Priyanshu Agarwal

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.

Combining skeletal pose with local motion for human activity recognition

Recent work in human activity recognition has focused on bottom-up approaches that rely on spatiotemporal features, both dense and sparse. In contrast, articulated motion, which naturally incorporates explicit human action information, has not been heavily studied; a fact likely due to the inherent challenge in modeling and inferring articulated human motion from video. However, recent developments in data-driven human pose estimation have made it plausible. In this paper, we extend these developments with a new middle-level representation called dynamic pose that couples the local motion information directly and independently with human skeletal pose, and present an appropriate distance function on the dynamic poses. We demonstrate the representative power of dynamic pose over raw skeletal pose in an activity recognition setting, using simple codebook matching and support vector machines as the classifier. Our results conclusively demonstrate that dynamic pose is a more powerful representation of human action than skeletal pose.

Simulation-Based Design of Exoskeletons Using Musculoskeletal Analysis

Exoskeletons are a new class of articulated mechanical systems whose performance is realized while in intimate contact with the human user. The overall performance depends on many factors including selection of architecture, device, parameters and the nature of the coupling to the human, offering numerous challenges to design-evaluation and refinement. In this paper, we discuss merger of techniques from the musculoskeletal analysis and simulation-based design to study and analyze the performance of such exoskeletons. A representative example of a simplified exoskeleton interacting with and assisting the human arm is used to illustrate principal ideas. Overall, four different case-scenarios are developed and examined with quantitative performance measures to evaluate the effectiveness of the design and allow for design refinement. The results show that augmentation by way of the exoskeleton can lead to a significant reduction in muscle loading.© 2010 ASME

Impedance and force-field control of the index finger module of a hand exoskeleton for rehabilitation

For rehabilitation robots to be successful, a control system that results in safe, comfortable and effective interaction between the robot and subject is necessary. We present two types of torque-based controllers for the index finger module of a hand exoskeleton for rehabilitation. Impedance control applies a position-dependent torque at the joints, while keeping the interaction with the finger compliant. Force-field control, on the other hand, applies a position-dependent normal and tangential torques to track a contour in the joint angle space. We also present self-guided impedance control wherein hemiparetic subjects could guide their impaired hand with their healthy hand by using active and passive versions of our device. Experiments with a healthy subject showed that the device could render a range of impedances from low to high. For accurate trajectory tracking high impedance is needed because the inherent finger stiffness varies significantly, however, high impedance increases the torque applied when the finger motion is impeded. On the contrary, force-field control is safer for subjects whose joint show involuntary locking (e.g. spastic joints) and does not apply increased torques during stalled motion, however, provides limited control over the velocity. These results suggest that impedance control would be better suited for therapy where the goal is to train for time-critical tasks and force-field control would work where coordination between the joints is important.

A novel framework for virtual prototyping of rehabilitation exoskeletons

Human-worn rehabilitation exoskeletons have the potential to make therapeutic exercises increasingly accessible to disabled individuals while reducing the cost and labor involved in rehabilitation therapy. In this work, we propose a novel human-model-in-the-loop framework for virtual prototyping (design, control and experimentation) of rehabilitation exoskeletons by merging computational musculoskeletal analysis with simulation-based design techniques. The framework allows to iteratively optimize design and control algorithm of an exoskeleton using simulation. We introduce biomechanical, morphological, and controller measures to quantify the performance of the device for optimization study. Furthermore, the framework allows one to carry out virtual experiments for testing specific “what-if” scenarios to quantify device performance and recovery progress. To illustrate the application of the framework, we present a case study wherein the design and analysis of an index-finger exoskeleton is carried out using the proposed framework.

Estimating Human Dynamics On-the-fly Using Monocular Video For Pose Estimation

Human pose estimation using uncalibrated monocular visual inputs alone is a challenging problem for both the computer vision and robotics communities. From the robotics perspective, the challenge here is one of pose estimation of a multiply-articulated system of bodies using a single nonspecialized environmental sensor (the camera) and thereby, creating low-order surrogate computational models for analysis and control. In this work, we propose a technique for estimating the lowerlimb dynamics of a human solely based on captured behavior using an uncalibrated monocular video camera. We leverage our previously developed framework for human pose estimation to (i) deduce the correct sequence of temporally coherent gap-filled pose estimates, (ii) estimate physical parameters, employing a dynamics model incorporating the anthropometric constraints, and (iii) filter out the optimized gap-filled pose estimates, using an Unscented Kalman Filter (UKF) with the estimated dynamicallyequivalent human dynamics model. We test the framework on videos from the publicly available DARPA Mind’s Eye Year 1 corpus [8]. The combined estimation and filtering framework not only results in more accurate physically plausible pose estimates, but also provides pose estimates for frames, where the original human pose estimation framework failed to provide one.

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.

Assist-as-Needed Controllers for Index Finger Module of a Hand Exoskeleton for Rehabilitation

We present two types of subject-specific assist-as-needed controllers for the index finger module of a hand exoskeleton designed for rehabilitation after a neuromuscular impairment such as stroke. Learned force-field control is a novel control technique in which a neural-network-based model of the required torques is learned for a specific subject and then used to build a force-field to assist the subject’s finger joint motion. Adaptive assist-as-needed control, on the other hand, estimates the coupled finger-exoskeleton system torque requirement of a subject using radial basis function (RBF) and on-the-fly adapts the RBF magnitudes to provide a feed-forward assistance for improved trajectory tracking. Experiments on the index finger exoskeleton prototype with a healthy subject showed that while the force-field control is non-adaptive and there is less control on the speed of execution of the task, it is safer as it does not apply increased torques if the finger motion is restricted. On the other hand, adaptive assist-as-needed controller adapts to the changing needs of the coupled finger-exoskeleton system and helps in performing the task with a consistent speed, however, applies increased torques in case of restricted motion resulting in potential user discomfort.Copyright