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80 results for “smartwatch”
Monipar Database: smartwatch movement data to monitor motor competency in subjects with Parkinson's disease
<p>Movement data was collected through smartwatches to monitor motor competence in subjects with Parkinson's Disease (PD). The data set collected for the Monipar study consists of triaxial acceleration data from 21 subjects with PD and 7 healthy control subjects when performing a set of physical exercises while wearing an off-the-shelf smartwatch. Each participant performed the complete set of eight exercises once a week, commonly on the same day and at a similar time. Three Matlab files are provided that contain the raw data of the experimental subgroups: (1) Supervised, (2) Remote, and (3) Healthy control. Additionally, two Matlab files are provided containing the Tremor Labels for selected subjects in the experimental subgroups: (1) Supervised and (2) Remote.</p><p>While the implementation of the experimental protocol for collecting movement data followed a consistent approach for all participants, three distinct experimental subgroups were established:</p><p>Remote group: This subgroup consisted of individuals diagnosed with Parkinson's disease (PD) who completed the experimental protocol at their regular PD association.</p><p>Supervised group: This subgroup comprised PD patients who underwent the experimental protocol under circumstances similar to the remote group. Additionally, clinical scoring (MDS-UPDRS) is reported for this group in the file "MONIPAR SUBJECTS DATA.xlsx"</p><p>Healthy control group: This subgroup consisted of healthy participants who performed exercises under the supervision of research project team members.</p><p>Data was collected using a sample rate of 50Hz and expressed in m/s^2.</p><p>Check the "Monipar_README.txt" file for details about this dataset. Further details are contained in the following reference -- if you use this dataset, please cite:</p><p>Sigcha, L., Polvorinos-Fernández, C., Costa, N., Costa, S., Arezes, P., Gago, M., ... & Pavón, I. "<strong>Monipar: Movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones</strong>". <i>Frontiers in Neurology</i>, <i>14</i>, 1326640. <a href="https://doi.org/10.3389/fneur.2023.1326640">https://doi.org/10.3389/fneur.2023.1326640</a></p><p>References:</p><p>Sigcha, L. et al. (2022). Bradykinesia Detection in Parkinson's Disease Using Smartwatches' Inertial Sensors and Deep Learning Methods. Sensors 11, 3879</p><p>Sigcha, L. et al. (2021). Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks. Sensors 21, 291.</p><p><strong>Funding:</strong></p><p>This research was funded by the following projects:</p><p>(1) "Tecnologías Capacitadoras para la Asistencia, Seguimiento y Rehabilitación de Pacientes con Enfermedad de Parkinson". Centro Internacional sobre el envejecimiento, CENIE (código 0348_CIE_6_E) Interreg V-A España-Portugal (POCTEP).</p><p>(2) FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.</p>
Smartwatch gait dataset in simulated Parkinson's disease restricted arm swing conditions
<p>Movement data was collected through smartwatches to monitor gait impairments in healthy subjects. </p> <p>The dataset collected for this study consists of triaxial acceleration and triaxial gyroscope data from 24 subjects when performing a set of gait activities while wearing a smartwatch in their preferred wrist. Each participant performed three gait activities twice, 30 meters straight walk while carrying progressively heavier loads (0 kg, 2 kg, and 4 kg) to simulate restricted arm swing. So, considering that there were 24 participants, 3 different activities and each activity performed twice, a total of 144 data files were obtained.</p> <p>Data was collected using a sample rate of 50Hz. Acceleration is expressed in m/s^2 and gyroscope data in rad/s.</p> <p>Check the " Bioclite_Restricted_Arm_Swing_Data_README.txt" file for details about this dataset.</p> <p><strong>Funding:</strong></p> <p>This research was funded by the following projects:</p> <div> <p>(1) Proyectos de Generación de Conocimiento 2021. PID2021-123708OB-I00, funded by MCIN/AEI/10.13039/ 501100011033/ FEDER, EU</p> </div>
SMARTED: SMARTwatch Emotion Dataset
<p>The SMARTED (SMARTwatch Emotion Dataset) dataset contains physiological data collected in three groups of people enrolled in a university research study.</p> <p>Refer to the README for further details.</p> <p> </p>
Physiological Data Collected from smartwatch: EDA, Pulse Rate, and Skin Temperature for Stress and Fatigue Analysis
<p>The dataset contains multiple columns capturing both <strong>physiological and demographic data</strong>.<strong> Physiological data</strong>, collected using the <strong>Empatica EmbracePlus smartwatch,</strong> includes electrodermal activity (EDA), pulse rate, and skin temperature. These metrics provide insights into participants' stress and fatigue levels. Empatica's proprietary algorithms preprocess the raw data, extracting digital biomarkers and metrics that reflect the wearer's physiological and behavioral states. <strong>The processed data is aggregated on a per-minute basis.</strong></p> <p>Demographic information, such as age, gender, fitness level, and sleep duration from the previous night, is also included. Additionally, participants rated their perceived physical fatigue on the Borg scale (ranging from 6 to 20), offering a subjective measure of exertion during or after physical tasks.</p> <p>The dataset was collected during controlled simulations of industrial tasks in a fitness environment. These simulations involved repetitive activities, including weightlifting, resistance band exercises, and isometric tasks, designed to mimic the physical demands of industrial work. This approach allowed for the safe and effective study of physical fatigue. The resulting data provides valuable insights into the physiological responses associated with repetitive physical labor.</p>
Activity recognition from in-the-wild smartwatches (ArWISE)
Open the record for dataset details and reuse information.
Monitoring mobility in older adults using a global positioning system (GPS) smartwatch and accelerometer: A validation study
<p><strong>Background</strong></p> <p>There is interest in identifying the most reliable method for detecting early mobility limitations. Accelerometry and Global Positioning System (GPS) could provide insight into declines in mobility, but few studies have used this multi-sensor approach to monitor mobility in older adults. </p> <p><strong>Methods</strong></p> <p>Thirty-two volunteers (66.2±6.3 years) agreed to participate in our validation study. We conducted two experiments to determine the validity of the TicWatch S2 and Pro 3 Ultra GPS models against the Qstarz receiver in measuring life-space mobility, trip frequency, duration, and mode. We also assessed the accuracy of the TicWatch in measuring step count and agreement with the ActiGraph wGT3X-BT for activity counts and sedentary behavior. Participants wore devices simultaneously for three consecutive days and recorded activity and trip information.</p> <p><strong><span>Results</span></strong></p> <p>The TicWatch Pro 3 Ultra GPS performed better than the S2 model and was similar to the Qstarz in all tested trip-related measures, and it was able to estimate both passive and active trip modes. Both models showed similar results to the Qstarz in life-space-related measures. The TicWatch S2 demonstrated good to excellent overall agreement with the ActiGraph algorithms for the time spent in sedentary and non-sedentary activities, with 84% and 87% agreement rates, respectively. Under supervised conditions, the TicWatch Pro 3 Ultra GPS measured step count consistently with the gold standard observer, with a bias of 0.4 steps. The thigh-worn ActiGraph algorithm accurately classified sitting and lying postures (97%) and standing postures (90%).</p> <p><strong>Conclusion</strong></p> <p>Our multi-sensor approach to monitoring mobility has the potential to capture both accelerometer-derived movement data and trip/life-space data only available through GPS. In this study, we found that the TicWatch models are valid devices for capturing GPS and raw accelerometer data, making them useful tools for assessing real-world mobility in older adults and advancing our knowledge of early mobility decline.</p>
WatchBLoc: A smartwatch IMU and ambient BLE dataset for room-level localisation
<p><strong>WatchBLoc dataset</strong></p> <p>This dataset consists of BLE RSSIs (emitted from identical BLE beacons) and IMU recordings (3-axial acceleration, 3-axial gyroscope, 3-axial magnetometer) recorded by a Sony Smartwatch 3. All participants wore the smartwatch in their right hand, which was the dominant hand in all participants.</p> <p>The data were recorded across two environments: a real-home and a demo-home. The demo-home consists of 6 rooms: big office, small office, kitchen, bathroom, meeting room, lab room. The real-home consists of 6 rooms: kitchen, living room, bedroom, bathroom, office, and loo. However, the living room and kitchen lie in the same open-plan space, and the loo lies within the office (i.e., it is an ensuite space). These can thus be accounted as:</p> <ul> <li>the aforementioned 6 rooms</li> <li>5 rooms, namely: open-plan kitchen/living room, bedroom, bathroom, office, loo</li> <li>5 rooms, namely: kitchen, living room, bedroom, bathroom, ensuite</li> <li>4 rooms, namely: open-plan kitchen/living room, bedroom, bathroom, ensuite.</li> </ul> <p>BLE beacons were installed in each room of the above environments. A total of three BLE configurations were considered for each room; one beacon was placed in the centre of each room (denoted as "centre" in the dataset), one by the entrance of each room (denoted "doors") and one at a location in each room chosen such that the pairwise distances between the beacons from all rooms are maximised (denoted as "far").</p> <p>The smartwatch was recording IMU at 100 Hz and BLE RSSIs at 0.2 Hz and the ground truth location which the participants had to report, by tapping the appropriate room label on the watch's screen every time they were entering a new room.</p> <p>Each participant performed the experiment for approximately 1 hour continuously; with the sensor recording application active and the user instructed on how to record the ground truth location, the participants moved around the environment, performing activities that are commonly encountered in each room in their own style. Not everyone performed the exact same activities, and the ground truth activity labels were not recorded.</p> <p>A total of 11 participants, noted as user1 to user11 performed the experiment across the two environments, yielding a total of 20 recordings, noted as rec1 to rec20 in the dataset. user1 performed the experiment in both environments; three times in the demo-home (rec1,5,8) and once in the real-home (rec13,15,17,19). user11 performed the experiment only in the real-home (rec14,16,18,20) while the rest of the users performed the experiment only in the demo home, yielding one recording each.</p> <p>Note that recording rec13,15,17,19 were recorded simultaneously, but account for the four different assumptions on what constitutes a room in the "real home", as described before. The same holds for recordings rec14,16,18,20.</p> <p>The data are organised per recording and user id. Note that some users have more than one corresponding recording.</p>
Smartphone and smartwatch inertial measurements from heterogeneous subjects for human activity recognition.
<p>This repository contains the dataset and contents described in the <i><strong>"Dataset of inertial measurements of smartphones and smartwatches for human activity recognition"</strong></i> data article.</p><blockquote><p>Matey-Sanz, M., Casteleyn, S., & Granell, C. (2023). Dataset of inertial measurements of smartphones and smartwatches for human activity recognition. <i>Data in Brief</i>, 109809.</p></blockquote>
Towards Detecting Cocaine Use Using Smartwatches in the NIDA Clinical Trials Network
ClinicalTrials.gov study NCT02915341. IPD Sharing: YES. Countries: 1. Publications: 2.
Pulsewatch: Smartwatch Monitoring for Atrial Fibrillation After Stroke
ClinicalTrials.gov study NCT03761394. IPD Sharing: NO. Countries: 1. Publications: 2.
Monitoring mobility in older adults using a global positioning system (GPS) smartwatch and accelerometer: A validation study
Open the record for dataset details and reuse information.
watchHAR: A Smartwatch IMU dataset for Activities of Daily Living
<pre># watchHAR: A Smartwatch IMU dataset for Activities of Daily Living This is a dataset of IMU recordings (3-axial acceleration, 3-axial gyroscope, and 3-axial magnetometer) recorded with Sony Smartwatch 3, along with ground truth location data from a vicon motion capture system. Smartwatch IMU and vicon location data exist for the users' left and right hands, as well as vicon location data from their left and right ankles. The recoded data cover the following `activity_id`s: * `brushing_teeth` * `idle` * `preparing_sandwich` * `reading_book` * `typing` * `using_phone` * `using_remote_control` * `walking_freely` * `walking_holding_a_tray` * `walking_with_handbag` * `walking_with_hands_in_pockets` * `walking_with_object_underarm` * `washing_face_and_hands` * `washing_mug` * `washing_plate` * `writing`, all of which were recorded in the same room, across the span of several days. In addition to these activities, walking stairs up and down (`activity_id`s `stairs_up` and `stairs_down`) events were recorded in various different locations. Note that for these stairs events only Smartwatch IMU recordings exist, and only from the dominant hand of the participants, which coincided with the right hand in all cases. The data are organised in the following folder structure: `user_id/activity_id/recording_type.csv`. Stair events are a special case in that multiple up/down stair recordings can exist for a single user. These are separated like the following example ``` `user_01/stairs_down-02/smartwatch_right_hand.csv` ``` that points to the IMU data of the second `stairs_down` recording of the first user. Finally, note that all timestamps refer to a UTC timezone, and that data collection took place in the United Kingdom. </pre>
Non-invasive Blood Pressure Measurement Using Samsung Smartwatch
ClinicalTrials.gov study NCT05302193. IPD Sharing: NO. Countries: 1. Publications: 1.
Diagnosis of Atrial Fibrillation in Postoperative Thoracic Surgery Using a Smartwatch
ClinicalTrials.gov study NCT06724718. IPD Sharing: NO. Countries: 1. Publications: 1.
Using Smartwatches to Monitor Smoking in Real-life Situations
ClinicalTrials.gov study NCT07067151. IPD Sharing: NO. Countries: 1. Publications: 37.
Accuracy of Smartwatches in Measuring Oxygen Levels in Patients With Pulmonary Hypertension: A Pilot Study
ClinicalTrials.gov study NCT07311135. IPD Sharing: NO. Countries: 1. Publications: 3.
Monitoring of Multiple Sclerosis (MS) Participants With the Use of Digital Technology (Smartphones and Smartwatches) - A Feasibility Study
ClinicalTrials.gov study NCT02952911. IPD Sharing: Not stated. Countries: 2. Publications: 3.
QT-Logs : Artificial Intelligence for QT Interval Analysis of ECG From Smartwatches in Patient Receiving Treatment for Covid-19
ClinicalTrials.gov study NCT04371744. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Comparison of SpO2 Measurement Accuracy of Different Types of Smartwatches
ClinicalTrials.gov study NCT05789563. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Smartwatch and Physician Well-Being
ClinicalTrials.gov study NCT05463250. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.