Optimizing Wearable Assistive Devices with Neuromuscular Models and Optimal Control

The coupling of human movement dynamics with the function and design of wearable assistive devices is vital to better understand the interaction between the two. Advanced neuromuscular models and optimal control formulations provide the possibility to study and improve this interaction. In addition, optimal control can also be used to generate predictive simulations that generate novel movements for the human model under varying optimization criterion

Human sit-to-stand transfer modeling towards intuitive and biologically-inspired robot assistance

© 2016, Springer Science+Business Media New York. Sit-to-stand (STS) transfers are a common human task which involves complex sensorimotor processes to control the highly nonlinear musculoskeletal system. In this paper, typical unassisted and assisted human STS transfers are formulated as optimal feedback control problem that finds a compromise between task end-point accuracy, human balance, energy consumption, smoothness of motion and control and takes further human biomechanical control constraints into account. Differential dynamic programming is employed, which allows taking the full, nonlinear human dynamics into consideration. The biomechanical dynamics of the human is modeled by a six link rigid body including leg, trunk and arm segments. Accuracy of the proposed modelling approach is evaluated for different human healthy and patient/elderly subjects by comparing simulations and experimentally collected data. Acceptable model accuracy is achieved with a generic set of constant weights that prioritize the different criteria. Finally, the proposed STS model is used to determine optimal assistive strategies suitable for either a person with specific body segment weakness or a more general weakness. These strategies are implemented on a robotic mobility assistant and are intensively evaluated by 33 elderlies, mostly not able to perform unassisted STS transfers. The validation results show a promising STS transfer success rate and overall user satisfaction

Legged Robots

International audienc

Evidence for Composite Cost Functions in Arm Movement Planning: An Inverse Optimal Control Approach

An important issue in motor control is understanding the basic principles underlying the accomplishment of natural movements. According to optimal control theory, the problem can be stated in these terms: what cost function do we optimize to coordinate the many more degrees of freedom than necessary to fulfill a specific motor goal? This question has not received a final answer yet, since what is optimized partly depends on the requirements of the task. Many cost functions were proposed in the past, and most of them were found to be in agreement with experimental data. Therefore, the actual principles on which the brain relies to achieve a certain motor behavior are still unclear. Existing results might suggest that movements are not the results of the minimization of single but rather of composite cost functions. In order to better clarify this last point, we consider an innovative experimental paradigm characterized by arm reaching with target redundancy. Within this framework, we make use of an inverse optimal control technique to automatically infer the (combination of) optimality criteria that best fit the experimental data. Results show that the subjects exhibited a consistent behavior during each experimental condition, even though the target point was not prescribed in advance. Inverse and direct optimal control together reveal that the average arm trajectories were best replicated when optimizing the combination of two cost functions, nominally a mix between the absolute work of torques and the integrated squared joint acceleration. Our results thus support the cost combination hypothesis and demonstrate that the recorded movements were closely linked to the combination of two complementary functions related to mechanical energy expenditure and joint-level smoothness

Legged Robots

International audienc

Towards efficient lower-limb exoskeleton evaluation: Defining biomechanical metrics to quantify assisted gait familiarization

\ua9 2022 IEEE.Biomechanical evaluation of exoskeletons is a fundamental part of testing hardware and software modifications, but it can be quite challenging and inefficient since different users demonstrate different familiarization patterns and time windows. In this paper, we define four biomechanical metrics that serve as gait familiarization indicators for lower-limb exoskeleton assistance, in order to increase the efficiency and accuracy of biomechanical measurements and data collection, for exoskeleton testing. We assess stride duration, mediolateral deviation from a straight path, polygon of support area, and muscle effort of the lower and upper body, for five participants performing five walking bouts with a manual and a more automatic mode of exoskeleton each. We observed familiarization trends only under the automatic mode for the latter five bouts, based on the reduction of the quantities of the first three metrics (p < 0.05). Muscle effort showed evidence of familiarization based on reductions of co-contraction for the arms only, while muscle activity did not show any familiarization trends for lower or upper body muscles. This study demonstrated that the first three biomechanical metrics are promising candidates for familiarization indicators and thus paves the path towards more efficient exoskeleton testing