Walking along a curved path requires coordinated motor actions of the entire body. Here, we investigate the relationship between head and trunk movements during walking. Previous studies have found that the head systematically turns into turns before the trunk does. This has been found to occur at a constant distance rather than at a constant time before a turn. We tested whether this anticipatory head behavior is spatially invariant for turns of different angles. Head and trunk positions and orientations were measured while participants walked around obstacles in 45°, 90°, 135° or 180° turns. The radius of the turns was either imposed or left free. We found that the head started to turn into the direction of the turn at a constant distance before the obstacle (~1.1 m) for turn angles up to 135°. During turns, the head was consistently oriented more into the direction of the turn than the trunk. This difference increased for larger turning angles and reached its maximum later in the turn for larger turns. Walking speeds decreased monotonically for increasing turn angles. Imposing fixed turn radii only affected the point at which the trunk started to turn into a turn. Our results support the view that anticipatory head movements during turns occur in order to gather advance visual information about the trajectory and potential obstacles.

This study develops a multi-level neuromuscular model consisting of topological pools of spiking motor, sensory and interneurons controlling a bi-muscular model of the human arm. The spiking output of motor neuron pools were used to drive muscle actions and skeletal movement via neuromuscular junctions. Feedback information from muscle spindles were relayed via monosynaptic excitatory and disynaptic inhibitory connections, to simulate spinal afferent pathways. Subject-specific model parameters were identified from human experiments by using inverse dynamics computations and optimization methods. The identified neuromuscular model was used to simulate the biceps stretch reflex and the results were compared to an independent dataset. The proposed model was able to track the recorded data and produce dynamically consistent neural spiking patterns, muscle forces and movement kinematics under varying conditions of external forces and co-contraction levels. This additional layer of detail in neuromuscular models has important relevance to the research communities of rehabilitation and clinical movement analysis by providing a mathematical approach to studying neuromuscular pathology.

Predicting the movements, ground reaction forces and neuromuscular activity during gait can be a valuable asset to the clinical rehabilitation community, both to understand pathology, as well as to plan effective intervention. In this work we use an optimal control method to generate predictive simulations of pathological gait in the sagittal plane. We construct a patient-specific model corresponding to a 7-year old child with gait abnormalities and identify the optimal spring characteristics of an ankle-foot orthosis that minimizes muscle effort. Our simulations include the computation of foot-ground reaction forces, as well as the neuromuscular dynamics using computationally efficient muscle torque generators and excitation-activation equations. The optimal control problem is solved with a direct multiple shooting method. The solution of this problem is physically consistent synthetic neural excitation commands, muscle activations and whole body motion. Our simulations produced similar changes to the gait characteristics as those recorded on the patient. The orthosis-equipped model was able to walk faster with more extended knees. Notably, our approach can be easily tuned to simulate weakened muscles, produces physiologically realistic ground reaction forces and smooth muscle activations and torques, and can be implemented on a standard workstation to produce results within a few hours. These results are an important contribution towards bridging the gap between research methods in computational neuromechanics and day-to-day clinical rehabilitation.

Concurrent advancements in mechanical design and motion planning algorithms allow state-of-the-art humanoid robots to exhibit complex and realistic behavior. In face of this added complexity and the need for humanlike behavior, research has begun to look toward studies in human neuroscience to better organize and guide humanoid robot motion. In this paper, we present one such method of generating anthropomorphic motion by building the “invariants” of human movements and applying them as kinematic tasks. Whole-body motion of 14 healthy participants was recorded during a walking and grasping task. The recorded data were statistically analyzed to extract invariants which best described the observed motion. These invariants were expressed as a set of rules that were used to synthesize the stereotypy in human motion. We propose an algorithm that reproduces the key parameters of motion, taking into account the knowledge from human movement and the limitations of the target anthropomorph. The results are then generalized such that we can generate motion for targets which were not originally recorded. The algorithmic output is applied in a task-based prioritized inverse kinematics solver to generate dynamically stable and realistic anthropomorphic motion. We illustrate our results on the humanoid HRP-2 by making it walk to and grasp objects at various positions. Our approach complements classical optimization or motion-planning-based methods and provides interesting perspectives toward the use of human movements for deducing effective cost functions in optimization techniques or heuristics for planning algorithms.

Editorial: Neuromechanics and Control of Physical Behavior: From Experimental and Computational Formulations to Bio-inspired Technologies