cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17876/rave/dr.6/fields/20051111_0030m11
title: RAVE Field 20051111_0030m11

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051111_0255m28
title: RAVE Field 20051111_0255m28

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051112_0028m77
title: RAVE Field 20051112_0028m77

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051112_0207m76
title: RAVE Field 20051112_0207m76

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051112_0315m08
title: RAVE Field 20051112_0315m08

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051112_2349m11
title: RAVE Field 20051112_2349m11

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051113_0043m82
title: RAVE Field 20051113_0043m82

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051113_0252m10
title: RAVE Field 20051113_0252m10

description: A field from the RAVE survey
DOI: 10.5281/zenodo.3891118
title: Data set from the article Mazzaccaro D, Muzzarelli L, Modafferi A, Nano G. Sizes of endografts for endovascular aortic repair: do few fit most? Ann Ital Chir. 2019;90:287-291. PMID: 31657351.

description: Data set from the article Mazzaccaro D, Muzzarelli L, Modafferi A, Nano G. Sizes of endografts for endovascular aortic repair: do few fit most? Ann Ital Chir. 2019;90:287-291. PMID: 31657351. This is the abstract <strong>Aim: </strong> The endoprostheses for the endovascular aortic repair (EVAR) of abdominal aortic aneurysms (AAA), are currently available in many sizes in reference to the aortic diameters of the proximal neck, but often not all of them are really used. Aim of our work was to review in our experience the most frequent proximal aortic diameters of main bodies that were used, among all those available for EVAR, with respect to the native proximal aortic neck. <strong>Methods: </strong> All the sizes of main bodies of the different endografts used for EVAR from 2000 to 2016 were retrospectively counted. For each endograft, we calculated the number of times each size of main bodies' proximal diameter was used. The mean diameter of the proximal aortic neck was also calculated for each group of main bodies. <strong>Results: </strong> From 2000 to 2016, 607 patients underwent EVAR for infrarenal AAA. Overall, mean diameter of the proximal aortic neck was 23.4 ± 0.5 mm (median 23.1 mm, IQR 22.2-23.7 mm). The most frequently used main bodies had a 28 mm, 26 mm and 25 mm proximal diameter (161/607, 26.5%; 147/607, 24.2%; 122/607, 20.1% respectively), for a mean proximal neck diameter of 23.2 ± 0.5 mm, 22.2 ± 0.4 mm and 22.1 ± 0.2 respectively. The least frequently used main bodies had a 21 mm and a 36 mm proximal diameter (3/607 times each, 0.5%), for a mean proximal neck diameter of 18.1 ± 0.2 mm and 32.4 ± 0.8 mm respectively. <strong>Conclusions: </strong> In our experience, the most frequently used main bodies had a 25, 26 and 28 mm proximal diameters.
DOI: 10.5281/zenodo.3891117
title: Data set from the article Mazzaccaro D, Muzzarelli L, Modafferi A, Nano G. Sizes of endografts for endovascular aortic repair: do few fit most? Ann Ital Chir. 2019;90:287-291. PMID: 31657351.

description: Data set from the article Mazzaccaro D, Muzzarelli L, Modafferi A, Nano G. Sizes of endografts for endovascular aortic repair: do few fit most? Ann Ital Chir. 2019;90:287-291. PMID: 31657351. This is the abstract <strong>Aim: </strong> The endoprostheses for the endovascular aortic repair (EVAR) of abdominal aortic aneurysms (AAA), are currently available in many sizes in reference to the aortic diameters of the proximal neck, but often not all of them are really used. Aim of our work was to review in our experience the most frequent proximal aortic diameters of main bodies that were used, among all those available for EVAR, with respect to the native proximal aortic neck. <strong>Methods: </strong> All the sizes of main bodies of the different endografts used for EVAR from 2000 to 2016 were retrospectively counted. For each endograft, we calculated the number of times each size of main bodies' proximal diameter was used. The mean diameter of the proximal aortic neck was also calculated for each group of main bodies. <strong>Results: </strong> From 2000 to 2016, 607 patients underwent EVAR for infrarenal AAA. Overall, mean diameter of the proximal aortic neck was 23.4 ± 0.5 mm (median 23.1 mm, IQR 22.2-23.7 mm). The most frequently used main bodies had a 28 mm, 26 mm and 25 mm proximal diameter (161/607, 26.5%; 147/607, 24.2%; 122/607, 20.1% respectively), for a mean proximal neck diameter of 23.2 ± 0.5 mm, 22.2 ± 0.4 mm and 22.1 ± 0.2 respectively. The least frequently used main bodies had a 21 mm and a 36 mm proximal diameter (3/607 times each, 0.5%), for a mean proximal neck diameter of 18.1 ± 0.2 mm and 32.4 ± 0.8 mm respectively. <strong>Conclusions: </strong> In our experience, the most frequently used main bodies had a 25, 26 and 28 mm proximal diameters.
DOI: 10.17876/rave/dr.6/fields/20051113_0401m18
title: RAVE Field 20051113_0401m18

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051113_2342m23
title: RAVE Field 20051113_2342m23

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051123_0030m11
title: RAVE Field 20051123_0030m11

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051123_0207m76
title: RAVE Field 20051123_0207m76

description: A field from the RAVE survey
DOI: 10.34644/laudatio-dev-fiticxmb7carcq9c_tqo
title: Sächsiche Weltchronik
DOI: 10.17876/rave/dr.6/fields/20051205_0043m82
title: RAVE Field 20051205_0043m82

description: A field from the RAVE survey
DOI: 10.34644/laudatio-dev-xstfcxmb7carcq9clzal
title: RIDGES Herbology Version 4.1
DOI: 10.17876/rave/dr.6/fields/20051205_0304m15
title: RAVE Field 20051205_0304m15

description: A field from the RAVE survey
DOI: 10.34644/laudatio-dev-zcsgcxmb7carcq9czbo4
title: RIDGES Herbology Version 4.0
DOI: 10.17876/rave/dr.6/fields/20051205_0603m29
title: RAVE Field 20051205_0603m29

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051205_0707m47
title: RAVE Field 20051205_0707m47

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051207_0510m13
title: RAVE Field 20051207_0510m13

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051207_0736m57
title: RAVE Field 20051207_0736m57

description: A field from the RAVE survey
DOI: 10.34644/laudatio-dev-hitacxmb7carcq9cdy3m
title: Muspilli
DOI: 10.17876/rave/dr.6/fields/20051208_0133m59
title: RAVE Field 20051208_0133m59

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051208_0302m48
title: RAVE Field 20051208_0302m48

description: A field from the RAVE survey
DOI: 10.34644/laudatio-dev-wys4cxmb7carcq9cvcub
title: Ludolf
DOI: 10.17876/rave/dr.6/fields/20051208_0408m23
title: RAVE Field 20051208_0408m23

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051208_0720m52
title: RAVE Field 20051208_0720m52

description: A field from the RAVE survey
DOI: 10.34644/laudatio-dev-7ysucxmb7carcq9ccica
title: Heliand
DOI: 10.17876/rave/dr.6/fields/20051209_0213m49
title: RAVE Field 20051209_0213m49

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051209_0319m80
title: RAVE Field 20051209_0319m80

description: A field from the RAVE survey
DOI: 10.34644/laudatio-dev-aisscxmb7carcq9csyme
title: Anselm Corpus
DOI: 10.17876/rave/dr.6/fields/20051209_0456m20
title: RAVE Field 20051209_0456m20

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051209_0629m46
title: RAVE Field 20051209_0629m46

description: A field from the RAVE survey
DOI: 10.34644/laudatio-dev-oyr1cxmb7carcq9ctrpo
title: RIDGES Herbology Version 2.0
DOI: 10.17876/rave/dr.6/fields/20051210_0239m61
title: RAVE Field 20051210_0239m61

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051210_0357m57
title: RAVE Field 20051210_0357m57

description: A field from the RAVE survey
DOI: 10.34644/laudatio-dev-dsr5cxmb7carcq9cerba
title: RIDGES Herbology Version 3.0
DOI: 10.17876/rave/dr.6/fields/20051210_0520m42
title: RAVE Field 20051210_0520m42

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051210_0649m57
title: RAVE Field 20051210_0649m57

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051210_0655m42
title: RAVE Field 20051210_0655m42

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051211_0242m53
title: RAVE Field 20051211_0242m53

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051211_0403m68
title: RAVE Field 20051211_0403m68

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051212_0639m51
title: RAVE Field 20051212_0639m51

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051218_0206m42
title: RAVE Field 20051218_0206m42

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051218_0255m28
title: RAVE Field 20051218_0255m28

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051218_0441m29
title: RAVE Field 20051218_0441m29

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051218_0644m36
title: RAVE Field 20051218_0644m36

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051219_0202m30
title: RAVE Field 20051219_0202m30

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051219_0322m31
title: RAVE Field 20051219_0322m31

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051219_0446m35
title: RAVE Field 20051219_0446m35

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051219_0554m24
title: RAVE Field 20051219_0554m24

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051220_0201m24
title: RAVE Field 20051220_0201m24

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051220_0254m34
title: RAVE Field 20051220_0254m34

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051220_0449m03
title: RAVE Field 20051220_0449m03

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051220_0612m35
title: RAVE Field 20051220_0612m35

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051220_0923m17
title: RAVE Field 20051220_0923m17

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051221_0620m40
title: RAVE Field 20051221_0620m40

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20051222_0603m29
title: RAVE Field 20051222_0603m29

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060106_0408m08
title: RAVE Field 20060106_0408m08

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060107_0416m46
title: RAVE Field 20060107_0416m46

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060107_0707m47
title: RAVE Field 20060107_0707m47

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060107_0859m19
title: RAVE Field 20060107_0859m19

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060108_0350m35
title: RAVE Field 20060108_0350m35

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060108_0624m26
title: RAVE Field 20060108_0624m26

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060109_0427m05
title: RAVE Field 20060109_0427m05

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060109_0545m19
title: RAVE Field 20060109_0545m19

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060122_0449m46
title: RAVE Field 20060122_0449m46

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060122_0612m35
title: RAVE Field 20060122_0612m35

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060122_0736m57
title: RAVE Field 20060122_0736m57

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060122_0915m23
title: RAVE Field 20060122_0915m23

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060122_1040m32
title: RAVE Field 20060122_1040m32

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060122_1203m44
title: RAVE Field 20060122_1203m44

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060123_0620m40
title: RAVE Field 20060123_0620m40

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060123_0908m12
title: RAVE Field 20060123_0908m12

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060123_1036m38
title: RAVE Field 20060123_1036m38

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060125_0932m27
title: RAVE Field 20060125_0932m27

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060126_0456m20
title: RAVE Field 20060126_0456m20

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060126_0614m68
title: RAVE Field 20060126_0614m68

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060126_0655m42
title: RAVE Field 20060126_0655m42

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060126_0940m21
title: RAVE Field 20060126_0940m21

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060126_1103m41
title: RAVE Field 20060126_1103m41

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060126_1132m43
title: RAVE Field 20060126_1132m43

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060127_0504m26
title: RAVE Field 20060127_0504m26

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060127_0629m46
title: RAVE Field 20060127_0629m46

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060127_0940m21
title: RAVE Field 20060127_0940m21

description: A field from the RAVE survey
DOI: 10.21227/qpwk-zc15
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, pp. 868-873. DOI: 10.1109/ICORR.2019.8779540.
DOI: 10.17876/rave/dr.6/fields/20060128_0644m36
title: RAVE Field 20060128_0644m36

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060128_0649m57
title: RAVE Field 20060128_0649m57

description: A field from the RAVE survey
DOI: 10.21227/dx52-wt35
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, pp. 868-873. DOI: 10.1109/ICORR.2019.8779540.
DOI: 10.17876/rave/dr.6/fields/20060128_0908m12
title: RAVE Field 20060128_0908m12

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060128_1103m41
title: RAVE Field 20060128_1103m41

description: A field from the RAVE survey
DOI: 10.17632/y69wnv829s.1
title: Data for: Evaluating the Utility of Fasting Lipid Panel In Addition to Random Lipid Panel In Determining Lipid-Lowering Therapy in Acute Ischemic Stroke or TIA Patients

description: Objective. To determine if obtaining fasting lipid levels in addition to random lipid levels prompts changes in hyperlipidemia management of acute stroke patients. Methods. 206 patients met the study criteria which included a diagnosis of acute ischemic stroke or transient ischemic attack on admission and availability of both random and fasting LDL levels collected within 72 hours of each other. The dataset was analyzed to conform to the 2018 AHA/ACC guidelines using an LDL cutoff of 70 mg/dL. Results. In 206 patients, statin management would change based on the fasting LDL level in 12 patients, 11 of whom were in Group B. Our data suggests that lipid management is more likely to change if the initial random LDL falls between 70-99 mg/dL as compared to a value outside of this range (P&lt;0.001).
DOI: 10.17876/rave/dr.6/fields/20060128_1203m44
title: RAVE Field 20060128_1203m44

description: A field from the RAVE survey
DOI: 10.21227/4cs1-wz59
title: ExoNet Database: Wearable Camera Images of Human Locomotion Environments

description: Abstract: Recent advances in computer vision and artificial intelligence have allowed researchers to develop environment recognition systems for lower-limb exoskeletons and prostheses. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms for human locomotion environment recognition. To address these shortcomings, 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 comprises over 5.6 million images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable smartphone camera system throughout the summer, fall, and winter seasons. Approximately 940,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through the IEEE DataPort repository, ExoNet offers an unprecedented community-based platform for training, developing, and comparing next-generation image classification algorithms for human locomotion environment recognition. Beyond the control of lower-limb exoskeletons and prostheses, applications of ExoNet extend to humanoid and autonomous legged robotics.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. In Preparation.
DOI: 10.17876/rave/dr.6/fields/20060129_0447m15
title: RAVE Field 20060129_0447m15

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060129_0639m51
title: RAVE Field 20060129_0639m51

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060129_0707m47
title: RAVE Field 20060129_0707m47

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060129_0940m21
title: RAVE Field 20060129_0940m21

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060129_1117m10
title: RAVE Field 20060129_1117m10

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060129_1235m45
title: RAVE Field 20060129_1235m45

description: A field from the RAVE survey
DOI: 10.21227/4exz-tz33
title: Measurement and Evaluation of Human Sitting and Standing Movement Biomechanics

description: Although regenerative actuators can extend the operating durations of robotic lower-limb exoskeletons and prostheses, these energy-efficient powertrains have been exclusively designed and evaluated for level-ground walking. Building on previous research, we analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamics modelling and simulation to estimate the biomechanical energy available for electrical regeneration during activities more representative of aging and rehabilitation populations. 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 parameter identification, whereby differences in ground reaction forces and moments between the experimental measurements and inverse dynamic simulations were minimized. Joint mechanical power was calculated from net joint torques and rotational velocities and numerically integrated over time to determine joint biomechanical energy. The hip generated 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 80-kg bodyweight and previously published regenerative actuator efficiencies (i.e., maximum 63%), robotic lower-limb exoskeletons and prostheses could theoretically regenerate ~26 Joules of electrical energy while sitting down, compared to ~19 Joules during level-ground walking. Future research should focus on activity tracking and biomechanical analyses of seniors and rehabilitation patients to better estimate the biomechanical energy available for electrical regeneration among 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. Under Review.
DOI: 10.17876/rave/dr.6/fields/20060130_0510m31
title: RAVE Field 20060130_0510m31

description: A field from the RAVE survey
DOI: 10.17876/rave/dr.6/fields/20060130_0644m36
title: RAVE Field 20060130_0644m36

description: A field from the RAVE survey