8 accepted papers
Supernumerary Robotic Limbs (SRLs) offer considerable promise for assisting wearers in complex tasks, yet their adaptability is often constrained by their inherent fixed morphology. While modular reconfigurable designs present a viable solution, applying it to wearable systems introduces critical si
Previous research has shown that load-bearing with elastic suspension backpacks improves human biomechanics and reduces human energy expenditure. Constant-force suspension backpacks (CFSB) with zero stiffness were developed to minimize the inertial force of loads. However, there is a mismatch betwee
Preference-based optimizing methods have shown their advantages and potential in exploring individual, comfortable, and effective control strategies and assistance parameters of exoskeletons during locomotion. Research indicates that compared with naive wearers, knowledgeable wearers with abundant e…
Research has indicated that exoskeletons can assist human movement, but due to the influence of additional weight and challenges in control strategy design, only a few exoskeletons effectively reduce the wearers’ metabolic costs during running. This paper proposes an innovative and efficient hip-ass…
Graph neural networks are widely used tools for graph prediction tasks. Motivated by their empirical performance, prior works have developed generalization bounds for graph neural networks, which scale with graph structures in terms of the maximum degree. In this paper, we present generalization bou…
We consider the problem of traffic accident analysis on a road network based on road network connections and traffic volume. Previous works have designed various deep-learning methods using historical records to predict traffic accident occurrences. However, there is a lack of consensus on how accur…
Supernumerary robotic limbs (SRL) are new types of wearable robots used as the third limb to work with humans. The device is designed to provide the wearer with better auxiliary ability. This paper presents the design and implementation of a Series-Parallel-Reconfigurable Tendon-driven Supernumerary
We consider transfer learning approaches that fine-tune a pretrained deep neural network on a target task. We investigate generalization properties of fine-tuning to understand the problem of overfitting, which often happens in practice. Previous works have shown that constraining the distance from…