Find research datasets worth reusing

opencc-by-4.0Nov 2024View details →

ExoMove - Kinematics of Daily Activities with Lower-Limb Exoskeletons

This dataset reports the lower-limb kinematics of healthy individuals during various daily activities (sitting, walking, stair ascending and descending, and transitions between them) while using two distinct lower-limb exoskeletons, eWalk and Autonomyo. The dataset captures the biomechanical differences between the exoskeletons, offering a rich resource for advancing exoskeleton design and control for assistive and rehabilitative applications.

opencc-by-4.0Mar 2024View details →

Error Related Potential at the start of the gait with a lower limb exoskeleton

<h2>Description</h2> This dataset contains EEG signals from experiments designed to evoke Error Related Potentials (ErrP) at the onset of gait using a Brain-Computer Interface (BCI) to control a lower limb exoskeleton. The ErrP is elicited using three different stimuli: Tactile, Visual, and VisuoTactile. During the experiment, participants remain stationary and engage in two mental tasks: Relax (R) and Motor Imagery (I) of walking to activate the exoskeleton. These tasks can be executed correctly (RC, IC) or incorrectly (RE, IE). For example, during RC (Relax Correct), the subject maintains an idle state, whereas during RE (Relax Error), the exoskeleton activates unexpectedly. Conversely, in IC, the subject imagines the sensation of starting to walk in their muscles, and the exoskeleton activates, but during IE, the exoskeleton does not move despite the motor imagery. When the exoskeleton activates before starting to walk, the stimulus remains active for 2 seconds to alert the subject about the impending movement. Therefore, ErrP is elicited by the stimuli in RE and can be compared with the absence of ErrP in IC, where the stimulus activates but should not evoke an error. Each subject participates in three sessions, one for each stimulus, consisting of 12 trials. In each trial, 10 mental tasks are performed, 5 Relax and 5 Imagination, interleaved. Since the subject is never in control of the system, tasks are correctly performed 70% of the time (RC, IC), while the remaining 30% are incorrect (RE, IE). In an exception, subject R01_VisuoTactile performed 7 trials of 20 mental tasks each, 10 of each type. However, due to the extended duration of the trials and resulting fatigue, they were later split for subsequent sessions.   <h2>Data information</h2> A trial consists of a Matlab structure that stores all information related to the trial experiment.  <ul> <li>data_EEG: Original EEG signals recorded with a sampling rate of 250Hz, where each row is a channel (1-28 EEG, 29-32 EOG, 33-35 inertial electrodes).</li> <li>data_preprocessed_EEG: Matrix that contains the preprocessed signals for each channel. Rows 1-35 are the original signals and then, the preprocessed signals in blocks of 35. Find the indexes of each filter in session.conf.info.preprocessingSteps.ListPreprocessingSteps.</li> <li>trigger_EEG: Information related to signal quality and missing data while recording. </li> <li>data_EXO: Exoskeleton recorded data with a sampling rate of 250Hz.</li> <li>data_preprocessed_EXO: The same data recorded by the exoskeleton in data_EXO, since it does not require the application of any filter.</li> <li>trigger_EXO: Empty vector. </li> <li>data_Actuators:  Arduino response when activates (1) and deactivates (-1) the feedback. </li> <li>data_preprocessed_Actuators: The same Arduino resposes recorded in data_Actuators, because it does not require any filter application. </li> <li>trigger_Actuators: Empty vector. </li> <li>task_EEG: Vector that associates a task to each signal sample.</li> <li>task_index_EEG: Zero vector with negative peaks at the samples indicating the start of a task. Each peak decrements by one unit with each task.  </li> <li>task_order_EEG: Vector that increments a unit with each task change. </li> <li>event_EEG: Vector of commands to activate (1) and deactivate (-1) the feedback in Arduino. </li> <li>conf: Configuration employed for data acquisition and preprocessing. <ul> <li>acquisition: User and signals acquisition information. <ul> <li>user_code: User code name.</li> <li>feedback: Trial in openloop (User do not have control of the system).</li> <li>feedbackErrP: Feedback type employed during the trial.</li> <li>readfile: Path to read files after its acquisition.</li> <li>saveSession_Script: Script used to save the recorded data.</li> <li>writeResults: Path to save the recorded data.</li> <li>device: List of connected devices during the trial and their related information, such as name, sampling rate, connection order, etc.  </li> <li>task: Information about tasks occurring during the trial. <ul> <li>task_list: Decodes tasks numbers. The first number is the global task/mental activity, the second one is the physiological state of the user, and the third one indicates the task version (preparation or basic task).</li> <li>sequence_tasks: List of tasks in order of execution.</li> <li>sequence_times: List with the duration of each task in the sequence.</li> </ul> </li> <li>deviceOutput: List of devices that receive commands to execute orders, such as the exoskeleton for walking and stopping and the VibroLed for turning feeedback on and off.</li> <li>eye_index: Indexes of EOG electrodes.</li> <li>EEG_index: Indexes of EEG electrodes.</li> <li>inertial_index: Indexes of inertial electrodes.</li> <li>file_name: Trial name.</li> <li>num_epochs: Number of epochs within a trial. An epoch is the half of sampling rate (250Hz), this means that an epoch has a duration of 0.5s and 125 samples.  </li> </ul> </li> <li>preadjustment: Empty list. </li> <li>preprocessing: Information of the preprocessing filters, parameters and order of application.</li> <li>processing: Not necessary for this analysis. </li> <li>static: Information used internally by the architecture for its correct operation.</li> <li>info: Important information about filters, their order and indexes in data_processed_EEG.</li> </ul> </li> <li>times: Struct with information of the devices synchronization and preprocessing times.</li> <li>times_processing: Processing duration times. </li> </ul>

opencc-by-4.0May 2024View details →

Dataset for study: Adaptive Hip Exoskeleton Control using Heart Rate Feedback Reduces Oxygen Cost during Ecological Locomotion

This record contains the dataset for a manuscript currently under preparation and submission. See the description PDF file for more details. The information here will be updated according to the progress in the peer review and publication procedure.

Fig. 5 in Sequence of post-moult exoskeleton hardening preserved in a trilobite mass moult assemblage from the Lower Ordovician Fezouata Konservat-Lagerstätte, Morocco

Fig. 5. Mid-moult specimen of Limulus polyphemus Linnaeus, 1758 (YPMIZ 55597), Recent, USA, in dorsal (A1), ventral (A2), and anterior (A3) views. The moult is fully hardened and shows a robust convex exoskeleton, whereas the carcass partially emerged but trapped within the old exoskeleton shows extensive lateral wrinkling of its new exoskeleton (arrowed). Photo Russell Bicknell.

Fig. 1 in Sequence of post-moult exoskeleton hardening preserved in a trilobite mass moult assemblage from the Lower Ordovician Fezouata Konservat-Lagerstätte, Morocco

Fig. 1. Trilobite referred to Symphysurus ebbestadi Gutiérrez-Marco, Rábano, and García-Bellido, 2018, from the early Ordovician of Morocco (Tigzigzaouine area), in dorsal views, under standard lighting. A. MGL 102127. B. MGL 102128. C. MGL 102129. D. MGL 102130; D2 close up of thorax axial rings in D1, showing the clear terrace ridges. E. MGL 102131. F. MGL 102132. G. MGL 102133. H. MGL 102134. I. MGL 102135. Scale bars 5 mm.

Fig. 4 in Sequence of post-moult exoskeleton hardening preserved in a trilobite mass moult assemblage from the Lower Ordovician Fezouata Konservat-Lagerstätte, Morocco

Fig. 4. Graphs showing means (points) and ranges of exoskeleton thickness for cephala (A) and thoraces (B) of the thin sectioned trilobites Symphysurus ebbestadi.

Fig. 3 in Sequence of post-moult exoskeleton hardening preserved in a trilobite mass moult assemblage from the Lower Ordovician Fezouata Konservat-Lagerstätte, Morocco

Fig. 3. Thin sections showing the cuticular structure of trilobites Symphysurus ebbestadi Gutiérrez-Marco, Rábano and García-Bellido 2018, from the early Ordovician of Tigzigzaouine area, Morocco. A. MGL 102127, the putative moult. B. MGL 102130, a fully-hardened individual. C. MGL 102133, individual with medium levels of wrinkling. D. MGL 102134, the most wrinkled individual. A1–D1, anterodorsal sections through the cephalon (except C1, transverse section); A2–D2, anterodorsal sections through the thorax. Scale bars 1 mm.

Fig. 2 in Sequence of post-moult exoskeleton hardening preserved in a trilobite mass moult assemblage from the Lower Ordovician Fezouata Konservat-Lagerstätte, Morocco

Fig. 2. Wrinkled specimens of trilobite Symphysurus ebbestadi Gutiérrez-Marco, Rábano, and García-Bellido 2018, from the early Ordovician of Tigzigzaouine area, Morocco, photographed under low-angle incident lighting, in order to emphasise the three-dimensional surface texture of their exoskeletons. Specimens are organised in relative order of exoskeleton hardening, from that with the most wrinkled and soft exoskeleton (A) to the least wrinkled (D) before being fully hardened. A. MGL 102132. B. MGL 102134. C. MGL 102128. D. MGL 102133. Scale bars 5 mm.

opencc-by-4.0Mar 2009View details →

Fig. 4 in Red Devonian trilobites with green eyes from Morocco and the silicification of the trilobite exoskeleton

Fig. 4. Map of Hamar Laghdad showing the mud−mounds, the distribution of the red facies, the position of the red cliff, erosional remnants of the Cretaceous transgression conglomerate and the distribution of outcrops of Middle Devonian sediments (map based on satellite images, the topographic map sheet "Erfoud, feuille NH−30−XX−2, Carte du Maroc 1/100000", and Berkowski 2006: fig. 1).

opencc-by-4.0Mar 2009View details →

Fig. 3 in Red Devonian trilobites with green eyes from Morocco and the silicification of the trilobite exoskeleton

Fig. 3. SEM−SE image of cephalon PIMUZ 27076 and results of the element analyses ("EDX−spektrum X"). A. SEM−SE image of the eye and result of the element analysis of a lense ("EDX−spektrum 1"). Note the absence of iron and silica. B. SEM−SE image of the glabella and result of the element analysis ("EDX−spektrum 9"). Iron and silica are present. C. Analysis performed in the intralensar area ("EDX−spektrum 2") of the glabella(see A). Iron and silica are present.

opencc-by-4.0Mar 2009View details →

Fig. 1 in Red Devonian trilobites with green eyes from Morocco and the silicification of the trilobite exoskeleton

Fig. 1. Geologic map of Morocco and the Tafilalt (eastern Anti−Atlas), showing the position of the "red cliff" at Hamar Laghdad (modified after Klug 2007).

dryad40/100

User preference optimization for control of ankle exoskeletons using sample efficient active learning

A major challenge to the widespread success of augmentative exoskeletons is accurately adjusting the controller to provide cooperative assistance with their wearer. Often, the controller parameters are ``tuned'' to optimize a physiological or biomechanical objective. However, these approaches are resource-intensive, while typically only enabling optimization of a single objective. In reality, the exoskeleton user experience is derived from many factors, including comfort and stability, among others. This work introduces an approach to conveniently tune four parameters of the exoskeleton controller that maximize user preference. We use an evolutionary algorithm to recommend potential parameters, which are ranked by a neural network that is pre-trained with previously collected preference data. The controller parameters that have the highest preference ranking are provided to the exoskeleton, and the wearer provides feedback as forced-choice comparisons. Our approach was able to converge on controller parameters preferred by the wearer compared to randomized parameters with an accuracy of 88% on average. The result indicates that the proposed algorithm was able to identify users' preferences while requiring less than 50 queries to users. This work demonstrates user preference can be used to tune high-dimensional controller spaces easily and accurately, which shows the potential of translating lower-limb wearable technologies into our daily lives.

opencc-zeroOct 2023View details →

dryad40/100

User preference optimization for control of ankle exoskeletons using sample efficient active learning

Open the record for dataset details and reuse information.

publicOct 2023View details →

opencc-zeroJan 2021View details →

Ocean acidification alters properties of the exoskeleton in adult tanner crabs, Chionoecetes bairdi

Ocean acidification can affect the ability of calcifying organisms to build and maintain mineralized tissue. In decapod crustaceans, the exoskeleton is a multilayered structure composed of chitin, protein, and mineral, predominately magnesian calcite or amorphous calcium carbonate (ACC). We investigated the effects of acidification on the exoskeleton of mature (post-terminal-molt) female southern Tanner crabs, Chionoecetes bairdi. Crabs were exposed to one of three pH levels—8.1, 7.8, or 7.5—for two years. Reduced pH led to a suite of body-region-specific effects on the exoskeleton. Microhardness of the claw was 38% lower in crabs at pH 7.5 compared with those at pH 8.1, but carapace microhardness was unaffected by pH. In contrast, reduced pH altered elemental content in the carapace (reduced calcium, increased magnesium), but not the claw. Diminished structural integrity and thinning of the exoskeleton was observed at reduced pH in both body regions; internal erosion of the carapace was present in most crabs at pH 7.5, and the claws of these crabs showed substantial external erosion, with tooth-like denticles nearly or completely worn away. Using infrared spectroscopy, we observed a shift in the phase of calcium carbonate present in the carapace of pH-7.5 crabs: a mix of ACC and calcite was found in the carapace of crabs at pH 8.1, whereas the bulk of calcium carbonate had transformed to calcite in pH-7.5 crabs. With limited capacity for repair, the exoskeleton of long-lived crabs that undergo a terminal molt, such as C. bairdi, may be especially susceptible to ocean acidification.

opencc-zeroMar 2024View details →

Estimating human joint moments unifies exoskeleton control and reduces user effort

Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance based on instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average RMSE of 0.142 ± 0.021 Nm/kg and R2 of 0.840 ± 0.045 across 35 ambulatory conditions without any subject-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level ground and incline walking compared to walking without wearing the exoskeleton (P < 0.05). This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community.

opencc-zeroDec 2021View details →

Large and exaggerated sexually selected weapons comprise high proportions of metabolically inexpensive exoskeleton

The cost-minimization hypothesis proposes that positive allometry in sexually selected traits can be explained if the proportional energetic maintenance costs of weapons decrease as traits increase in size. Energetic maintenance costs are the costs of maintaining homeostasis. They are slow, persistent energy sinks that are distinct from ephemeral costs of growth. Because some tissues expend more energy on maintenance than others, energetic maintenance costs can be inferred from proportional tissue composition. For example, soft tissues require more energy for maintenance than exoskeleton, so an arthropod claw that is 50% soft tissue and 50% exoskeleton would have higher energetic maintenance costs than one that is 30% soft tissue and 70% exoskeleton. I tested the cost-minimization hypothesis using proportional tissue composition as a proxy for energetic maintenance costs in snapping shrimp ( Alpheus heterochaelis and Alpheus estuariensis ) and fiddler crabs ( Uca pugilator ). As predicted, larger weapons comprised proportionally less soft tissue mass and more exoskeleton mass than smaller weapons. Furthermore, I extended cost-minimization to explain trait exaggeration: individuals might exaggerate traits by investing more mass in exoskeleton. As predicted, exoskeleton mass proportional to weapon mass increased as exaggeration increased. These results support and extend the cost-minimization hypothesis to explain positive allometry and weapon exaggeration.