Youngmok Yun

An index finger exoskeleton with series elastic actuation for rehabilitation

Rehabilitation of the hands is critical for the restoration of independence in activities of daily living for individuals exhibiting disabilities of the upper extremities. There is initial evidence that robotic devices with force-control-based strategies can help in effective rehabilitation of human limbs. However, to the best of our knowledge, none of the existing hand exoskeletons allow for accurate force or torque control. In this work, we present a novel index finger exoskeleton with Bowden-cable-based series elastic actuation allowing for bidirectional torque control of the device with high backdrivability and low reflected inertia. We present exoskeleton and finger joint torque controllers along with an optimization-based offline parameter estimator. Finally, we carry out tests with the developed prototype to characterize its kinematics, dynamics, and controller performance. Results show that the device preserves the characteristics of natural motion of finger and can be controlled to achieve both exoskeleton and finger joint torque control. Finally, dynamic transparency tests show that the device can be controlled to offer minimal resistance to finger motion. Beyond the present application of the device as a hand rehabilitation exoskeleton, it has the potential to be used as a haptic device for teleoperation.

Statistical method for prediction of gait kinematics with Gaussian process regression

We propose a novel methodology for predicting human gait pattern kinematics based on a statistical and stochastic approach using a method called Gaussian process regression (GPR). We selected 14 body parameters that significantly affect the gait pattern and 14 joint motions that represent gait kinematics. The body parameter and gait kinematics data were recorded from 113 subjects by anthropometric measurements and a motion capture system. We generated a regression model with GPR for gait pattern prediction and built a stochastic function mapping from body parameters to gait kinematics based on the database and GPR, and validated the model with a cross validation method. The function can not only produce trajectories for the joint motions associated with gait kinematics, but can also estimate the associated uncertainties. Our approach results in a novel, low-cost and subject-specific method for predicting gait kinematics with only the subjects body parameters as the necessary input, and also enables a comprehensive understanding of the correlation and uncertainty between body parameters and gait kinematics.

Robust-PCA-based hierarchical plane extraction for application to geometric 3D indoor mapping

Purpose – The authors aim to propose a novel plane extraction algorithm for geometric 3D indoor mapping with range scan data. Design/methodology/approach – The proposed method utilizes a divide-and-conquer step to efficiently handle huge amounts of point clouds not in a whole group, but in forms of separate sub-groups with similar plane parameters. This method adopts robust principal component analysis to enhance estimation accuracy. Findings – Experimental results verify that the method not only shows enhanced performance in the plane extraction, but also broadens the domain of interest of the plane registration to an information-poor environment (such as simple indoor corridors), while the previous method only adequately works in an information-rich environment (such as a space with many features). Originality/value – The proposed algorithm has three advantages over the current state-of-the-art method in that it is fast, utilizes more inlier sensor data that does not become contaminated by severe sensor...

Experimental characterization of Bowden cable friction

This paper presents a systematic method for experimental characterization of Bowden cable friction. A novel tension measurement method using a motion capture system and a spring is introduced. With the tension measurement method, the effects of nine variables on friction are investigated. Experimental results show that i) a combination of 7×19 FEP-coated stainless steel cable and double-sheaths has the highest force transmission efficiency; ii) smaller coefficient of friction material, smaller cable moving speed, shorter cable length, longer clamp distance, stiffer cable/sheath all help to increase the force transmission efficiency; and iii) the static friction pretension ratio only decreases as the coefficient of friction of cable/sheath decreases or as cable/sheath stiffness increases. We have generated guidelines for the Bowden cable performance which may help robotics researchers in choosing materials for the Bowden cables and designing control systems for actuation.

Maestro: An EMG-driven assistive hand exoskeleton for spinal cord injury patients

In this paper, we present an electromyography (EMG)-driven assistive hand exoskeleton for spinal-cord-injury (SCI) patients. We developed an active assistive orthosis, called Maestro, which is light, comfortable, compliant, and capable of providing various hand poses. The EMG signals are obtained from a subjects forearm, post-processed, and classified for operating Maestro. The performance of Maestro is evaluated by a standardized hand function test, called the Sollerman hand function test. The experimental results show that Maestro improved the hand function of the SCI patients.

Design, control, and testing of a thumb exoskeleton with series elastic actuation:

We present an exoskeleton capable of assisting the human thumb through a large range of motion. Our novel thumb exoskeleton has the following unique features: (i) an underlying kinematic mechanism that is optimized to achieve a large range of motion, (ii) a design that actuates four degrees of freedom of the thumb, and (iii) a series elastic actuation based on a Bowden cable, allowing for bidirectional torque control of each thumb joint individually. We present a kinematic model of the coupled thumb exoskeleton system and use it to maximize the range of motion of the thumb. Finally, we carry out tests with the designed device on four subjects to evaluate its workspace and kinematic transparency using a motion capture system and torque control performance. Results show that the device allows for a large workspace with the thumb, is kinematically transparent to natural thumb motion to a high degree, and is capable of accurate torque control.

Accurate torque control of finger joints with UT hand exoskeleton through Bowden cable SEA

The torque control of finger joints is important for effective hand rehabilitation after neural disorders such as stroke. This paper presents an approach for accurate torque control of finger joints with UT hand exoskeleton. We present (1) how we obtained an accurate kinematics model, (2) how we built the torque actuation model with Bowden cable SEA, and (3) how we controlled the torque of finger joints with the kinematic model and the Bowden cable SEA. We have validated our approach with a testbed finger and results show that the UT hand exoskeleton accurately controls the torque of finger joints.

Characterization of a hand-wrist exoskeleton, READAPT, via kinematic analysis of redundant pointing tasks

Training coordinated hand and wrist movement is invaluable during post-neurological injury due to the anatomical, biomechanical, and functional couplings of these joints. This paper presents a novel rehabilitation device for coordinated hand and wrist movement. As a first step towards validating the new device as a measurement tool, the device transparency was assessed through kinematic analysis of a redundant finger pointing task requiring synergistic movement of the wrist and finger joints. The preliminary results of this new methodology showed that wearing the robot affects the kinematic coupling of the wrist and finger for unconstrained pointing tasks. However, further experiments specifying a subset of the solution manifold did not exhibit the same difference between robot and no robot trials. The experiments and analysis form a promising method for the characterization of multi-articular wearable robots as measurement tools in robotic rehabilitation.

Accurate, robust, and real-time estimation of finger pose with a motion capture system

Finger exoskeletons, haptic devices, and augmented reality applications demand an accurate, robust, and fast estimation of finger pose. We present a novel finger pose estimation method using a motion capture system. The method combines system identification and state estimation in a unified framework. The system identification stage investigates the accurate model of a finger, and the state estimation stage tracks the finger pose with the Extended Kalman Filter (EKF) algorithm based on the model obtained in the system identification stage. The algorithm is validated by simulation and experiment. The experimental results show that the method can robustly estimate the finger pose at a high frequency (greater than 1 Khz) in presence of measurement noise, occlusion of markers, and fast movement.

Control in the reliable region of a statistical model with Gaussian process regression

We present a novel statistical model based control algorithm, called Control in the Reliable Region of a Statistical model (CRROS). First, CRROS builds a statistical model with Gaussian process regression, which provides a prediction function and uncertainty of the prediction. Then, CRROS avoids high-uncertainty regions of the statistical model by regulating the null space of the pseudo inverse solution. The simulation results demonstrate that CRROS drives the states toward high-density and low-noise regions of training data, ensuring high reliability of the model. The experiments with a robotic finger, called Flex-finger, show the potential of CRROS to control robotic systems that are difficult to model, contain constrained inputs, and exhibit heteroscedastic noise output.