Haptic feedback is known to enhance the realism of an individual’s interactions with objects in virtual environments. Wearable haptic devices, such as vibrotactile sleeves or armbands, can provide haptic feedback in a smaller and more lightweight form factor than haptic gloves that can be bulky and cumbersome to the wearer. In this article, we present tactile and squeeze bracelet interface (Tasbi), a multimodal haptic wristband that can provide radial squeeze forces around the wrist along with vibrotactile feedback at six discrete locations around the band. Tasbi implements a squeezing mechanism that minimizes tangential forces between the band’s points of contact with the skin, instead of focusing the motor actuation to predominantly normal forces. Force sensing capacitors enable closed-loop control of the squeeze force, while vibration is achieved with linear resonant actuators. A detailed description of the design and experimental results demonstrating closed-loop control of squeeze cues provided by Tasbi is presented. Additionally, we present the results of psychophysical experiments that quantify user perception of the vibration and squeeze cues, including vibrotactile identification accuracy in the presence of varying squeeze forces, discrimination thresholds for the squeeze force, and an analysis of user preferences for squeeze actuation magnitudes.

Soft fluidic actuators are increasingly being used for wearable haptic devices due to their high energy density and low encumbrance. These actuators are typically controlled using constant fluidic pressure control (CFPC), where the actuator pressure is switched between a high pressure source and atmospheric pressure using a fluidic valve. However, this type of control has several limitations for soft actuators including limited dynamic range, slow actuator response, low pressure control resolution and unnatural haptic interaction. In this paper, we present a novel control strategy for soft fluidic actuators, called constant fluidic mass control (CFMC), where the mass of fluid introduced into the actuator is kept constant during actuation, rather than the pressure as in CFPC. Our experimental results show that compared to CFPC, CFMC results in a larger dynamic range of actuator output forces, faster actuator response time to reach a desired target pressure, and higher resolution of pressure control, which makes it particularly useful for wearable haptics. In addition, CFMC enables analog pressure control and we present a neural-network-based supervised learning algorithm for accurate pressure control of soft actuators. Results show that our algorithm can predict actuator pressure with an accuracy of 99% and can be generalized to different soft TPU-fabric fluidic actuators.

Integration of soft haptic devices into garments can improve their usability and wearability for daily computing interactions. In this paper, we introduce PneuSleeve, a fabric-based, compact, and highly expressive forearm sleeve which can render a broad range of haptic stimuli including compression, skin stretch, and vibration. The haptic stimuli are generated by controlling pneumatic pressure inside embroidered stretchable tubes. The actuation configuration includes two compression actuators on the proximal and distal forearm, and four uniformly distributed linear actuators around and tangent to the forearm. Further, to ensure a suitable grip force, two soft mutual capacitance sensors are fabricated and integrated into the compression actuators, and a closed-loop force controller is implemented. We physically characterize the static and dynamic behavior of the actuators, as well as the performance of closed-loop control. We quantitatively evaluate the psychophysical characteristics of the six actuators in a set of user studies. Finally, we show the expressiveness of PneuSleeve by evaluating combined haptic stimuli using subjective assessments.

A critical question to be answered to improve robotic rehabilitation is what is the optimal rehabilitation environment for a subject that will facilitate maximum recovery during therapy? Studies suggest that task variability, nature and degree of assistance or error-augmentation and type of feedback play a critical role in motor (re)-learning. In this work, we present a framework for robot-assisted motor (re)-learning that provides subject-specific training by allowing for simultaneous adaptation of task, assistance and feedback based on the performance of the subject on the task. We model a continuous and coordinated multi-joint task using a learning-from-demonstration approach, which allows the task to be modeled in a generative manner such that the challenge-level of the task could be modulated in an online manner. To train the subjects for dexterous manipulation, we present a torque-based task that requires the subject to dynamically regulate their joint torques. Finally, we carry out a pilot study with healthy human subjects using our previously developed hand exoskeleton to test a hypothesis and the results suggest that training under simultaneous adaptation of task, assistance and feedback positively affects motor learning.

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 subject's 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.

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.

Effective teleoperation requires real-time control of a remote robotic system. In this work, we develop a controller for realizing smooth and accurate motion of a robotic head with application to a teleoperation system for the Furhat robot head [1], which we call TeleFurhat. The controller uses the head motion of an operator measured by a Microsoft Kinect 2 sensor as reference and applies a processing framework to condition and render the motion on the robot head. The processing framework includes a pre-filter based on a moving average filter, a neural network-based model for improving the accuracy of the raw pose measurements of Kinect, and a constrained-state Kalman filter that uses a minimum jerk model to smooth motion trajectories and limit the magnitude of changes in position, velocity, and acceleration. Our results demonstrate that the robot can reproduce the human head motion in real time with a latency of approximately 100 to 170 ms while operating within its physical limits. Furthermore, viewers prefer our new method over rendering the raw pose data from Kinect.

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.