Design of a Passive Exoskeleton Spine

In this thesis, a passive exoskeleton spine was designed and evaluated by a series of biomechanics simulations. The design objectives were to reduce the human operator’s back muscle efforts and the intervertebral reaction torques during a full range sagittal plane spine flexion/extension. The biomechanics simulations were performed using the OpenSim modeling environment. To manipulate the simulations, a full body musculoskeletal model was created based on the OpenSim gait2354 and “lumbar spine” models. To support flexion and extension of the torso a “push-pull” strategy was proposed by applying external pushing and pulling forces on different locations on the torso. The external forces were optimized via simulations and then a physical exoskeleton prototype was built to evaluate the “push-pull” strategy in vivo. The prototype was tested on three different subjects where the sEMG and inertial data were collected to estimate the muscle force reduction and intervertebral torque reduction. The prototype assisted the users in sagittal plane flexion/extension and reduced the average muscle force and intervertebral reaction torque by an average of 371 N and 29 Nm, respectively

A novel robotic platform to assist, train, and study head-neck movement

Moving the head-neck freely is an everyday task that a healthy person takes for granted. Such a simple movement, however, may be very challenging for individuals with neurological disorders such as amyotrophic lateral sclerosis. These individuals often do not have enough neck muscle strength to stabilize the head at the upright neutral or to move it in a controlled manner. Static braces are commonly prescribed to these patients. However, these braces often fix the head at a single configuration, which makes them uncomfortable to wear for an extended period of time. In this thesis, a robotic neck brace is developed. It accommodates three rotations and covers roughly 70% range of motion of the head-neck of a typical able-bodied adult. The hardware is lightweight (1.5 kilogram) and wearable, with a pair of pads and a soft band attached to the shoulders and the forehead, respectively. A parallel mechanism connecting the shoulder pads and the headband was designed to meet the empirical human movement data. This design choice is novel where the parasitic motion (translation of the head) was parameterized and optimized to address misalignment between the robot and the user's head. A user can control this neck brace to assist intended head-neck movement through input devices, including hand-held joysticks, keyboards, and eye-trackers. This provides a potential solution to remediate head drop. Additionally, this robotic brace is developed into a versatile platform to train and study head-neck movements. The robot was designed to be highly transparent to the user and features different force controllers. Therefore, it can be used to assess the free movement of the head-neck and mimic different interactions between a therapist and a patient. The modalities of this neck brace have been validated with different users. To the best of our knowledge, this robotic neck brace is the first in the literature to assist, train, and study head-neck movements

TSNet-SAC: Leveraging Transformers for Efficient Task Scheduling

In future 6G Mobile Edge Computing (MEC), autopilot systems require the capability of processing multimodal data with strong interdependencies. However, traditional heuristic algorithms are inadequate for real-time scheduling due to their requirement for multiple iterations to derive the optimal scheme. We propose a novel TSNet-SAC based on Transformer, that utilizes heuristic algorithms solely to guide the training of TSNet. Additionally, a Sliding Augment Component (SAC) is introduced to enhance the robustness and resolve algorithm defects. Furthermore, the Extender component is designed to handle multi-scale training data and provide network scalability, enabling TSNet to adapt to different access scenarios. Simulation demonstrates that TSNet-SAC outperforms existing networks in accuracy and robustness, achieving superior scheduling-making latency compared to heuristic algorithms

Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features

Machine learning has demonstrated remarkable prediction accuracy over i.i.d data, but the accuracy often drops when tested with data from another distribution. In this paper, we aim to offer another view of this problem in a perspective assuming the reason behind this accuracy drop is the reliance of models on the features that are not aligned well with how a data annotator considers similar across these two datasets. We refer to these features as misaligned features. We extend the conventional generalization error bound to a new one for this setup with the knowledge of how the misaligned features are associated with the label. Our analysis offers a set of techniques for this problem, and these techniques are naturally linked to many previous methods in robust machine learning literature. We also compared the empirical strength of these methods demonstrated the performance when these previous techniques are combined, with an implementation available at https://github.com/OoDBag/WRComment: to appear at UAI 202

Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models

Machine learning has demonstrated remarkable performance over finite datasets, yet whether the scores over the fixed benchmarks can sufficiently indicate the model's performance in the real world is still in discussion. In reality, an ideal robust model will probably behave similarly to the oracle (e.g., the human users), thus a good evaluation protocol is probably to evaluate the models' behaviors in comparison to the oracle. In this paper, we introduce a new robustness measurement that directly measures the image classification model's performance compared with a surrogate oracle (i.e., a foundation model). Besides, we design a simple method that can accomplish the evaluation beyond the scope of the benchmarks. Our method extends the image datasets with new samples that are sufficiently perturbed to be distinct from the ones in the original sets, but are still bounded within the same image-label structure the original test image represents, constrained by a foundation model pretrained with a large amount of samples. As a result, our new method will offer us a new way to evaluate the models' robustness performance, free of limitations of fixed benchmarks or constrained perturbations, although scoped by the power of the oracle. In addition to the evaluation results, we also leverage our generated data to understand the behaviors of the model and our new evaluation strategies

Iterative Few-shot Semantic Segmentation from Image Label Text

Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only a few support images. Most previous methods rely on the pixel-level label of support images. In this paper, we focus on a more challenging setting, in which only the image-level labels are available. We propose a general framework to firstly generate coarse masks with the help of the powerful vision-language model CLIP, and then iteratively and mutually refine the mask predictions of support and query images. Extensive experiments on PASCAL-5i and COCO-20i datasets demonstrate that our method not only outperforms the state-of-the-art weakly supervised approaches by a significant margin, but also achieves comparable or better results to recent supervised methods. Moreover, our method owns an excellent generalization ability for the images in the wild and uncommon classes. Code will be available at https://github.com/Whileherham/IMR-HSNet.Comment: ijcai 202

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning

Label-noise learning (LNL) aims to increase the model's generalization given training data with noisy labels. To facilitate practical LNL algorithms, researchers have proposed different label noise types, ranging from class-conditional to instance-dependent noises. In this paper, we introduce a novel label noise type called BadLabel, which can significantly degrade the performance of existing LNL algorithms by a large margin. BadLabel is crafted based on the label-flipping attack against standard classification, where specific samples are selected and their labels are flipped to other labels so that the loss values of clean and noisy labels become indistinguishable. To address the challenge posed by BadLabel, we further propose a robust LNL method that perturbs the labels in an adversarial manner at each epoch to make the loss values of clean and noisy labels again distinguishable. Once we select a small set of (mostly) clean labeled data, we can apply the techniques of semi-supervised learning to train the model accurately. Empirically, our experimental results demonstrate that existing LNL algorithms are vulnerable to the newly introduced BadLabel noise type, while our proposed robust LNL method can effectively improve the generalization performance of the model under various types of label noise. The new dataset of noisy labels and the source codes of robust LNL algorithms are available at https://github.com/zjfheart/BadLabels