Static Modeling of the Fiber-Reinforced Soft Pneumatic Actuators Including Inner Compression: Bending in Free Space, Block Force, and Deflection upon Block Force.

Fiber-reinforced soft pneumatic actuators (FR-SPAs) are among the most successful soft actuators in the soft robotics community considering their structural strength, motion range, and force output. Inspired by the pneumatic artificial muscle, the bending-type tubular SPAs have also been applied with fiber winding for body reinforcement and then utilized in many applications. Due to their superior utility and popularity, FR-SPAs have been extensively modeled using different methods. However, very little attention was given to the compression on the inner wall of the FR-SPAs by the compressed air. Furthermore, there is no unified modeling principle for bending, force output, and deflection of the FR-SPA. In this article, we take into account the inner compression and propose a static modeling approach based on the moment equilibrium for three important and frequently studied cases: free bending, block force, and deflection upon block force of the FR-SPA. Stress analysis of the material inside the fiber wall is conducted, which reveals the influence of radial compression by the input air. Then, the new stress expression is used in the moment equilibrium and results in the SPA models for the three aforementioned cases. The models are experimentally verified using SPAs featuring two profile designs and three different silicone rubbers. The results show that the placement of the fiber wall plays an important role in the SPA behavior. More importantly, the models successfully differentiate the two profiles and produce fairly accurate predictions of the bending angles, block forces, and deflections. The understanding of the compression effect offers a new variable in the FR-SPA design process, which can be used to tune the SPA properties for different applications.

Predicting Transitioning Walking Gaits: Hip and Knee Joint Trajectories From the Motion of Walking Canes.

In recent years, wearable exoskeletons and powered prosthetics have been considered key elements to remedy mobility loss. One of the main challenges pertaining to this field is the prediction of the wearer's desired motion. In this paper, we perform a human locomotion analysis, and we investigate the accuracy of predicting the angular position of the lower limb joints from the motion of walking canes. Nine healthy subjects took part of this study and performed a locomotor task that comprised straight walking on flat ground, stair ascent, and upright resting posture. Recurrent Neural Networks and polynomial fitting using Least Squares were used to model dynamic and static non-linear mappings, respectively, between the motion of a cane and its contra-lateral leg joints. A successful prediction of both the hip and knee joints was achieved using information from the cane only, and significant improvement of the prediction error was realized through the addition of data from the arm joints. Overall, Recurrent Neural Networks outperformed Least Squares for both joints' angular position prediction. When using the cane only, the static maps were able to predict steady behaviour but failed in predicting transitioning, as opposed to RNN, which was able to capture both steady behaviour and transitions.

Prediction of Smooth Gait Transitioning for Active Lower Limb Prosthetics.

Amputation is the major cause of gait impairment in our society and is due to several factors and conditions such as war injuries or diabetes lower limb complication, often resulting in a gait impairment. Active prosthetics have been considered to remedy this mobility loss. These devices have the potential to enhance significantly the quality of life of patients. One major challenge resides in the generation of smooth trajectories, especially during gait transitioning for the active joints of the powered devices. Here we propose a smooth trajectory predictor for above-knee prosthetics, where the motion of the hip joints is translated into knee and ankle joint trajectories. We consider a locomotion task that includes overground walking and stairs ascent. Successful prediction is achieved for both knee and ankle joint angular positions.

Human locomotion analysis: Identifying a dynamic mapping between upper and lower limb joints using the Koopman operator.

Human locomotion is a complex process that shows some inherent synergies and coordination, also called inter-joint coordination, between the upper and lower limbs. In this paper, we investigate the use of Koopman operator to identify a dynamic mapping between an upper limb and its contra-lateral lower limb in the human locomotion. We perform a human locomotion analysis in the sagittal plane and restrict the study to the forward motion; more specifically, a straight walking task at a constant speed. We use canes as walking aids to provide additional information about the terrain and enforce a frequency locking between the upper and lower body. This mapping will provide a model of the human locomotion.

Real-time estimation of cerebrospinal fluid system parameters via oscillating pressure infusion.

Hydrocephalus is related to a disturbed cerebrospinal fluid (CSF) system. For diagnosis, lumbar infusion test are performed to estimate outflow conductance, C (out), and pressure volume index, PVI, of the CSF system. Infusion patterns and analysis methods used in current clinical practice are not optimized. Minimizing the investigation time with sufficient accuracy is of major clinical relevance. The aim of this study was to propose and experimentally evaluate a new method, the oscillating pressure infusion (OPI). The non-linear model of the CSF system was transformed into a linear time invariant system. Using an oscillating pressure pattern and linear system identification methods, C (out) and PVI with confidence intervals, were estimated in real-time. Forty-two OPI and constant pressure infusion (CPI) investigations were performed on an experimental CSF system, designed with PVI = 25.5 ml and variable C (out). The ARX model robustly estimated C (out) (mean C (out,OPI) - C (out,CPI) = 0.08 µl/(s kPa), n = 42, P = 0.68). The Box-Jenkins model proved most reliable for PVI (23.7 ± 2.0 ml, n = 42). The OPI method, with its oscillating pressure pattern and new parameter estimation methods, efficiently estimated C (out) and PVI as well as their confidence intervals in real-time. The results from this experimental study show potential for the OPI method and supports further evaluation in a clinical setting.

Experimental testing of a method for on-line identification of the cerebrospinal fluid system.

Accurate estimates of the compliance and out-flow resistance of the human cerebrospinal fluid system are important for diagnosis of a medical condition known as hydrocephalus. In this paper we present a system which provides simultaneous on-line estimates of the outflow resistance and compliance. It's performance is experimentally verified using the same apparatus used to perform actual patient diagnoses and a specially designed physical model of the human cerebrospinal fluid system.