77 research outputs found
Comfort-Centered Design of a Lightweight and Backdrivable Knee Exoskeleton
This paper presents design principles for comfort-centered wearable robots
and their application in a lightweight and backdrivable knee exoskeleton. The
mitigation of discomfort is treated as mechanical design and control issues and
three solutions are proposed in this paper: 1) a new wearable structure
optimizes the strap attachment configuration and suit layout to ameliorate
excessive shear forces of conventional wearable structure design; 2) rolling
knee joint and double-hinge mechanisms reduce the misalignment in the sagittal
and frontal plane, without increasing the mechanical complexity and inertia,
respectively; 3) a low impedance mechanical transmission reduces the reflected
inertia and damping of the actuator to human, thus the exoskeleton is
highly-backdrivable. Kinematic simulations demonstrate that misalignment
between the robot joint and knee joint can be reduced by 74% at maximum knee
flexion. In experiments, the exoskeleton in the unpowered mode exhibits 1.03 Nm
root mean square (RMS) low resistive torque. The torque control experiments
demonstrate 0.31 Nm RMS torque tracking error in three human subjects.Comment: 8 pages, 16figures, Journa
Learning Enhanced Resolution-wise features for Human Pose Estimation
Recently, multi-resolution networks (such as Hourglass, CPN, HRNet, etc.)
have achieved significant performance on pose estimation by combining feature
maps of various resolutions. In this paper, we propose a Resolution-wise
Attention Module (RAM) and Gradual Pyramid Refinement (GPR), to learn enhanced
resolution-wise feature maps for precise pose estimation. Specifically, RAM
learns a group of weights to represent the different importance of feature maps
across resolutions, and the GPR gradually merges every two feature maps from
low to high resolutions to regress final human keypoint heatmaps. With the
enhanced resolution-wise features learnt by CNN, we obtain more accurate human
keypoint locations. The efficacies of our proposed methods are demonstrated on
MS-COCO dataset, achieving state-of-the-art performance with average precision
of 77.7 on COCO val2017 set and 77.0 on test-dev2017 set without using extra
human keypoint training dataset.Comment: Published on ICIP 202
Efficient Oblivious Sorting and Shuffling for Hardware Enclaves
Oblivious sorting is arguably the most important building block in the design of efficient oblivious algorithms. We propose new oblivious sorting algorithms for hardware enclaves. Our algorithms achieve asymptotic optimality in terms of both computational overhead and the number of page swaps the enclave has to make to fetch data from insecure memory or disk. We also aim to minimize the concrete constants inside the big-O. One of our algorithms achieve bounds tight to the constant in terms of the number of page swaps. We have implemented our algorithms and made them publicly available through open source. In comparison with (an unoptimized version of) bitonic sort, which is asymptotically non-optimal but the de facto algorithm used in practice, we achieve a speedup of 2000 times for 12 GB inputs
Pilot Test of Dosage Effects in HEXORR II for Robotic Hand Movement Therapy in Individuals With Chronic Stroke
Impaired use of the hand in functional tasks remains difficult to overcome in many individuals after a stroke. This often leads to compensation strategies using the less-affected limb, which allows for independence in some aspects of daily activities. However, recovery of hand function remains an important therapeutic goal of many individuals, and is often resistant to conventional therapies. In prior work, we developed HEXORR I, a robotic device that allows practice of finger and thumb movements with robotic assistance. In this study, we describe modifications to the device, now called HEXORR II, and a clinical trial in individuals with chronic stroke. Fifteen individuals with a diagnosis of chronic stroke were randomized to 12 or 24 sessions of robotic therapy. The sessions involved playing several video games using thumb and finger movement. The robot applied assistance to extension movement that was adapted based on task performance. Clinical and motion capture evaluations were performed before and after training and again at a 6-month followup. Fourteen individuals completed the protocol. Fugl-Meyer scores improved significantly at the 6 month time point compared to baseline, indicating reductions in upper extremity impairment. Flexor hypertonia (Modified Ashworth Scale) also decreased significantly due to the intervention. Motion capture found increased finger range of motion and extension ability after the intervention that continued to improve during the followup period. However, there was no change in a functional measure (Action Research Arm Test). At the followup, the high dose group had significant gains in hand displacement during a forward reach task. There were no other significant differences between groups. Future work with HEXORR II should focus on integrating it with functional task practice and incorporating grip and squeezing tasks.Trial Registration:ClinicalTrials.gov, NCT04536987. Registered 3 September 2020 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/record/NCT04536987
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