During gait neurorehabilitation, many factors influence the quality of gait patterns, particularly the chosen body-weight support (BWS) device. Consequently, robotic BWS devices play a key role in gait rehabilitation of people with neurological disorders. The device transparency, support force vector direction, and attachment to the harness vary widely across existing robotic BWS devices, but the influence of these factors on the production of gait remains unknown. Because this information is key to designing an optimal BWS, we systematically studied these determinants in this work. We report that with a highly transparent device and a conventional harness, healthy participants select a small backward force when asked for optimal BWS conditions. This unexpected finding challenges the view that during human-robot interactions, humans predominantly optimize energy efficiency. Instead, they might seek to increase their feeling of stability and safety. We also demonstrate that the location of the attachment points on the harness strongly affects gait patterns, yet harness attachment is hardly reported in literature. Our results establish principles for the design of BWS devices and personalization of BWS settings for gait neurorehabilitation.

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BACKGROUND: Gait training with partial body weight support (BWS) has become an established rehabilitation technique. Besides passive unloading mechanisms such as springs or counterweights, also active systems that allow rendering constant or modulated vertical forces have been proposed. However, only pilot studies have been conducted to compare different unloading or modulation strategies, and conducting experimental studies is costly and time-consuming. Simulation models that predict the influence of unloading force on human walking may help select the most promising candidates for further evaluation. However, the reliability of simulation results depends on the chosen gait model. The purpose of this paper is two-fold: First, using human experimental data, we evaluate the accuracy of some of the most prevalent walking models in replicating human walking under the influence of Constant-Force BWS: The Simplest Walking model (SW), the Spring-Loaded Inverted Pendulum model (SLIP) and the Muscle-Reflex (MR) gait model. Second, three realizations of BWS, based on Constant-Force (CF), Counterweight (CW) and Tuned-Spring (TS) approaches, are compared to each other in terms of their influence on gait parameters. METHODS: We conducted simulations in Matlab/Simulink to model the behaviour of each gait model under all three BWS conditions. Nine simulations were undertaken in total and gait parameter response was analysed in each case. Root mean square error (mrmse) w.r.t human data was used to compare the accuracy of gait models. The metrics of interest were spatiotemporal parameters and the vertical ground reaction forces. To scrutinize the BWS strategies, loss of dynamic similarity was calculated in terms of root mean square difference in gait dynamics (Δgd) with respect to the reference gait under zero unloading. The gait dynamics were characterized by a dimensionless number Modela-w. RESULTS: SLIP model showed the lowest mrmse for 6 out of 8 gait parameters and for 1 other, the mrmse value were comparable to the MR model; SW model had the highest mrmse. Out of three BWS strategies, Tuned-Spring strategies led to the lowest Δgd values. CONCLUSIONS: The results of this work demonstrate the usefulness of gait models for BWS simulation and suggest the SLIP model to be more suitable for BWS simulations than the Simplest Walker and the Muscle-reflex models. Further, the Tuned-Spring approach appears to cause less distortions to the gait pattern than the more established Counterweight and Constant-Force approaches and merits experimental verification.

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Background: Body weight support (BWS) systems have shown promise as rehabilitation tools for neurologically impaired individuals. This paper reviews the experiment-based research on BWS systems with the aim: (1) To investigate the influence of body weight unloading (BWU) on gait characteristics; (2) To study whether the effects of BWS differ between treadmill and overground walking and (3) To investigate if modulated BWU influences gait characteristics less than unmodulated BWU. Method: A systematic literature search was conducted in the following search engines: Pubmed, Scopus, Web of Science and Google Scholar. Statistical analysis was used to quantify the effects of BWU on gait parameters. Results: 54 studies of experiments with healthy and neurologically impaired individuals walking in a BWS system were included and 32 of these were used for the statistical analysis. Literature was classified using three distinctions: (1) treadmill or overground walking; (2) the type of subjects and (3) the nature of unloading force. Only 27% studies were based on neurologically impaired subjects; a low number considering that they are the primary user group for BWS systems. The studies included BWU from 5% to 100% and the 30% and 50% BWU conditions were the most widely studied. The number of participants varied from 1 to 28, with an average of 12. It was seen that due to the increase in BWU level, joint moments, muscle activity, energy cost of walking and ground reaction forces (GRF) showed higher reduction compared to gait spatio-temporal and joint kinematic parameters. The influence of BWU on kinematic and spatio-temporal gait parameters appeared to be limited up to 30% unloading. 5 gait characteristics presented different behavior in response to BWU for overground and treadmill walking. Remaining 21 gait characteristics showed similar behavior but different magnitude of change for overground and treadmill walking. Modulated unloading force generally led to less difference from the 0% condition than unmodulated unloading. Conclusion: This review has shown that BWU influences all gait characteristics, albeit with important differences between the kinematic, spatio-temporal and kinetic characteristics. BWU showed stronger influence on the kinetic characteristics of gait than on the spatio-temporal parameters and the kinematic characteristics. It was ascertained that treadmill and overground walking can alter the effects of BWU in a different manner. Our results indicate that task-specific gait training is likely to be achievable at a BWU level of 30% and below.

Correction to the article: http://resolver.tudelft.nl/uuid:87343b36-5c7d-4af8-8ab6-3e32d2e2c8d6

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Body weight support (BWS) systems are widely used in gait research and rehabilitation. This letter introduces a new three-dimensional overground BWS system, called the RYSEN. The RYSEN is designed to be intrinsically safe and low power consuming, while still performing at least as well as existing BWS systems regarding human-robot interaction. These features are mainly achieved by decoupling degrees of freedom between motors: slow/high-torque motors for vertical motion and fast/low-torque motors for horizontal motion. This letter explains the design and evaluates its performance on power consumption and safety. Power consumption is expressed in terms of the sum of the positive mechanical output power of all motor axes. Safety is defined as the difference between the mechanical power available for horizontal and vertical movements and the mechanical power that is needed to perform its task. The results of the RYSEN are compared to the performance of three similar systems: a gantry, the FLOAT, and a classic cable robot. The results show that the RYSEN and a gantry consume approximately the same amount of power. The amount is approximately half the power consumed by the next-best system. For the safety, the gantry is taken as the benchmark, because of its perfect decoupling of directions. The RYSEN has a surplus of 268 W and 126 W for horizontal and vertical movements, respectively. This is significantly lower than the next-best system, which has a surplus of 1088 W and 1967 W, respectively@en

Correction The original article [1] contained a major error whereby Fig. 1 mistakenly displayed a duplicate of Fig. 5. The correct version of Fig. 1 has now been restored and can be viewed ahead. Furthermore, this error was mistakenly introduced by the production team that handled this article and as such, was not the fault of the authors. (Figure Presented).Original article: http://resolver.tudelft.nl/uuid:2bb15cdc-7014-4efd-ad51-0dd6a2899675 @en

This paper identifies the class of actuators called clutched elastic actuators (CEAs). CEAs use clutches to control the energy flow into springs. CEAs in exoskeletons, prostheses, legged robots, and robotic arms have shown the ability to reduce the energy consumption and motor requirements, such as peak torque and peak power. Because of those abilities, they are increasingly used in robotics. In this paper, we categorize existing CEA designs, identify trends in those designs, and provide a method to analyze their functionality. Based on a literature survey, current CEA designs are placed in nine categories, depending on their morphology. The main trend is that CEA designs are becoming more complex, meaning that the number of clutches and springs increases. We show with the introduced mathematical analysis that the functionality can be analyzed with a constraint matrix, a stiffness matrix, and multiplication of a clutch-dependent diagonal matrix with an oriented incidence matrix. This method eases the analysis of the functionality of CEAs. Furthermore, it can lead to new CEA designs in which the number of resulting stiffnesses grows exponentially with the number of springs and clutches.@en

Parallel elastic actuators (PEAs) have shown the ability to reduce the energy consumption of robots. However, regular PEAs do not allow us to freely choose at which instant or configuration to store or release energy. This paper introduces the concept and design of the bidirectional clutched parallel elastic actuator (BIC-PEA), which reduces the energy consumption of robots by loading and unloading a parallel spring with controlled timing and direction. The concept of the BIC-PEA consists of a spring that is mounted between the two outgoing axes of a differential mechanism. Those axes can also be locked to the ground by two locking mechanisms. At any position, the BIC-PEA can store the kinetic energy of a joint in the spring such that the joint is decelerated to zero velocity. The spring energy can then be released, accelerating the joint in any desired direction. Such functionality is suitable for robots that perform rest-to-rest motions, such as pick-and-place robots or intermittently moving belts. The main body of our prototype weighs 202 g and fits in a cylinder with a length of 51 mm and a diameter of 45 mm. This excludes the size and weight of the nonoptimized clutches, which would approximately triple the total volume and weight. In the results, we also omit the energy consumption of the clutches. The BIC-PEA can store 0.77 J and has a peak torque of 1.5 N.m. Simulations show that the energy consumption of our one-degree-of-freedom setup can be reduced by 73%. In hardware experiments, we reached peak reductions of 65% and a reduction of 53% in a realistic task, which is larger than all other concepts with the same functionality.

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