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42 results for “activities of daily life”
Multimodal video and IMU kinematic dataset on daily life activities using affordable devices (VIDIMU)
<p>Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient tracking solutions for remote daily life activities recognition and kinematic analysis. </p> <p>The VIDIMU dataset includes 54 healthy young adults that were recorded on video and 16 of them were simultaneously recorded using custom IMUs. For each subject, 13 activities were registered using a low-resolution video camera and five Inertial Measurement Units (IMUs). Inertial sensors were placed in the lower or the upper limbs of the subject, respectively for activities that involve movement with the lower or the upper body. Video recordings were postprocessed using the state-of-the-art pose estimator <em>BodyTrack</em> (similar to OpenPose, and included in NVIDIA Maxine-AR-SDK) to provide a sequence of 3D joint positions for each movement. Raw IMU recordings were post-processed to compute joint angles by inverse kinematics with <em>OpenSim</em>. For recordings including simultaneous acquisition of video and IMU data types, these signals were used for data file synchronization. Collected data can be further used in applications related to human activity recognition and biomechanics related experiments in simulated home-like settings.</p> <p> </p> <p> </p>
Synthetic Multimodal Dataset for Daily Life Activities
<p><strong>Outline</strong></p> <ul> <li>This dataset is originally created for the <a href="https://challenge.knowledge-graph.jp/2022/">Knowledge Graph Reasoning Challenge for Social Issue</a>s (KGRC4SI)</li> <li>Video data that simulates daily life actions in a virtual space from Scenario Data.</li> <li>Knowledge graphs, and transcriptions of the Video Data content ("who" did what "action" with what "object," when and where, and the resulting "state" or "position" of the object).</li> <li>Knowledge Graph Embedding Data are created for reasoning based on machine learning </li> <li>This data is open to the public as open data</li> </ul> <p><strong>Details</strong></p> <ul> <li> <p><a href="https://github.com/KnowledgeGraphJapan/KGRC-RDF/blob/kgrc4si/Movie">Videos</a></p> <ul> <li>mp4 format</li> <li>203 action scenarios</li> <li>For each scenario, there is a character rear view (file name ending in 0), an indoor camera switching view (file name ending in 1), and a fixed camera view placed in each corner of the room (file name ending in 2-5). Also, for each action scenario, data was generated for a minimum of 1 to a maximum of 7 patterns with different room layouts (scenes). A total of 1,218 videos</li> <li>Videos with slowly moving characters simulate the movements of elderly people.</li> </ul> </li> <li> <p><a href="https://github.com/KnowledgeGraphJapan/KGRC-RDF/blob/kgrc4si/RDF">Knowledge Graphs</a></p> <ul> <li>RDF format</li> <li>203 knowledge graphs corresponding to the videos</li> <li>Includes schema and location supplement information</li> <li>The schema is described below</li> <li><a href="http://kgrc4si.ml:7200/sparql">SPARQL endpoints</a> and <a href="https://github.com/KnowledgeGraphJapan/KGRC-RDF/tree/kgrc4si#%E3%83%8A%E3%83%AC%E3%83%83%E3%82%B8%E3%82%B0%E3%83%A9%E3%83%95%E3%81%AE%E4%BD%BF%E7%94%A8%E6%96%B9%E6%B3%95">query examples</a> are available</li> </ul> </li> <li> <p><a href="https://github.com/KnowledgeGraphJapan/KGRC-RDF/blob/kgrc4si/Program">Script Data</a></p> <ul> <li>txt format</li> <li>Data provided to VirtualHome2KG to generate videos and knowledge graphs</li> <li>Includes the action title and a brief description in text format.</li> </ul> </li> <li>Embedding <ul> <li>Embedding Vectors in TransE, ComplEx, and RotatE. Created with DGL-KE (<a href="https://dglke.dgl.ai/doc/">https://dglke.dgl.ai/doc/</a>)</li> <li>Embedding Vectors created with jRDF2vec (<a href="https://github.com/dwslab/jRDF2Vec">https://github.com/dwslab/jRDF2Vec</a>).</li> </ul> </li> </ul> <p><strong>Specification of Ontology</strong></p> <ul> <li>Please refer to the specification for descriptions of all classes, instances, and properties: <a href="https://aistairc.github.io/VirtualHome2KG/vh2kg_ontology.html">https://aistairc.github.io/VirtualHome2KG/vh2kg_ontology.htm</a></li> </ul> <p><strong>Related Resources</strong></p> <ul> <li><a href="https://www.youtube.com/watch?v=Ajbn8hNXiZ8&list=PLHaRK-B0LUwjvrPgmIBTrf3DsPhmdnFTW">KGRC4SI Final Presentations with automatic English subtitles (YouTube)</a></li> <li><a href="https://github.com/aistairc/VirtualHome2KG">VirtualHome2KG (Software)</a></li> <li><a href="https://github.com/aistairc/virtualhome_unity_aist">VirtualHome-AIST (Unity</a>)</li> <li><a href="https://github.com/aistairc/virtualhome_aist">VirtualHome-AIST (Python API</a>)</li> <li><a href="https://github.com/aistairc/virtualhome2kg_visualization">Visualization Tool</a> (Software)</li> <li><a href="https://github.com/aistairc/virtualhome2kg_generation">Script Editor</a> (Software)</li> </ul>
Daily Life Activities Dataset
<p><strong>Experiment design</strong></p> <p>The Daily Life Activities (DLA) dataset consists of trials of daily life object manipulation tasks performed by a human. The dataset consists of ten tasks: <em>cutting, painting, pouring with cup, putting cup away, quarter turn, scooping and pouring, scooping food, shaking, sinusoidal motion</em>, and <em>table wiping</em>. In this dataset, a high variation in the context was purposefully introduced. That is, the tasks were performed with respect to three different viewpoints (V1, V2, V3) and with four different execution styles (normal, with larger spatial scale, with different velocity profile, and with longer time duration). This resulted in a total of (3x4=12) twelve different contexts in which the tasks were performed. Each task was performed ten times in every context, resulting in a total of (10x3x4x10=1200) trials.</p> <p><strong>Experimental setup</strong></p> <p>The trials were recorded using a Krypton K600 camera from NIKON Metrology by tracking up to nine LED markers attached to the manipulated object. The 3D position of each LED marker was recorded with a sampling rate of 50 Hz and expected accuracy of 0.4mm with respect to the measurement frame of the camera system.</p> <p><strong>Data format</strong></p> <p>Every trial_xxx.mat file is a Matlab structure array. The trailing number xxx refers to the order in which the trials were performed. For every task:</p> <ul> <li> <em>trial_001.mat</em> up to <em>trial_040.mat </em>were recorded in sensor viewpoint 1. <ul> <li><em>trial_001.mat</em> up to <em>trial_010.mat </em>were executed with execution style:<em> normal.</em></li> <li><em>trial_011.mat</em> up to <em>trial_020.mat </em>were executed with execution style:<em> longer time duration.</em></li> <li><em>trial_021.mat</em> up to <em>trial_030.mat </em>were executed with execution style:<em> larger spatial scale.</em></li> <li><em>trial_031.mat</em> up to <em>trial_040.mat </em>were executed with execution style:<em> different velocity profile.</em></li> </ul> </li> <li> <em>trial_041.mat</em> up to <em>trial_080.mat </em>were recorded in sensor viewpoint 2. <ul> <li><em>trial_041.mat</em> up to <em>trial_050.mat </em>were executed with execution style:<em> normal.</em></li> <li><em>trial_051.mat</em> up to <em>trial_060.mat </em>were executed with execution style:<em> longer time duration.</em></li> <li><em>trial_061.mat</em> up to <em>trial_070.mat </em>were executed with execution style:<em> larger spatial scale.</em></li> <li><em>trial_071.mat</em> up to <em>trial_080.mat </em>were executed with execution style:<em> different velocity profile.</em></li> </ul> </li> <li><em> trial_081.mat</em> up to <em>trial_120.mat </em>were recorded in sensor viewpoint 3. <ul> <li><em>trial_081.mat</em> up to <em>trial_090.mat </em>were executed with execution style:<em> normal.</em></li> <li><em>trial_091.mat</em> up to <em>trial_100.mat </em>were executed with execution style:<em> longer time duration.</em></li> <li><em>trial_101.mat</em> up to <em>trial_110.mat </em>were executed with execution style:<em> larger spatial scale.</em></li> <li><em>trial_111.mat</em> up to <em>trial_120.mat </em>were executed with execution style:<em> different velocity profile.</em></li> </ul> </li> </ul> <p>The structure array trial_xxx.mat has the following fields:</p> <ul> <li>'number_of_timesamples': the total number of timesamples (N) for the recorded task,</li> <li>'K6C_12250_3_x': a 4xN matrix containing the 3D position coordinates of the LED marker expressed in millimeters. The trailing number x in 'K6C_12250_3_x' refers to the LED number, which can range from 1 to 9. <ul> <li>In case the LED marker was visible, the first, second and third row contain the x-, y-, and z-coordinates of the marker, respectively. The fourth row contains the zero value in this case.</li> <li>In case the LED marker was not visible, the first, second and third row contain zero values. The fourth row contains a non-zero value in this case.</li> </ul> </li> </ul>
Physiological signals during activities for daily life: Dataset
<p>The dataset used in this work is composed by four participants, two men and two women. Each of them carried the wearable device Empatica E4 for a total number of 15 days. They carried the wearable during the day, and during the nights we asked participants to charge and load the data into an external memory unit. During these days, participants were asked to answer EMA questionnaires which are used to label our data. However, some participants could not complete the full experiment or some days were discarded due to data corruption. Specific demographic information, total sampling days and total number of EMA answers can be found in table I.</p> <p> </p> <table align="center"> <thead> <tr> <th scope="col"> </th> <th scope="col">Participant 1</th> <th scope="col">Participant 2</th> <th scope="col">Participant 3</th> <th scope="col">Participant 4</th> </tr> </thead> <tbody> <tr> <td>Age</td> <td>67</td> <td>55</td> <td>60</td> <td>63</td> </tr> <tr> <td>Gender</td> <td>Male</td> <td>Female</td> <td>Male</td> <td>Female</td> </tr> <tr> <td> <p>Final Valid Days</p> </td> <td>9</td> <td>15</td> <td>12</td> <td>13</td> </tr> <tr> <td>Total EMAs</td> <td>42</td> <td>57</td> <td>64</td> <td>46</td> </tr> </tbody> </table> <p> Table I. Summary of participants' collected data.</p> <p> </p> <p>This dataset provides three different type of labels. <em>Activeness</em> and <em>happiness</em> are two of these labels. These are the answers to EMA questionnaires that participants reported during their daily activities. These labels are numbers between <em>0</em> and <em>4</em>.<br> These labels are used to interpolate the mental well-being state according to [1] We report in our dataset a total number of eight emotional states: (1) pleasure, (2) excitement, (3) arousal, (4) distress, (5) misery, (6) depression, (7) sleepiness, and (8) contentment.</p> <p>The data we provide in this repository consist of two type of files:</p> <ul> <li><strong>CSV files</strong>: These files contain physiological signals recorded during the data collection process. The first line of each CSV file defines the timestamp by which data started being sampled. The second line defines the sampling frequency used for gathering the signal. From the third line until the end of the file, one can find sampled datapoints. <br> </li> <li><strong>Excel files</strong>: These files contain the labels obtained from EMA answers. It is indicated the timestamp at which the answer was registered. Labels for <em>pleasure</em>, <em>activeness</em> and <em>mood</em> can be found in this file. </li> </ul> <p><strong>NOTE: </strong>Files are numbered according to each specific sampling day. For example, ACC1.csv corresponds to the signal ACC for sampling day 1. The same applied to excel files.</p> <p> </p> <p>Code and a tutorial of how to labelled and extract features can be found in this repository: <a href="https://github.com/edugm94/temporal-feat-emotion-prediction">https://github.com/edugm94/temporal-feat-emotion-prediction</a></p> <p> </p> <p>References:</p> <p>[1] . A. Russell, “A circumplex model of affect,” Journal of personality and social psychology, vol. 39, no. 6, p. 1161, 1980</p>
Effect of Selexipag on Daily Life Physical Activity of Patients With Pulmonary Arterial Hypertension.
ClinicalTrials.gov study NCT03078907. IPD Sharing: Not stated. Countries: 10. Publications: 2.
Evaluation of Outcomes for Quality of Life and Activities of Daily Living for BKP in the Treatment of VCFs
ClinicalTrials.gov study NCT01871519. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of Respiratory Muscle Training and Respiratory Exercise in Exercise Tolerance, Performing Daily Life Activities and Quality of Life of Patients With Chronic Obstructive Pulmonary Disease
ClinicalTrials.gov study NCT01510041. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Correlation Between Daily Physical Activity and Disability, Fatigue, Cognition and Quality of Life in MS Patients
ClinicalTrials.gov study NCT04115930. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Assessment of Advanced Glaucomatous Visual Field Loss and Its Impact on Visual Exploration, Activities of Daily Living (ADL) and Quality of Life (QoL)
ClinicalTrials.gov study NCT01372319. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Effects of Resistance Training on Physical Activity in Daily Life and Functional Capacity in Hemodialysis Patients
ClinicalTrials.gov study NCT02651025. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Effects of Lumbopelvic Rhythm on Postural Control, Daily Activities, and Quality of Life in Individuals With AIS
ClinicalTrials.gov study NCT07391488. IPD Sharing: NO. Countries: 1. Publications: 6.
Improving Quality of Life and Daily Life Activities With Bioarginine in Patients With COPD
ClinicalTrials.gov study NCT05412160. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
The Impact of Exercise on Hand Function, Daily Activities Performance and Quality of Life of SLE' Patients
ClinicalTrials.gov study NCT03802578. IPD Sharing: NO. Countries: 1. Publications: 1.
Physical Activities in Daily Life After Lung Transplantation
ClinicalTrials.gov study NCT00395889. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Assessment of Homonymous Visual Loss and Its Impact on Visual Exploration, Activities of Daily Living (ADL) and Quality of Life (QoL)
ClinicalTrials.gov study NCT01372332. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Investigation on a New Power Knee Firmware Update on Gait and Daily Life Activities for Transfemoral Amputees
ClinicalTrials.gov study NCT06620861. IPD Sharing: NO. Countries: 1. Publications: 0.
Data for: "Random forest algorithms for recognizing daily life activities using plantar pressure information: A smart-shoe study"
<p>Data and software related to the manuscript "Random forest algorithms for recognizing daily life activities using plantar pressure information: A smart-shoe study" (2020). </p> <p>========</p> <p>Typo in README_DataRep.txt:</p> <p>' 2) "one_config" [...] Selected results requiring these data are shown in Fig. <strong>13</strong>'<strong> </strong>(not 11).</p>
Effects of Robot-assisted Arm Training on Respiratory Muscle Strength, Activities of Daily Living and Quality of Life in Stroke Patients: A Single-blinded Randomized Controlled Trial
ClinicalTrials.gov study NCT05299853. IPD Sharing: NO. Countries: 1. Publications: 0.
Effect of a Daily Life Activity-Based Awareness Training Program on Hypertensive Individuals
ClinicalTrials.gov study NCT06547879. IPD Sharing: NO. Countries: 1. Publications: 0.
Daily Life Activities in Patients Treated With Continuous-Flow Left Ventricular Assist Devices
ClinicalTrials.gov study NCT04732039. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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