cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/v7vw-ed38
title: Supplementary Material To: A Safety Benchmark for Capacitive Proximity Sensing in Robotics – Safe Transient Contact Compliant to ISO TS 15066: Power and Force Limiting

description: Supplementary Data for Publication "A Safety Benchmark for Capacitive Proximity Sensing in Robotics - Safe Transient Contact in Compliance to ISO TS 15066: Power and Force Limiting The Supplementary Data includesSTL files of the test objects (already available)Measurement Data of each institute for the shown test objects (will be uploaded soon) Disclaimer: This publication was submitted and is currently under review to be published for IEEE RA-L with ICRA 2021 option. Fundings: The research leading to this results has received fundingfrom the ”K ̈arntner Wirtschaftsf ̈orderung Fonds” (KWF) andthe ”European Regional Development Fund” (EFRE) withinthe CapSize project 26616/30969/44253 and from the Fed-eral Ministry of Education and Research (Bundesministeriumf ̈ur Bildung und Forschung in Germany, BMWF) within theproject SINA (Verbundprojekt-Nr.: V5ARA114) Preliminary version of the abstract: During the co-presence of human workers and robots in the same workspace, Power and Force Limiting (PFL) according to ISO TS 15066 is required to avoid injuries during undesired contacts. Capacitive Proximity Sensors (CPSs) offer a cost-effective solution to cover the entire robot manipulator with a fast close-range perception for Human-Robot Collaboration (HRC) tasks. Compared to pure tactile or force sensing, the sensors allow to increase the operating speed of cobots while still maintaining PFL. The robot reacts before impacts occur and does not suffer from occlusion, as the sensors can be placed directly on the robot’s surface to cover the full area and close the perception gap between tactile detection and mid-range distance sensors. However, since capacitive coupling to obstacles varies with distance, shape, and material properties,the projection from capacitance to actual distances is a general problem. A reference benchmark is required for the evaluation to ensure safe PFL. We derive the requirements described in ISO TS 15066 for PFL of critical body regions and propose a method for determining the pre-detection operation speed to comply with PFL. We propose an universal benchmark test procedure for fellow researchers to evaluate their capacitive sensors for safe PFL. With researchers from four different institutes presenting three different concepts of capacitive sensors and their applicability on different robots, the benchmark test procedure is evaluated, and the general applicability is demonstrated.
DOI: 10.6084/m9.figshare.12941639
title: Data for "Impact of the reference list features on the number of citations"

description: The database (tab-delimited file) and R script to generate the analysis in the study:<br>Mammola, S., Martinez, A., Fontaneto, D., & Chichorro, F. (2020). Scientometric correlates of high-quality reference lists in ecological papers. Scientometrics, in press. https://doi.org/10.1007/s11192-020-03759-0 <br>Note that the database was constructed based on a raw database downloaded from Web of Science (see main text for details).
DOI: 10.6084/m9.figshare.13102388
title: Supplementary Material for: Relationship between Serum Uric Acid and Mortality Risk in Hemodialysis Patients: A Multicenter Prospective Cohort Study

description: <b><i>Background:</i></b> Several studies have reported that low serum uric acid (SUA) levels are related to increased risk of mortality in maintenance hemodialysis (MHD) patients. However, the possible detrimental effects of high SUA on the mortality risk have not been well examined. Moreover, the possible effect modifiers for the SUA-mortality association have not been fully investigated. To address the aforementioned gap, we aimed to explore the nonlinear relationship between SUA levels and all-cause and cardiovascular disease (CVD) mortality risk, and to examine any possible effect modifiers in MHD patients. <b><i>Methods:</i></b> We conducted a multicenter, prospective cohort study among 1,018 MHD patients from 8 hemodialysis centers. The primary outcome was all-cause mortality, and the secondary outcomes were CVD mortality and non-CVD mortality. <b><i>Results:</i></b> The mean value for SUA in the total population was 8.5 ± 1.9 mg/dL. The lowest and highest quintiles of SUA were &lt;7.0 and &gt;10.1 mg/dL, respectively. Over a median follow-up of 45.6 months, 343 deaths were recorded, of which 202 (58.9%) were due to CVD. When SUA was assessed as quintiles, a significantly higher risk of all-cause mortality was found in patients in quintile 1 (&lt;7.0 mg/dL; hazard ratio [HR], 1.33; 95% confidence interval [CI]: 1.02–1.73) or quintile 5 (≥10.1 mg/dL; HR, 1.47; 95% CI: 1.09–2.00), compared to those in quintiles 2–4 (7–10.1 mg/dL). Moreover, the U-shaped SUA-mortality association was mainly found in those with lower C-reactive protein levels (&lt;3 compared with ≥3 mg/L; <i>p</i> for interaction = 0.018). Similar trends were found for CVD mortality and non-CVD mortality. <b><i>Conclusion:</i></b> There was a U-shaped relationship between SUA levels and the risk of all-cause mortality, CVD mortality, and non-CVD mortality in MHD patients.
DOI: 10.5281/zenodo.4094723
title: Data for monitoring stakeholder engagement_v2

description: These data are collected during the project’s stakeholder engagement activities with a view to effectively measuring and reporting the progress of these activities. With that in mind, a dedicated methodological tool has been designed and was being employed throughout the duration of INVITE, namely the Stakeholder Matrix. In this respect, this Stakeholder matrix included anonymised and aggregated data about key stakeholders (organization type, gender and region / country).
DOI: 10.5281/zenodo.4094722
title: Data for monitoring stakeholder engagement_v2

description: These data are collected during the project’s stakeholder engagement activities with a view to effectively measuring and reporting the progress of these activities. With that in mind, a dedicated methodological tool has been designed and was being employed throughout the duration of INVITE, namely the Stakeholder Matrix. In this respect, this Stakeholder matrix included anonymised and aggregated data about key stakeholders (organization type, gender and region / country).
DOI: 10.21227/d0ss-3486
title: Cloud benchmarking

description: Cloud computing
DOI: 10.21227/nsz3-9583
title: A Multispectral Light Field Dataset for Light Field Deep Learning

description: Deep learning undoubtedly has had a huge impact on the computer vision community in recent years. In light field imaging, machine learning-based applications have significantly outperformed their conventional counterparts. Furthermore, multi- and hyperspectral light fields have shown promising results in light field-related applications such as disparity or shape estimation. Yet, a multispectral light field data\-set, enabling data-driven approaches, is missing. Therefore, we propose a new synthetic multispectral light field dataset with depth and disparity ground truth. The dataset consists of a training, validation and test dataset, containing light fields of randomly generated scenes, as well as a challenge dataset rendered from hand-crafted scenes enabling detailed performance assessment.
DOI: 10.21227/46ct-ny67
title: A Multispectral Light Field Dataset for Light Field Deep Learning

description: Deep learning undoubtedly has had a huge impact on the computer vision community in recent years. In light field imaging, machine learning-based applications have significantly outperformed their conventional counterparts. Furthermore, multi- and hyperspectral light fields have shown promising results in light field-related applications such as disparity or shape estimation. Yet, a multispectral light field data\-set, enabling data-driven approaches, is missing. Therefore, we propose a new synthetic multispectral light field dataset with depth and disparity ground truth. The dataset consists of a training, validation and test dataset, containing light fields of randomly generated scenes, as well as a challenge dataset rendered from hand-crafted scenes enabling detailed performance assessment.
DOI: 10.5281/zenodo.4094813
title: An algorithm to quantify intratumor heterogeneity based on alterations of gene expression profiles: Data availability

description: DEPTH evaluates the tumor heterogeneity level of each tumor sample based on gene expression profiles Heterogeneity score.
DOI: 10.5281/zenodo.3968335
title: An algorithm to quantify intratumor heterogeneity based on alterations of gene expression profiles: Data availability

description: DEPTH evaluates the tumor heterogeneity level of each tumor sample based on gene expression profiles Heterogeneity score.
DOI: 10.6084/m9.figshare.13102391.v1
title: OD9 dataset part 11/28

description: 435 032 nwchem logfiles calculated thanks to the quchempedia BOINC project.DFT calculations (B3LYP / 321G)The first 184 158 molecules corresond to the recalculation of QM9 and PC9. The others are newly generated molecules with the EvoMol software.<br>Beware the concaneted tar file is 141GB of compressed files.<br>
DOI: 10.6084/m9.figshare.13102391
title: OD9 dataset part 11/28

description: 435 032 nwchem logfiles calculated thanks to the quchempedia BOINC project.DFT calculations (B3LYP / 321G)The first 184 158 molecules corresond to the recalculation of QM9 and PC9. The others are newly generated molecules with the EvoMol software.<br>Beware the concaneted tar file is 141GB of compressed files.<br>
DOI: 10.6084/m9.figshare.13102394
title: OD9 dataset part 12/28

description: 435 032 nwchem logfiles calculated thanks to the quchempedia BOINC project.DFT calculations (B3LYP / 321G)The first 184 158 molecules corresond to the recalculation of QM9 and PC9. The others are newly generated molecules with the EvoMol software.<br>Beware the concaneted tar file is 141GB of compressed files.<br>
DOI: 10.6084/m9.figshare.13102394.v1
title: OD9 dataset part 12/28

description: 435 032 nwchem logfiles calculated thanks to the quchempedia BOINC project.DFT calculations (B3LYP / 321G)The first 184 158 molecules corresond to the recalculation of QM9 and PC9. The others are newly generated molecules with the EvoMol software.<br>Beware the concaneted tar file is 141GB of compressed files.<br>
DOI: 10.5281/zenodo.3665273
title: Magnetic and all-optical switching properties of amorphous TbxCo100−x alloys

description: Supporting data for the paper
DOI: 10.5281/zenodo.3665272
title: Magnetic and all-optical switching properties of amorphous TbxCo100−x alloys

description: Supporting data for the paper
DOI: 10.21227/ex7c-bs67
title: WFSM gait

description: This dataset contains normal gait data for 13 healthy individuals acquired using a wireless foot sensor module
DOI: 10.21227/tnq3-k069
title: WFSM gait

description: This dataset contains normal gait data for 13 healthy individuals acquired using a wireless foot sensor module
DOI: 10.6084/m9.figshare.13102502
title: Dataset of frequency of occurrence of words in title of journal articles of different fields in biology

description: Article title should theoretically reflect the content to which it represents. But, there is a paucity of quantitative methods for measuring the relevance of a title to an article’s content. One approach for quantifying the relevance of a title with respect to an article’s content is through mining the frequency of occurrence of words in the article title in the main text of the article. Profiling of high frequency of occurrence of many words in the title would suggests higher degree of relevance of the title with respect to the article’s content. But this must be taken in light of the caveat that some titles are phrased to express the conclusion of a paper, and would thus suffer from a lower frequency of occurrence of words of the title in the main text. This work reports a dataset that has been obtained through analysing the frequency of occurrence of words in the title of journal article in the main text of the article. This process is aided by an in-house MATLAB software coded for the project. Articles from different fields of biology such as biochemistry, bioinformatics, biotechnology, cell biology, computational biology, genetics, genomics, microbiology, molecular biology, structural biology, synthetic biology, and systems biology were put through the same analysis, and an Excel file detailing the frequency of occurrence of words in the title of the article serve as the output. The data is also visualized as a bar chart in the same Excel file.
DOI: 10.6084/m9.figshare.13102520
title: Conjectural-Variations Equilibria in Electricity, Natural-Gas, and Carbon-Emission Markets

description: The data of a three-bus power system and three-node natural-gas system is attached.
DOI: 10.6084/m9.figshare.13102457
title: OD9 dataset part 13/28

description: 435 032 nwchem logfiles calculated thanks to the quchempedia BOINC project.DFT calculations (B3LYP / 321G)The first 184 158 molecules corresond to the recalculation of QM9 and PC9. The others are newly generated molecules with the EvoMol software.<br>Beware the concaneted tar file is 141GB of compressed files.<br>
DOI: 10.6084/m9.figshare.13102457.v1
title: OD9 dataset part 13/28

description: 435 032 nwchem logfiles calculated thanks to the quchempedia BOINC project.DFT calculations (B3LYP / 321G)The first 184 158 molecules corresond to the recalculation of QM9 and PC9. The others are newly generated molecules with the EvoMol software.<br>Beware the concaneted tar file is 141GB of compressed files.<br>
DOI: 10.5517/ccdc.csd.cc21ln3f
title: CCDC 1894754: Experimental Crystal Structure Determination

description: Related Article: Lingling Lv, Josiah Roberts, Chengyi Xiao, Zhenmei Jia, Wei Jiang, Guowei Zhang, Chad Risko, Lei Zhang|2019|Chemical Science|10|4951|doi:10.1039/C9SC00849G
DOI: 10.5517/ccdc.csd.cc20v5pr
title: CCDC 1872204: Experimental Crystal Structure Determination

description: Related Article: Yutong Wang, Liang Feng, Weidong Fan, Kun-Yu Wang, Xia Wang, Xiaokang Wang, Kai Zhang, Xiurong Zhang, Fangna Dai, Daofeng Sun, Hong-Cai Zhou|2019|J.Am.Chem.Soc.|141|6967|doi:10.1021/jacs.9b00122
DOI: 10.5517/ccdc.csd.cc20v5qs
title: CCDC 1872205: Experimental Crystal Structure Determination

description: Related Article: Yutong Wang, Liang Feng, Weidong Fan, Kun-Yu Wang, Xia Wang, Xiaokang Wang, Kai Zhang, Xiurong Zhang, Fangna Dai, Daofeng Sun, Hong-Cai Zhou|2019|J.Am.Chem.Soc.|141|6967|doi:10.1021/jacs.9b00122
DOI: 10.5517/ccdc.csd.cc20v5rt
title: CCDC 1872206: Experimental Crystal Structure Determination

description: Related Article: Yutong Wang, Liang Feng, Weidong Fan, Kun-Yu Wang, Xia Wang, Xiaokang Wang, Kai Zhang, Xiurong Zhang, Fangna Dai, Daofeng Sun, Hong-Cai Zhou|2019|J.Am.Chem.Soc.|141|6967|doi:10.1021/jacs.9b00122
DOI: 10.5517/ccdc.csd.cc20v5tw
title: CCDC 1872208: Experimental Crystal Structure Determination

description: Related Article: Yutong Wang, Liang Feng, Weidong Fan, Kun-Yu Wang, Xia Wang, Xiaokang Wang, Kai Zhang, Xiurong Zhang, Fangna Dai, Daofeng Sun, Hong-Cai Zhou|2019|J.Am.Chem.Soc.|141|6967|doi:10.1021/jacs.9b00122
DOI: 10.5517/ccdc.csd.cc20v5sv
title: CCDC 1872207: Experimental Crystal Structure Determination

description: Related Article: Yutong Wang, Liang Feng, Weidong Fan, Kun-Yu Wang, Xia Wang, Xiaokang Wang, Kai Zhang, Xiurong Zhang, Fangna Dai, Daofeng Sun, Hong-Cai Zhou|2019|J.Am.Chem.Soc.|141|6967|doi:10.1021/jacs.9b00122
DOI: 10.5517/ccdc.csd.cc24csy8
title: CCDC 1977550: Experimental Crystal Structure Determination

description: Related Article: Jiaxin Tian, Yan Chen, Marie Vayer, Alexandre Djurovic, Régis Guillot, Refka Guermazi, Samuel Dagorne, Chritophe Bour, Vincent Gandon|2020|Chem.-Eur.J.|26|12831|doi:10.1002/chem.202001376
DOI: 10.5517/ccdc.csd.cc24cvbq
title: CCDC 1977593: Experimental Crystal Structure Determination

description: Related Article: Luyao Li,  Tianxiao Yang,  Tao Zhang,  Bo Zhu,  Junbiao Chang|2020|J.Org.Chem.|85|12294|doi:10.1021/acs.joc.0c01545
DOI: 10.5517/ccdc.csd.cc24dggh
title: CCDC 1978186: Experimental Crystal Structure Determination

description: Related Article: Alexander F. Pozharskii,  Olga V. Dyablo,  Olga G. Pogosova,  Valery A. Ozeryanskii,  Aleksander Filarowski,  Kseniya M. Vasilikhina,  Narek A. Dzhangiryan|2020|J.Org.Chem.|85|12468|doi:10.1021/acs.joc.0c01697
DOI: 10.5517/ccdc.csd.cc24dghj
title: CCDC 1978187: Experimental Crystal Structure Determination

description: Related Article: Alexander F. Pozharskii,  Olga V. Dyablo,  Olga G. Pogosova,  Valery A. Ozeryanskii,  Aleksander Filarowski,  Kseniya M. Vasilikhina,  Narek A. Dzhangiryan|2020|J.Org.Chem.|85|12468|doi:10.1021/acs.joc.0c01697
DOI: 10.5517/ccdc.csd.cc24dgjk
title: CCDC 1978188: Experimental Crystal Structure Determination

description: Related Article: Alexander F. Pozharskii,  Olga V. Dyablo,  Olga G. Pogosova,  Valery A. Ozeryanskii,  Aleksander Filarowski,  Kseniya M. Vasilikhina,  Narek A. Dzhangiryan|2020|J.Org.Chem.|85|12468|doi:10.1021/acs.joc.0c01697
DOI: 10.5517/ccdc.csd.cc24dgkl
title: CCDC 1978189: Experimental Crystal Structure Determination

description: Related Article: Alexander F. Pozharskii,  Olga V. Dyablo,  Olga G. Pogosova,  Valery A. Ozeryanskii,  Aleksander Filarowski,  Kseniya M. Vasilikhina,  Narek A. Dzhangiryan|2020|J.Org.Chem.|85|12468|doi:10.1021/acs.joc.0c01697
DOI: 10.5517/ccdc.csd.cc24dgmn
title: CCDC 1978191: Experimental Crystal Structure Determination

description: Related Article: Alexander F. Pozharskii,  Olga V. Dyablo,  Olga G. Pogosova,  Valery A. Ozeryanskii,  Aleksander Filarowski,  Kseniya M. Vasilikhina,  Narek A. Dzhangiryan|2020|J.Org.Chem.|85|12468|doi:10.1021/acs.joc.0c01697
DOI: 10.5517/ccdc.csd.cc24dgnp
title: CCDC 1978192: Experimental Crystal Structure Determination

description: Related Article: Alexander F. Pozharskii,  Olga V. Dyablo,  Olga G. Pogosova,  Valery A. Ozeryanskii,  Aleksander Filarowski,  Kseniya M. Vasilikhina,  Narek A. Dzhangiryan|2020|J.Org.Chem.|85|12468|doi:10.1021/acs.joc.0c01697
DOI: 10.5517/ccdc.csd.cc24dgpq
title: CCDC 1978193: Experimental Crystal Structure Determination

description: Related Article: Alexander F. Pozharskii,  Olga V. Dyablo,  Olga G. Pogosova,  Valery A. Ozeryanskii,  Aleksander Filarowski,  Kseniya M. Vasilikhina,  Narek A. Dzhangiryan|2020|J.Org.Chem.|85|12468|doi:10.1021/acs.joc.0c01697
DOI: 10.5517/ccdc.csd.cc24f46y
title: CCDC 1978829: Experimental Crystal Structure Determination

description: Related Article: Yuan-Bin Cheng|2020|CSD Communication|||
description: Related Article: Shu-Rong Chen,  Shih-Wei Wang,  Jyh-Horng Sheu,  Ting-Hsuan Chang,  Yuan-Bin Cheng|2020|J.Org.Chem.|85|12553|doi:10.1021/acs.joc.0c01731
DOI: 10.5517/ccdc.csd.cc24fdgg
title: CCDC 1979085: Experimental Crystal Structure Determination

description: Related Article: Bas Venderbosch, Jean-Pierre H. Oudsen, Jarl Ivar van der Vlugt, Ties J. Korstanje, Moniek Tromp|2020|Organometallics|39|3480|doi:10.1021/acs.organomet.0c00348
DOI: 10.5517/ccdc.csd.cc24fdhh
title: CCDC 1979086: Experimental Crystal Structure Determination

description: Related Article: Bas Venderbosch, Jean-Pierre H. Oudsen, Jarl Ivar van der Vlugt, Ties J. Korstanje, Moniek Tromp|2020|Organometallics|39|3480|doi:10.1021/acs.organomet.0c00348
DOI: 10.5517/ccdc.csd.cc24fdjj
title: CCDC 1979087: Experimental Crystal Structure Determination

description: Related Article: Bas Venderbosch, Jean-Pierre H. Oudsen, Jarl Ivar van der Vlugt, Ties J. Korstanje, Moniek Tromp|2020|Organometallics|39|3480|doi:10.1021/acs.organomet.0c00348
DOI: 10.5517/ccdc.csd.cc24fdkk
title: CCDC 1979088: Experimental Crystal Structure Determination

description: Related Article: Bas Venderbosch, Jean-Pierre H. Oudsen, Jarl Ivar van der Vlugt, Ties J. Korstanje, Moniek Tromp|2020|Organometallics|39|3480|doi:10.1021/acs.organomet.0c00348
DOI: 10.5517/ccdc.csd.cc24fdll
title: CCDC 1979089: Experimental Crystal Structure Determination

description: Related Article: Bas Venderbosch, Jean-Pierre H. Oudsen, Jarl Ivar van der Vlugt, Ties J. Korstanje, Moniek Tromp|2020|Organometallics|39|3480|doi:10.1021/acs.organomet.0c00348
DOI: 10.5517/ccdc.csd.cc24fdmm
title: CCDC 1979090: Experimental Crystal Structure Determination

description: Related Article: Bas Venderbosch, Jean-Pierre H. Oudsen, Jarl Ivar van der Vlugt, Ties J. Korstanje, Moniek Tromp|2020|Organometallics|39|3480|doi:10.1021/acs.organomet.0c00348
DOI: 10.5517/ccdc.csd.cc24fkhn
title: CCDC 1979241: Experimental Crystal Structure Determination

description: Related Article: Liam J. Donnelly, Simon Parsons, Carole A. Morrison, Stephen P. Thomas, Jason B. Love|2020|Chemical Science|11|9994|doi:10.1039/D0SC03458D
DOI: 10.5517/ccdc.csd.cc24fkjp
title: CCDC 1979242: Experimental Crystal Structure Determination

description: Related Article: Liam J. Donnelly, Simon Parsons, Carole A. Morrison, Stephen P. Thomas, Jason B. Love|2020|Chemical Science|11|9994|doi:10.1039/D0SC03458D
DOI: 10.5517/ccdc.csd.cc24fnhr
title: CCDC 1979334: Experimental Crystal Structure Determination

description: Related Article: Majji Shankar, Raja K. Rit, Somratan Sau, Kallol Mukherjee, Vincent Gandon, Akhila K. Sahoo|2020|Chemical Science|11|10770|doi:10.1039/D0SC01373K
DOI: 10.5281/zenodo.4095000
title: Multi-objective Optimization of Long-run Average and Total Rewards: Supplemental Material

description: Supplemental Material for the Paper:<br> Multi-objective Optimization of Long-run Average and Total Rewards<br> by Tim Quatmann and Joost-Pieter Katoen.
DOI: 10.21227/70zv-tc49
title: Hibbot data - proof of concept

description: This dataset contains the output from 3D gait analysis.Over a period of 3 months, between January 1st and March 31st in 2019, 5 children were familiarized with the Hibbot by using the walking aid for 30 minutes, twice a week, under the supervision of a physiotherapist. Physical examination and gait analysis were performed before (December 17 – 21, 2018) and after (April 1 – 5, 2019) a familiarisation period with the Hibbot, at the local gait lab.Children had to meet the following inclusion criteria: confirmed diagnosis of CP, GMFCS level II-IV, age between 2 and 7 years, stature less than 1.25 meters and body weight less than 30 kilograms.Children were encouraged to walk at self-selected speed over the walkway in Hibbot or using their habitual walking aid (if necessary, Table I) in a randomized order. Patients walked either barefoot or with ankle-foot orthoses, if necessary.Kinematics and kinetics of gait were recorded by an optical motion capture system (8 camera’s, 120 Hz., Qualisys Proreflex, Goteborg, Sweden) surrounding a 10-meter walkway and synchronized with 3 force-platforms (two of 0.5mx0.5m and one of 1.0x0.5m; AMTI Accugait, 1000 Hz., Advanced Medical Technology Inc., Massachusetts, USA). Reflective markers were attached to the skin over specific anatomical landmarks:Markers were placed on processus spinosus of the 7th cervical vertebra (C7), incisura jugulars (IJ), processus xyphoideus (PX) and bilaterally over the clavicular head (CC), major trochanteres (TM), lateral epicondyles (LE), lateral malleoli (LM), calcanei (CA) and 2nd metatarsal heads (MTII).Gait analysis data in C3D file format was analysed using visual 3D software (Visual3D Professional v5.01.9, C-motion, Kingston, ON, Canada) and custom models. The body was modelled as an interconnected chain of rigid segments: CC – TM for trunk, TM – LE for thigh, LE – LM for leg and LM – MT for foot. In addition, the thorax was modelled as a 6 – degrees – of – freedom segment with the segment coordinate system definitions partially adapted from the ISB recommendations.