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Wearable devices developed with flexible electronics have great potential applications for human health monitoring and motion sensing. Although material softness and structural flexibility provide a deformable human-machine interface to adapt to joint bending or tissue stretching/compression, flexible sensors are inconvenient in practical uses as they usually require calibration every time they are installed. This article presents an approach to design and fabricate a flexible curvature sensor to measure human articular movements for amphibious applications. This flexible sensor employs the capacitive sensing principle, where the dielectric layer and electrodes are made from the polyurethane resin and eutectic gallium-indium (EGaIn) liquid metal; and the fabrication process is implemented with shape deposition molding for batch production. The sensing method for articular rotation angles employs the Euler beam model to make the sensor reusable after one-time calibration by compensating for the unpredicted manual installation error. The illustrative application to ankle sensing in amphibious gaits shows that the root-mean-square error is within 5° for different walking speeds (0.7-1.1 m/s) in treadmill tests and the maximum error is within 3° for underwater sensing with quasi-static measurements. It is expected that the proposed waterproof flexible sensor can push the boundaries of wearable robotics, human locomotion, as well as their related applications.

This article presents an analytical method that offers both spectral and spatial information to predict local electric fields capable of driving neural activities for neuromuscular activation, and the findings of an experimental investigation on a common strategy utilizing multiple high-frequency (HF) electric fields to create an interference to recruit neural firing at depth. By introducing a cut-off frequency [Formula: see text] too high to recruit neural firing in a frequency-based field descriptor, the analytical method offers an effective means to position a focused temporal interference (TI) without mechanically moving the electrodes. The experiment, which was conducted on both forearms of five healthy volunteers, validates the feasibility of the method for selective neuromuscular stimulation, where three nerve/muscles that control human fingers were independently stimulated with two current channels. The numerical and experimental findings demonstrate that the frequency-based method overcomes several limitations associated with surface-based electrical stimulation.

Loaded walking with a rucksack results in both gravitational and inertial forces of the load that must be borne by human carriers. The inertial force may be the source of metabolic burden and musculoskeletal injuries. This paper presents a lightweight backpack with a disturbance observer-based acceleration control to minimize the inertial force. The backpack was evaluated by seven participants walking on a treadmill at 5 km h -1 with a 19.4 kg load. Three experimental conditions were involved, including walking with a locked load (LOCKED), with an acceleration-controlled load (ACTIVE) using the designed backpack and walking with the same load using a rucksack (RUCKSACK). Our results showed that the ACTIVE condition reduces the load acceleration by 98.5% on average, and reduce the gross metabolic power by 8.0% and 11.0% as compared to LOCKED and RUCKSACK conditions respectively. The results demonstrate that the proposed active backpack can improve the loaded walking economy compared with a conventional rucksack in level-ground walking.

Localization of active neural source (ANS) from measurements on head surface is vital in magnetoencephalography. As neuron-generated magnetic fields are extremely weak, significant uncertainties caused by stochastic measurement interference complicate its localization. This paper presents a novel computational method based on reconstructed magnetic field from sparse noisy measurements for enhanced ANS localization by suppressing effects of unrelated noise. In this approach, the magnetic flux density (MFD) in the nearby current-free space outside the head is reconstructed from measurements through formulating the infinite series solution of the Laplace's equation, where boundary condition (BC) integrals over the entire measurements provide "smooth" reconstructed MFD with the decrease in unrelated noise. Using a gradient-based method, reconstructed MFDs with good fidelity are selected for enhanced ANS localization. The reconstruction model, spatial interpolation of BC, parametric equivalent current dipole-based inverse estimation algorithm using reconstruction, and gradient-based selection are detailed and validated. The influences of various source depths and measurement signal-to-noise ratio levels on the estimated ANS location are analyzed numerically and compared with a traditional method (where measurements are directly used), and it was demonstrated that gradient-selected high-fidelity reconstructed data can effectively improve the accuracy of ANS localization.

This article presents a multi-motion control system to help severe disabled people operate an auxiliary appliance using neck-up bioelectric signals measured by a single-channel dry electrode on the forehead. The single-channel dry-electrode multi-motion control system exhibits several practical advantages over its conventional counterparts that use multi-channel wet-electrodes; among the challenges is an effective technique to extract bioelectric features for reliable implementation of multi degrees-of-freedom motion control. Using both time and frequency characteristics of the single-channel dry-electrode measurements, motion commands are derived from multiple feature signals associated with concentration demands and different eye-blink actions for use in a two-level control strategy that has been developed to control predefined multi degrees-of-freedom motion trajectories. Test paradigms were designed to pre-calibrate the users' feature signals to statistically account for individual variances. Experimental trials were then carried out on able-bodied and disabled volunteers to validate the universal applicability of the algorithms. The classification success rates for two different eye-blink feature signals were approximately 95% with an average time of 2.4 s for executing a concentration feature signal. The single-channel dry-electrode-based technique has been validated on a 6-degree-of-freedom robot arm demonstrating its significant potentials to help patients suffering severe motor dysfunctions operate a multi-motion auxiliary appliance in everyday living where the ease of use is a priority., (© IMechE 2015.)

A method based on binocular vision servoing for positioning of a diagnostic package in inertial confinement fusion (ICF) experiments is presented. The general diagnostic instrument manipulator will provide precision three dimension positioning and alignment-to-target capability in ICF experiments. In this work, we focus on the final precise automatic positioning with a binocular vision system. A three dimension image projection vector (IPV), which has an almost linear relationship with the target position in 3D space under the condition of weak perspective, is introduced to extract target position information from binocular image. The difference of the IPV between the current image and the desired image will be used as the input of servo controller. A differential motion model was found for the hybrid manipulator with three degrees of freedom. With this model and the said IPV, the servo strategy will be dramatically simplified compare with general image based visual servo in which the image Jacobian matrix needs estimated online. The experiment result implies that the locating accuracy of the manipulator is less than two pixels. This method can also be used in micromanipulation visual servo field.

This paper presents a knee-joint model to provide a better understanding on the interaction between natural joints and artificial mechanisms for design and control of rehabilitation exoskeletons. The anatomically based knee model relaxes several commonly made assumptions that approximate a human knee as engineering pin-joint in exoskeleton design. Based on published MRI data, we formulate the kinematics of a knee-joint and compare three mathematical approximations; one model bases on two sequential circles rolling a flat plane; and the other two are mathematically differentiable ellipses-based models with and without sliding at the contact. The ellipses-based model taking sliding contact into accounts shows that the rolling-sliding ratio of a knee-joint is not a constant but has an average value consistent with published measurements. This knee-joint kinematics leads to a physically more accurate contact-point trajectory than methods based on multiple circles or lines, and provides a basis to derive a knee-joint kinetic model upon which the effects of a planar exoskeleton mechanism on the internal joint forces and torque during flexion can be numerically investigated. Two different knee-joint kinetic models (pin-joint approximation and anatomically based model) are compared against a condition with no exoskeleton. The leg and exoskeleton form a closed kinematic chain that has a significant effect on the joint forces in the knee. Human knee is more tolerant than pin-joint in negotiating around a singularity but its internal forces increase with the exoskeleton mass-to-length ratio. An oversimplifying pin-joint approximation cannot capture the finite change in the knee forces due to the singularity effect., (Copyright 2010 Elsevier Ltd. All rights reserved.)