cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/b835-yq12
title: Measurement and Simulation of Human Sitting and Standing Movement Biomechanics

description: Previous studies of robotic exoskeletons and prostheses with regenerative actuators have focused exclusively on level-ground walking applications. Here we analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamic simulations to estimate the biomechanical energy available for electrical regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using system parameter identification. Joint mechanical powers were calculated from net joint torques and rotational velocities and numerically integrated over time to estimate joint biomechanical energies. The hip produced the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee (0.8 ± 0.3 W/kg) and ankle (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle joints per stand-to-sit movement were 0.35 ± 0.06 J/kg, 0.15 ± 0.08 J/kg, and 0.02 ± 0.01 J/kg, respectively. Assuming known regenerative actuator efficiencies (i.e., maximum 63%), robotic exoskeletons and prostheses could theoretically regenerate ~26 Joules of electrical energy while sitting down, compared to ~19 Joules per walking stride. Given that these regeneration performance calculations were based on healthy young adults, future studies should involve seniors and/or rehabilitation patients to better estimate the biomechanical energy available for electrical regeneration in individuals with mobility impairments.Reference:Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. bioRxiv. DOI: 10.1101/801258.
DOI: 10.21227/ar3z-kt45
title: Measurement and Simulation of Human Sitting and Standing Movement Biomechanics

description: Previous studies of robotic exoskeletons and prostheses with regenerative actuators have focused exclusively on level-ground walking applications. Here we analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamic simulations to estimate the biomechanical energy available for electrical regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using system parameter identification. Joint mechanical powers were calculated from net joint torques and rotational velocities and numerically integrated over time to estimate joint biomechanical energies. The hip produced the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee (0.8 ± 0.3 W/kg) and ankle (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle joints per stand-to-sit movement were 0.35 ± 0.06 J/kg, 0.15 ± 0.08 J/kg, and 0.02 ± 0.01 J/kg, respectively. Assuming known regenerative actuator efficiencies (i.e., maximum 63%), robotic exoskeletons and prostheses could theoretically regenerate ~26 Joules of electrical energy while sitting down, compared to ~19 Joules per walking stride. Given that these regeneration performance calculations were based on healthy young adults, future studies should involve seniors and/or rehabilitation patients to better estimate the biomechanical energy available for electrical regeneration in individuals with mobility impairments. Reference: Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. bioRxiv. DOI: 10.1101/801258.
DOI: 10.21227/x5ad-x294
title: Measurement and Simulation of Human Sitting and Standing Movement Biomechanics

description: Previous studies of robotic exoskeletons and prostheses with regenerative actuators have focused exclusively on level-ground walking applications. Here we analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamic simulations to estimate the biomechanical energy available for electrical regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using system parameter identification. Joint mechanical powers were calculated from net joint torques and rotational velocities and numerically integrated over time to estimate joint biomechanical energies. The hip produced the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee (0.8 ± 0.3 W/kg) and ankle (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle joints per stand-to-sit movement were 0.35 ± 0.06 J/kg, 0.15 ± 0.08 J/kg, and 0.02 ± 0.01 J/kg, respectively. Assuming known regenerative actuator efficiencies (i.e., maximum 63%), robotic exoskeletons and prostheses could theoretically regenerate ~26 Joules of electrical energy while sitting down, compared to ~19 Joules per walking stride. Given that these regeneration performance calculations were based on healthy young adults, future studies should involve seniors and/or rehabilitation patients to better estimate the biomechanical energy available for electrical regeneration in individuals with mobility impairments.Reference:Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. bioRxiv. DOI: 10.1101/801258.
DOI: 10.21227/j5e0-sh70
title: Measurement and Simulation of Human Sitting and Standing Movement Biomechanics

description: Previous studies of robotic exoskeletons and prostheses with regenerative actuators have focused exclusively on level-ground walking applications. Here we analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamic simulations to estimate the biomechanical energy available for electrical regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using system parameter identification. Joint mechanical powers were calculated from net joint torques and rotational velocities and numerically integrated over time to estimate joint biomechanical energies. The hip produced the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee (0.8 ± 0.3 W/kg) and ankle (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle joints per stand-to-sit movement were 0.35 ± 0.06 J/kg, 0.15 ± 0.08 J/kg, and 0.02 ± 0.01 J/kg, respectively. Assuming known regenerative actuator efficiencies (i.e., maximum 63%), robotic exoskeletons and prostheses could theoretically regenerate ~26 Joules of electrical energy while sitting down, compared to ~19 Joules per walking stride. Given that these regeneration performance calculations were based on healthy young adults, future studies should involve seniors and/or rehabilitation patients to better estimate the biomechanical energy available for electrical regeneration in individuals with mobility impairments.Reference:Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. bioRxiv. DOI: 10.1101/801258.
DOI: 10.21227/w902-gt09
title: Measurement and Simulation of Human Sitting and Standing Movement Biomechanics

description: Previous studies of robotic exoskeletons and prostheses with regenerative actuators have focused exclusively on level-ground walking applications. Here we analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamic simulations to estimate the biomechanical energy available for electrical regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using system parameter identification. Joint mechanical powers were calculated from net joint torques and rotational velocities and numerically integrated over time to estimate joint biomechanical energies. The hip produced the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee (0.8 ± 0.3 W/kg) and ankle (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle joints per stand-to-sit movement were 0.35 ± 0.06 J/kg, 0.15 ± 0.08 J/kg, and 0.02 ± 0.01 J/kg, respectively. Assuming known regenerative actuator efficiencies (i.e., maximum 63%), robotic exoskeletons and prostheses could theoretically regenerate ~26 Joules of electrical energy while sitting down, compared to ~19 Joules per walking stride. Given that these regeneration performance calculations were based on healthy young adults, future studies should involve seniors and/or rehabilitation patients to better estimate the biomechanical energy available for electrical regeneration in individuals with mobility impairments.Reference:Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. bioRxiv. DOI: 10.1101/801258.
DOI: 10.21227/w01e-7c60
title: Measurement and Simulation of Human Sitting and Standing Movement Biomechanics

description: Previous studies of robotic exoskeletons and prostheses with regenerative actuators have focused exclusively on level-ground walking applications. Here we analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamic simulations to estimate the biomechanical energy available for electrical regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using system parameter identification. Joint mechanical powers were calculated from net joint torques and rotational velocities and numerically integrated over time to estimate joint biomechanical energies. The hip produced the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee (0.8 ± 0.3 W/kg) and ankle (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle joints per stand-to-sit movement were 0.35 ± 0.06 J/kg, 0.15 ± 0.08 J/kg, and 0.02 ± 0.01 J/kg, respectively. Assuming known regenerative actuator efficiencies (i.e., maximum 63%), robotic exoskeletons and prostheses could theoretically regenerate ~26 Joules of electrical energy while sitting down, compared to ~19 Joules per walking stride. Given that these regeneration performance calculations were based on healthy young adults, future studies should involve seniors and/or rehabilitation patients to better estimate the biomechanical energy available for electrical regeneration in individuals with mobility impairments.Reference:Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. bioRxiv. DOI: 10.1101/801258.
DOI: 10.6084/m9.figshare.13498653
title: Article Ecological conditions and interpersonal skills leading to ECE teachers' competences in working with immigrant children.xlsx (codebook, database)

description: The file contains results of EFA with variables:- demograpfhic variable (DV)- environment support (SE)- teachers' competences in work with immigrant children (TC)- positive teacher attitudes in work with immigrant children (TA)- teacher values (TV)- emotional comeptences (EC)<br>Plus database (file Baza 2_EN..) of the research and varibles description.
DOI: 10.5281/zenodo.4382084
title: Repository of data supporting the thesis "Poly-algorithmic Techniques in Real Quantifier Elimination"

description: Dataset of various files (as a .zip) supporting the PhD thesis "Poly-algorithmic Techniques in Real Quantifier Elimination" by Zak Tonks, University of Bath. The PhD thesis is in the area of Quantifier Elimination over the Reals (QE) in Computer Algebra. The PhD thesis concerns implementation of algorithms in Quantifier Elimination, which largely culminates in the package QuantifierElimination for the Computer Algebra software Maple. Much of this repository is output of the benchmarking of this package against various other packes in Maple and otherwise. Otherwise there are some auxiliary tools and files to assist with working with QE in Maple, converting between various formats, and understanding case studies and the package QuantifierElimination via software demoes as Maple worksheets. An overview of the contents of this repository (as a .zip file, which contains subdirectories described in the README):<br> - The example databases contributed from the project, as files that can be read into Maple defining tables of examples, and associated functions to build or examine various examples,<br> - A pdf file providing the references for all examples from the example databases, and typesetting of the examples as associated QE problems,<br> - The benchmarking data produced from the benchmarking of the project as csv (comma separated value) files, and the Excel workbooks (xlsx files) processing said data into survival plots for the thesis,<br> - Copies of the survival plots themselves as .png files,<br> - The bash and Maple scripts used to generate the raw benchmarking data, that can be reused, including documentation how to do so in the associated README,<br> - Other auxiliary tools allowing for conversion of QE formulae between formats (such as that of SyNRAC, RegularChains, QuantifierElimination (amongst packages in Maple), and QEPCAD B.<br> - Maple worksheets and the associated exported pdf files used in software demos at conferences to demonstrate features of QuantifierElimination.<br> - Some pdf files demonstrating early case studies on Lazard curtains generated from an early development build of QuantifierElimination. Lastly, there is a README with more detail on the files of the repository further. To emulate the benchmarking of the thesis, an understanding of bash and potentially Maple is assumed, but the raw data from the project is provided here. Before QuantifierElimination's official release, the source code and/or Maple package as an .mla file is available for interested parties upon request to the author Zak Tonks (zak.p.tonks@bath.edu). Any other queries about this data or associated work should be directed to this email address. The author's PhD supervisor's email address is J.H.Davenport@bath.ac.uk.
description: {"references": ["Z. Tonks. Poly-algorithmic Techniques in Real Quantifier Elimination. PhD Thesis, University of Bath, 2021."]}
DOI: 10.5281/zenodo.4399242
title: Repository of data supporting the thesis "Poly-algorithmic Techniques in Real Quantifier Elimination"

description: Dataset of various files (as a .zip) supporting the PhD thesis "Poly-algorithmic Techniques in Real Quantifier Elimination" by Zak Tonks, University of Bath. The PhD thesis is in the area of Quantifier Elimination over the Reals (QE) in Computer Algebra. The PhD thesis concerns implementation of algorithms in Quantifier Elimination, which largely culminates in the package QuantifierElimination for the Computer Algebra software Maple. Much of this repository is output of the benchmarking of this package against various other packes in Maple and otherwise. Otherwise there are some auxiliary tools and files to assist with working with QE in Maple, converting between various formats, and understanding case studies and the package QuantifierElimination via software demoes as Maple worksheets. An overview of the contents of this repository (as a .zip file, which contains subdirectories described in the README):<br> - The example databases contributed from the project, as files that can be read into Maple defining tables of examples, and associated functions to build or examine various examples,<br> - A pdf file providing the references for all examples from the example databases, and typesetting of the examples as associated QE problems,<br> - The benchmarking data produced from the benchmarking of the project as csv (comma separated value) files, and the Excel workbooks (xlsx files) processing said data into survival plots for the thesis,<br> - Copies of the survival plots themselves as .png files,<br> - The bash and Maple scripts used to generate the raw benchmarking data, that can be reused, including documentation how to do so in the associated README,<br> - Other auxiliary tools allowing for conversion of QE formulae between formats (such as that of SyNRAC, RegularChains, QuantifierElimination (amongst packages in Maple), and QEPCAD B.<br> - Maple worksheets and the associated exported pdf files used in software demos at conferences to demonstrate features of QuantifierElimination.<br> - Some pdf files demonstrating early case studies on Lazard curtains generated from an early development build of QuantifierElimination. Lastly, there is a README with more detail on the files of the repository further. To emulate the benchmarking of the thesis, an understanding of bash and potentially Maple is assumed, but the raw data from the project is provided here. Before QuantifierElimination's official release, the source code and/or Maple package as an .mla file is available for interested parties upon request to the author Zak Tonks (zak.p.tonks@bath.edu). Any other queries about this data or associated work should be directed to this email address. The author's PhD supervisor's email address is J.H.Davenport@bath.ac.uk.
description: {"references": ["Z. Tonks. Poly-algorithmic Techniques in Real Quantifier Elimination. PhD Thesis, University of Bath, 2021."]}
DOI: 10.21227/d4j6-eg70
title: ExoNet Database: Wearable Camera Images of Human Locomotion Environments

description: Abstract: Advances in computer vision and artificial intelligence are allowing researchers to develop environment recognition systems for robotic lower-limb exoskeletons and prostheses. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms for classifying human walking environments. To address these limitations, we developed ExoNet - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in scale and diversity, ExoNet contains over 5.6 million RGB images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable camera system throughout the summer, fall, and winter seasons. Approximately 923,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented communal platform to train, develop, and compare next-generation image classification algorithms for human locomotion environment recognition. Besides the control of lower-limb exoskeletons and prostheses, applications of ExoNet could extend to humanoids and autonomous legged robots.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Frontiers in Robotics and AI, 7, 562061. DOI: 10.3389/frobt.2020.562061.
DOI: 10.6084/m9.figshare.7757069
title: Assessing the performance of 38 machine learning models: the case of land consumption rates in Bavaria, Germany

description: Machine learning (ML) is at the forefront of land-use change modeling. Due to numerous available ML approaches, the model choice is complex and usually based on ad hoc decisions, though informed through a few comparative studies that considered a limited number of models. This study contributes a comprehensive comparison of 38 ML models to examine land consumption rates (LCR) (i.e. the transition of landscapes to built-up areas). We modeled LCR for 2009–2015 in Bavaria, Germany, and predicted rates for 2015–2021 at a municipality level. To assess the performance of each approach, we measured the mean absolute error (MAE), the root-mean-square error (RMSE), and the coefficient of determination (R2) using cross-validation. All algorithms consistently predicted that the land consumption rate for Bavaria will increase. eXtreme gradient boosting decision trees performed best with respect to the RMSE (0.500) and R2 (0.183), while the support vector machine with polynomial kernel has the lowest MAE (0.288). The generalized additive model and the random forest models also performed well. We recommend these ML approaches for future land consumption and land-use change studies. A poor performance was found for recursive partitioning by decision trees, self-organizing maps, and partitioning using deletion, substitution, and addition moves.
DOI: 10.21227/9s24-tp92
title: Wearable RGB Camera Images of Human Locomotion Environments

description: Drawing inspiration from autonomous vehicles, using future environment information could improve the control of wearable biomechatronic devices for assisting human locomotion. To the authors knowledge, this research represents the first documented investigation using machine vision and deep convolutional neural networks for environment recognition to support the predictive control of robotic lower-limb prostheses and exoskeletons. One participant was instrumented with a battery-powered, chest-mounted RGB camera system. Approximately 10 hours of video footage were experimentally collected while ambulating throughout unknown outdoor and indoor environments. The sampled images were preprocessed and individually labelled. A deep convolutional neural network was developed and trained to automatically recognize three walking environments: level-ground, incline staircases, and decline staircases. The environment recognition system achieved 94.85% overall image classification accuracy. Extending these preliminary findings, future research should incorporate other environment classes (e.g., incline ramps) and integrate the environment recognition system with electromechanical sensors and/or surface electromyography for automated locomotion mode recognition. The challenges associated with implementing deep learning on wearable biomechatronic devices are discussed.Reference: Laschowski B, McNally W, McPhee J, and Wong A. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 868-873. DOI: 10.1109/ICORR.2019.8779540.