Pressure control of a pneumatic actuator using fast switching On/Off valves continues to remain a major challenge for researchers. This article proposes a novel pulse width modulation–sliding mode controller that improves pressure tracking of pneumatic actuators. First, a comprehensive mathematical model of the pneumatic system was developed that consists of several submodels. The model comprises pressure and temperature equations describing the thermodynamic process inside the pneumatic chamber, an orifice flow model for the On/Off valve, and a model for the dynamic response of the On/Off valve to the control signal. Second, computer simulations were carried out using the model, and then, experimental tests were performed to verify the simulation results. The comparison between simulation and experimental results demonstrates good accuracy of the presented model. Since the governing equations of pneumatic systems are highly nonlinear in terms of parametric (including discharge coefficients of the valve) and structural uncertainties (the lack of knowledge about the exact type of the thermodynamic process), a robust controller was designed for such a system. In this study, a novel pulse width modulation–sliding mode controller is proposed that demonstrates a significant improvement in pressure control of pneumatic actuators compared to other proposed controllers from the literature.

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Previous studies have shown that inclusion of arm swing in gait rehabilitation leads to more effective walking recovery in patients with walking impairments. However, little is known about the correct arm-swing trajectories to be used in gait rehabilitation given the fact that changes in walking conditions affect arm-swing patterns. In this paper we present a comprehensive look at the effects of a variety of conditions on arm-swing patterns during walking. The results describe the effects of surface slope, walking speed, and physical characteristics on arm-swing patterns in healthy individuals. We propose data-driven mathematical models to describe arm-swing trajectories. Thirty individuals (fifteen females and fifteen males) with a wide range of height (1.58-1.91m) and body mass (49-98kg), participated in our study. Based on their self-selected walking speed, each participant performed walking trials with four speeds on five surface slopes while their whole-body kinematics were recorded. Statistical analysis showed that walking speed, surface slope, and height were the major factors influencing arm swing during locomotion. The results demonstrate that data-driven models can successfully describe arm-swing trajectories for normal gait under varying walking conditions. The findings also provide insight into the behavior of the elbow during walking.

Gait rehabilitation is often focused on the legs and overlooks the role of the upper limbs. However, a variety of studies have demonstrated the importance of proper arm swing both during healthy walking and during rehabilitation. In this paper, we describe a method for generating proper arm-swing trajectories in real time using only measurements of the angular velocity of a person's thighs, to be used during gait rehabilitation with self-selected walking speed. A data-driven linear time-invariant transfer function is developed, using frequency-response methods, which captures the frequency-dependent magnitude and phase relationship between the thighs' angular velocities and the arm angles (measured at the shoulder, in the sagittal plane), using a data set of 30 healthy adult subjects. We show that the proposed method generates smooth trajectories for both healthy individuals and patients with mild to moderate Parkinson disease. The proposed method can be used in future robotic devices that integrate arm swing in gait rehabilitation of patients with walking impairments to improve the efficacy of their rehabilitation.

This paper describes an improved control system for the Treadport immersive locomotion interface, with results that generalize to any treadmill that utilizes an actuated tether to enable self-selected walking speed. A new belt controller is implemented to regulate the user's position; when combined with the user's own volition, this controller also enables the user to naturally self-select their walking speed as they would when walking over ground. A new kinesthetic-force-feedback controller is designed for the tether that applies forces to the user's torso. This new controller is derived based on maintaining the user's sense of balance during belt acceleration, rather than by rendering an inertial force as was done in our prior work. Based on the results of a human-subjects study, the improvements in both controllers significantly contribute to an improved perception of realistic walking on the Treadport. The improved control system uses intuitive dynamic-system and anatomical parameters and requires no ad hoc gain tuning. The control system simply requires three measurements to be made for a given user: the user's mass, the user's height, and the height of the tether attachment point on the user's torso.

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This paper presents the novel multimodal sensing capabilities of ionic polymer metal composites (IPMCs) for measuring linear and rotational deformations. Two IPMC samples with Gold (Au) and black Platinum (Pt) electrodes were manufactured, and their responses under twisting and axial compression/tension were investigated. To perform consistent and rigorous experiments to study the multimodal sensing capabilities of the IPMC samples, an experimental test bed was developed that enabled the application of twisting, axial compression, and axial tension to the IPMC strips both in a separate and combined manner, while recording their generated voltage responses. Through rigorous experiments, this study demonstrates the unique sensing capabilities of IPMCs to create distributed nanosensing over the entire body of the IPMC sample. Upon various types of deformations, IPMCs were shown to generate unique and distinct output voltage signals that were highly correlated with the type of motions induced. In particular, the strongest correlations of more than 0.9 were found between the IPMCs' responses and the applied mechanical stimuli in the twisting and axial compression modes, while moderate correlations were observed in the axial tension mode. These results provide a proof-of-concept for the future use of IPMCs as flexible and versatile sensors for measuring linear and rotational motions in applications such as wearable soft robotics.