal configuration are generated on the task level and transformed into descending motor commands that execute the planned movements, using internal models. The movement goals and plans are updated in real time based on sensory feedback and task constraints. On the spinal level, the descending commands during the swing phase are integrated with a generic stretch reflex for each muscle. Stance leg control solely relies on dedicated spinal reflex pathways. Spinal reflexes stimulate Hill-type muscles that actuate a biomechanical model with eight internal joints and six free-body degrees of freedom. The model is able to generate voluntary, goal-directed reaching movements with the swing leg and combine multiple movements in a rhythmic sequence. During walking, the swing leg is moved in a goal-directed manner to a target that is updated in real-time based on sensory feedback to maintain upright balance, while the stance leg is stabilized by low-level reflexes and a behavioral organization switching between swing and stance control for each leg. With this combination of reflex-based stance leg and voluntary, goal-directed control of the swing leg, the model controller generates rhythmic, stable walking patterns in which the swing leg movement can be flexibly updated in real-time to step over or around obstacles. 

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2. A Robotic Assistance Personalization Control Approach of Hip Exoskeletons for Gait Symmetry Improvement

   https://doi.org/10.1109/IROS55552.2023.10341440  

   Zhang, Qiang; Tu, Xikai; Si, Jennie; Lewek, Michael D.; Huang, He (October 2023, 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   Healthy human locomotion functions with good gait symmetry depend on rhythmic coordination of the left and right legs, which can be deteriorated by neurological disorders like stroke and spinal cord injury. Powered exoskeletons are promising devices to improve impaired people's locomotion functions, like gait symmetry. However, given higher uncertainties and the time-varying nature of human-robot interaction, providing personalized robotic assistance from exoskeletons to achieve the best gait symmetry is challenging, especially for people with neurological disorders. In this paper, we propose a hierarchical control framework for a bilateral hip exoskeleton to provide the adaptive optimal hip joint assistance with a control objective of imposing the desired gait symmetry during walking. Three control levels are included in the hierarchical framework, including the high-level control to tune three control parameters based on a policy iteration reinforcement learning approach, the middle-level control to define the desired assistive torque profile based on a delayed output feedback control method, and the low-level control to achieve a good torque trajectory tracking performance. To evaluate the feasibility of the proposed control framework, five healthy young participants are recruited for treadmill walking experiments, where an artificial gait asymmetry is imitated as the hemiparesis post-stroke, and only the ‘paretic’ hip joint is controlled with the proposed framework. The pilot experimental studies demonstrate that the hierarchical control framework for the hip exoskeleton successfully (asymmetry index from 8.8% to − 0.5%) and efficiently (less than 4 minutes) achieved the desired gait symmetry by providing adaptive optimal assistance on the ‘paretic’ hip joint. 

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3. Programmable coupled oscillators for synchronized locomotion

   https://doi.org/10.1038/s41467-019-11198-6  

   Dutta, Sourav; Parihar, Abhinav; Khanna, Abhishek; Gomez, Jorge; Chakraborty, Wriddhi; Jerry, Matthew; Grisafe, Benjamin; Raychowdhury, Arijit; Datta, Suman (July 2019, Nature Communications)

   Abstract The striking similarity between biological locomotion gaits and the evolution of phase patterns in coupled oscillatory network can be traced to the role of central pattern generator located in the spinal cord. Bio-inspired robotics aim at harnessing this control approach for generation of rhythmic patterns for synchronized limb movement. Here, we utilize the phenomenon of synchronization and emergent spatiotemporal pattern from the interaction among coupled oscillators to generate a range of locomotion gait patterns. We experimentally demonstrate a central pattern generator network using capacitively coupled Vanadium Dioxide nano-oscillators. The coupled oscillators exhibit stable limit-cycle oscillations and tunable natural frequencies for real-time programmability of phase-pattern. The ultra-compact 1 Transistor-1 Resistor implementation of oscillator and bidirectional capacitive coupling allow small footprint area and low operating power. Compared to biomimetic CMOS based neuron and synapse models, our design simplifies on-chip implementation and real-time tunability by reducing the number of control parameters. 

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4. Overground gait transitions are not sharp but involve gradually changing walk–run mixtures even over long distances

   https://doi.org/10.1098/rsos.251445  

   Baker, Nicholas; Long_III, Leroy; Srinivasan, Manoj (November 2025, Royal Society Open Science)

   Humans typically walk at low speeds and run at higher speeds. Previous studies of transitions between walking and running were mostly on treadmills, but real-world locomotion allows more flexibility. Here, we study overground locomotion over long distances (800 or 2400 m) under time constraints, simulating everyday scenarios like traveling to an appointment. Unlike on treadmills, participants can vary both speed and gait during this task. Gait transition in this overground task occurs over a broad ‘gait transition regime’ spanning average speeds from 1.9 to 3.0 m s⁻¹. In this regime, people use mixtures of walking and running on each travel bout: mostly walking at low average speeds (around 1.9 m s⁻¹) and mostly running at high average speeds (3.0 m s⁻¹). The walk–run fraction changes gradually between these speed limits and is 50% at about 2.5 m s⁻¹. Within each walk–run mixture, walking is slower than running, with an unused gap between the two gait speeds. These gait mixtures and their speed dependence are predicted by energy optimality. These findings extend earlier results for shorter distances, showing that similar energetic principles govern longer, more physically and cognitively demanding tasks. Our results highlight the role of whole-task energy minimization including transients in shaping human locomotion and gait choice. 

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5. Neural Networks Trained via Reinforcement Learning Stabilize Walking of a Three-Dimensional Biped Model With Exoskeleton Applications

   https://doi.org/10.3389/frobt.2021.710999  

   Liu, Chujun; Audu, Musa L.; Triolo, Ronald J.; Quinn, Roger D. (August 2021, Frontiers in Robotics and AI)

   null (Ed.)

   Our group is developing a cyber-physical walking system (CPWS) for people paralyzed by spinal cord injuries (SCI). The current CPWS consists of a functional neuromuscular stimulation (FNS) system and a powered lower-limb exoskeleton for walking with leg movements in the sagittal plane. We are developing neural control systems that learn to assist the user of this CPWS to walk with stability. In a previous publication (Liu et al., Biomimetics, 2019, 4, 28), we showed a neural controller that stabilized a simulated biped in the sagittal plane. We are considering adding degrees of freedom to the CPWS to allow more natural walking movements and improved stability. Thus, in this paper, we present a new neural network enhanced control system that stabilizes a three-dimensional simulated biped model of a human wearing an exoskeleton. Results show that it stabilizes human/exoskeleton models and is robust to impact disturbances. The simulated biped walks at a steady pace in a range of typical human ambulatory speeds from 0.7 to 1.3 m/s, follows waypoints at a precision of 0.3 m, remains stable, and continues walking forward despite impact disturbances and adapts its speed to compensate for persistent external disturbances. Furthermore, the neural network controller stabilizes human models of different statures from 1.4 to 2.2 m tall without any changes to the control parameters. Please see videos at the following link: 3D biped walking control . 

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