No abstract
Our goal is to introduce a more appropriate method of representing, generalising and comparing gaits; particularly, walking gait. Human walking gaits are a result of complex, interdependent factors that include variations resulting from embodiments, environment and tasks, making techniques that use average template frameworks suboptimal for systematic analysis or corrective interventions. The proposed work aims to devise methodologies for being able to represent gaits and gait transitions such that optimal policies that eliminate the inter-personal variations from tasks and embodiments may be recovered. Our approach is built upon (i) work in the domain of nullspace policy recovery and (ii) previous work in generalisation for point-to-point movements. The problem is formalised using a walking-phase model, and the nullspace learning method is used to generalise a consistent policy from multiple observations with rich variations. Once recovered, the underlying policies (mapped to different gait phases) can serve as reference guideline to quantify and identify pathological gaits while being robust against interpersonal and task variations. To validate our methods, we have demonstrated robustness of our method with simulated sagittal two-link gait data with multiple ground truth constraints and policies. Pathological gait identification was then tested on real-world human gait data with induced gait abnormality, with the proposed method showing significant robustness to variations in speed and embodiment compared to template-based methods. Future work will extend this to kinetic features and higher dimensional features.
There is increasing interest in control frameworks capable of moving robots from industrial cages to unstructured environments and coexisting with humans. Despite significant improvement in some specific applications (e.g., medical robotics), there is still the need for a general control framework that improves interaction robustness and motion dynamics. Passive controllers show promising results in this direction; however, they often rely on virtual energy tanks that can guarantee passivity as long as they do not run out of energy. In this paper, a Fractal Attractor is proposed to implement a variable impedance controller that can retain passivity without relying on energy tanks. The controller generates a Fractal Attractor around the desired state using an asymptotic stable potential field, making the controller robust to discretization and numerical integration errors. The results prove that it can accurately track both trajectories and end-effector forces during interaction. Therefore, these properties make the controller ideal for applications requiring robust dynamic interaction at the end-effector.
Thraustochytrids are heterotrophic fungus-like protists that can dissolve organic matters with enzymes. Four strains, AP45, ASP1, ASP2, and ASP4, were isolated from the coastal water of Taiwan, and respectively identified as Aurantiochytrium sp., Schizochytrium sp., Parietichytrium sp., and Botryochytrium sp. based on 18S rRNA sequences. Transcriptome datasets of these four strains at days 3-5 were generated using Next Generation Sequencing technology, and screened for enzymes with potential industrial applications. Functional annotations based on KEGG database suggest that many unigenes of all four strains were related to the pathways of industrial enzymes. Most of all four strains contained homologous genes for 15 out of the 17 targeted enzymes, and had extra-and/or intra-cellular enzymatic activities, including urease, asparaginase, lipase, glucosidase, alkaline phosphatase and protease. Complete amino sequences of the first-time identified L-asparaginase and phytase in thraustochytrids were retrieved, and respectively categorized to the Type I and BPPhy families based on phylogenetic relationships, protein structural modeling and active sites. Milligram quantities of highly purified, soluble protein of urease and L-asparaginase were successfully harvested and analyzed for recombinant enzymatic activities. These analytical results highlight the diverse enzymes for widerange applications in thraustochytrids.
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