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19 results for “smartphone sensors”
Smartphone sensor data (accelerometer, virtual keyboard) collected in-the-wild by Parkinson's Disease patients and Healthy Controls
<p>For detailed description of the dataset see the relevant <a href="https://www.nature.com/articles/s41598-020-78418-8">journal article</a>.</p> <p>Python code for model inference and training is available <a href="https://github.com/alpapado/deep_pd">here</a>.</p> <p> </p> <p><strong>DESCRIPTION</strong></p> <p>The dataset contains accelerometer recodings and keyboard typing data contributed by Parkinson's Disease patients and Healthy Controls. Accelerometer data consists of acceleration values recorded during phone calls and typing data consist of virtual keyboard press and release timestamps. The dataset is divided into two parts: the first part, called SData, contains data from a small, medically evaluated, set of users, while the second part, called GData, contains recordings from a large body of users with self-reported PD labels.</p> <p>The dataset is organized into 5 pickle files:</p> <p>1. <strong>imu_sdata.pickle</strong>: Contains the tri-axial accelerometer recordings for the SData part of the dataset in the form of a list of python dictionaries, one for each participating subject. Accelerometer data have been pre-processed to a sampling frequency of 100Hz and come segmented into non-overlapping 5 second windows. Hence, a segment's dimension will be 500 x 3 samples.</p> <p>Sample Python code for accessing the acceleration data of a subject</p> <pre><code class="language-python">sdata = pickle.load(open('imu_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] # subject_data is a list of length 4 ## The actual data is in the last element of the list acc_segments = subject_data[-1] num_acc_sessions_for_subject = len(acc_segments) acc_segments_for_first_session = acc_segments[0] acc_segments_for_second_session = acc_segments[1] # ..etc In: print(acc_segments_for_first_session.shape) Out: (3, 500, 3) ## The first accelerometer session for this subject consists of 3 five-second segments. In: print(acc_segments_for_second_session.shape) Out: (8, 500, 3) ## The second accelerometer session for this subject consists of 8 five-second segments.</code></pre> <p>2. <strong>imu_gdata.pickle</strong>: Same layout as imu_sdata.pickle but with data ffrom GData subjects.</p> <p>3. <strong>typing_sdata.pickle</strong>: This files contains the typing data originating from the SData part of the dataset. It is a list of dictionaries with one entry per subject. The typing data are given in the form of concatenated hold time (the time elapsed between press and release of the virtual key) and flight time (the time between releasing a key and press the next) histograms, computed over 10ms bins in the range of [0, 1]s for hold time and [0, 4]s for flight time (an additional bin that contains the values in the (1, +oo) and (4, +oo) intervals is also used). So, the total length of the concatenated histogram is 1000/10 + 1 + 4000/10 + 1 = 502.</p> <p>Sample Python code for accessing the typing data of a subject:</p> <pre><code class="language-python">sdata = pickle.load(open('typing_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] ## The actual data is in the first element of the list typing_histograms = subject_data[0] num_typing_sessions_for_subject = len(typing_histograms) typing_hist_for_first_session = typing_histograms[0] typing_hist_for_second_session = typing_histograms[1] # ..etc In: print(typing_hist_for_first_session.shape) Out: (502, ) ht_hist = typing_hist_for_first_session[:101] # Hold time histogram of the session ft_hist = typing_hist_for_first_session[101:] # Flight time histogram of the session</code></pre> <p>4. <strong>typing_gdata.pickle</strong>: Same layout as typing_sdata.pickle but with data from GData subjects.</p> <p>5. <strong>subject_metadata.pickle</strong>: A list of dictionaries with one entry per subject containing demographic information. The relevant demographic fields have the following interpretation:<br> 'age': Year of birth,<br> 'gender_id': 0 indicates male, 1 indicates female<br> 'healthstatus_id': 0 indicates PD patient, 1 indicates Healthy with PD family history, 2 indicates Healthy without PD family history</p> <p>In the case of SData subjects, there is also symptom UPDRS scores from one or two medical examinations. These are ncoded in the fields med_eval_1 and med_eval_2.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><br> </p> <p> </p>
Home-based measurements of dystonia and choreoathetosis in cerebral palsy using smartphone-coupled inertial sensor technology and machine learning: A proof-of-concept study - dataset
<p>Home-based measurements of dystonia in cerebral palsy using smartphone-coupled inertial sensor technology and machine learning: A proof-of-concept study</p> <p> </p> <p>This project contains:</p> <p>- 1 main MATLAB script: MODYSathome_main.m<br> - 12 MATLAB functions:<br> - function_calc_mean_recall_precision.m<br> - function_create_dataframes.m<br> - function_deep_learning.m<br> - function_determine_best_ML_model.m<br> - function_display_DL_results.m<br> - function_display_ML_results.m<br> - function_index_extremities.m<br> - function_machine_learning.m<br> - function_oversample.m<br> - function_partition_data.m<br> - function_pick_best_models.m<br> - function_prepare_DL_data.m</p> <p>Downloading the Matlab scripts</p> <p> - Create a folder named 'MODYS' and create a subfolder named 'results'<br> - Download the zip file via <a href="https://zenodo.org/record/6379348">RehabAUmc/modys-at-home: v1.0 | Zenodo</a><br> - Unzip the zip file in the path MODYS\</p> <p>STEPS<br> 1. Open MATLAB<br> 2. In MATLAB, go to the 'HOME' tab and click on 'Set Path'<br> 3. Click on 'Add Folder' and browse to MODYS/RehabAUmc-modys-at-home-86b14c3/functions<br> 4. Click on 'Select Folder' and click on 'Save'<br> 5. Click on 'Browse to folder' and browse to a patients' data in MODYS/data/PatientXXX, then click on 'Select Folder'<br> 6. In the 'HOME' tab click on 'Open' and open MODYSathome.m in MODYS/RehabAUmc-modys-at-home-86b14c3<br> 7. In the 'EDITOR' tab click on 'Run Section' to run the script<br> 8. When the code has been run, the results are displayed in the Command Window and saved in MODYS/results/PatientXXX</p>
Laboratory validation of a smartphone-based sensor for diffuse optical volume properties
<p>This data set contains raw image data for laboratory validation of a diffuse-optical, smartphone-based sensor. The measurements were taken using scattering phantoms with known scattering and absorption coefficient. The raw image files have been converted to an uncompressed Adobe-.dng file format, file names indicate whether the file contains data for the three scattering phantoms (One, Two, Three) or spatial calibration data using a 9mm x 9mm calibration pattern (calib). The raw images are located in the folder ./calib. Matlab code is contained in the filder ./matlab. It can be run on Matlab R2021b.</p> <p>For analyzing the raw data set, use "CameraBatchCalib.m". It wraps around the function "CameraAnalysisCalib.m", which performs the image analysis and least-square fit to resorted and rescaled data, employing in turn the model function "theosurfG.m". The resulting data is plotted for comparison with the nominal attenuation length of the scattering phantoms.</p> <p>If you wish to use this data set please contact Markus Allgaier at markusa@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset and code. When using the data set within a publication, please cite:</p> <p>Markus Allgaier & Brian Smith, "A Smartphone-Based Sensor for Measuring the Optical Properties of Snow", in preparation, (2022).</p> <p>The underlying fit function is based on the calculations from:</p> <p>Markus Allgaier and Smith, Diffuse optics for glaciology, Opt. Express 29, 18845–18864 (2021)</p> <p> </p>
Dataset of "Raising Awareness for Inertial Sensors-based Keylogging on Smartphones" scientific research
<p>Dataset for the article</p> <p>Federico Montori, Luca Sciullo, and Luca Bedogni. 2024. Raising Awareness for Inertial Sensors-based Keylogging on Smartphones. In Proceedings of the 2024 International Conference on Information Technology for Social Good (GoodIT '24). Association for Computing Machinery, New York, NY, USA, 14–21. https://doi.org/10.1145/3677525.3678634</p> <p>Please cite the above paper if you are using this dataset.</p>
Using the Socialise app to collect smartphone sensor data for mental health research: A feasibility study
<p>To investigate the feasibility of collecting smartphone sensor data for mental health research, we tested the Socialise app that was developed at the Black Dog Institute in a group of people with a lived experience of mental health challenges (n=32). Bluetooth, GPS and battery status data were collected at regular intervals (3, 4, 5 or 8 minutes) for 4 weeks. In addition, survey data was collected using the app to investigate the views of participants on user experience and the acceptability of passive data collection for mental health research. No mental health data was collected as part of the feasibility study.</p>
Fig. 1 in LiDAR sensors in smartphones can enrich herbarium specimens with 3D models of habitat at high precision and little cost
Fig. 1. Example of a 3D point-cloud model of specimen habitat obtained with the LiDAR scanner of an iPad Pro. A, Plan view of the model with potential use cases, including annotation and extraction of general habitat characteristics; B, Side view with measurements that can be extracted from the model at centimetre precision (DBH, diameter at breast height); C, Average times needed for physical herbarium specimen collection (orange) and LiDAR scanning (purple) in the field over 20 replicates; time for scanning depends on the area scanned and the habitat.
Diffuse-optical data set measured with a smartphone-based sensor on Potato Hill, Oregon, USA
<p>This data set contains both raw data and derived data obtained on Potato Hill, Oregon, on December 17th 2021 using a diffuse-optical, smartphone-based sensor. The raw image files have been converted to an uncompressed Adobe-.dng file format, file names indicate whether the file contains data for the blue (405nm) or red (650nm) laser or spatial calibration data using a 9mm x 9mm calibration pattern. Spectral albedo measurements are contained in the subfilder ./Albedo, the raw images in ./Phone. The root directory contains the matlab code (Matlab R2021b) needed for analysis as well as the derived data.</p> <p>For analyzing the raw data set, use "CameraMatchPotatoHillFinal.m". It wraps around the function "CameraAnalysisFinal.m", which performs the image analysis and least-square fit to resorted and rescaled data, employing in turn the model function "theosurfGInf.m". It saves a derived data set (attenuation, absorption and scattering coefficients, albedos, absorption enhancement factor and snow density.</p> <p>The script "Albedo.m" analyzes the derived data set along with measured albedo and simulated albedo deposited in the file "snicar_120ppb.txt". The obtained albedo curves and black carbon mixing ratio are as shown in the below manuscript.</p> <p>If you wish to use this data set please contact Markus Allgaier at markusa@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset. When using the data set within a publication, please cite:</p> <p>Markus Allgaier & Brian Smith, "A Smartphone-Based Sensor for Measuring the Optical Properties of Snow", in preparation, (2022)</p>
Monitoring Health Care Workers at Risk for COVID-19 Using Wearable Sensors and Smartphone Technology
ClinicalTrials.gov study NCT04756869. IPD Sharing: YES. Countries: 1. Publications: 1.
Período de um pêndulo utilizando o sensor de luminosidade de um Smartphone e o aplicativo Phyphox
<p>Neste Tutorial vamos utilizar o aplicativo de celular phyphox como instrumento para coleta de dados acerca do período de um pêndulo. Os valores obtidos conduzem ao valor da aceleração da gravidade local com bom grau de aproximação e erro estatístico ou precisão na medida da ordem de 3% e Exatidão da ordem de 1,1%. Outras medidas foram realizadas conduzindo a valores sempre desta ordem de grandeza.</p> <p>Propostas de experimentação para aulas remotas. Espero que apreciem esta proposta e que possa contrinuir com suas aulas presenciais ou remotas</p>
Campus Daily Life Behaviors Data based on Smartphone Sensors
Open the record for dataset details and reuse information.
Prediction of Relapse in Schizophrenia/Schizoaffective Disorder With Smartphones and On-body Sensors
ClinicalTrials.gov study NCT02224430. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Smartphone Sensors to Assess Symptom Relief in Patients With Social Anxiety Disorder Undergoing Treatment
ClinicalTrials.gov study NCT06732999. IPD Sharing: NO. Countries: 1. Publications: 1.
Portable smartphone integrated paper sensors for fluorescence detection of As(III) in groundwater
<p>Arsenic contamination in groundwater is a supreme environmental problem, and levels of this toxic metalloid must be strictly monitored by a portable, sensitive and selective analytical device. Herein, a new system of smartphone integrated paper sensors with Cu nanoclusters was established for the effective detection of As(III) in groundwater. For the integration system, the fluorescence emissive peak of Cu nanoclusters at 600 nm decreased gradually with increasing As(III) addition. Meanwhile, the fluorescence color also changed from orange to colorless, and the detection limit was determined as 2.93 nM (0.22 ppb) in a wide detection range. The interfering ions also cannot influence the detection selectivity of As(III). Furthermore, the portable paper sensors based on Cu nanoclusters were fabricated for visual detection of As(III) in groundwater. The quantitative determination of As(III) in natural groundwater has also been accomplished with the aid of a common smartphone. Our work has provided a portable and on-site detection technique toward As(III) in groundwater with high sensitivity and selectivity.</p>
Time variation of accelerometer sensor data from a smartphone placed on the same surface next to the mobilefuge
<p>Time variation of accelerometer sensor data from a smartphone placed on the same surface next to the mobilefuge is recorded to show the vibrations caused by the mobilefuge.</p> <p>This data set has three csv files that are used to create the Figure 9 in the mobilefuge article. </p> <p>Figure.9a_mobilefuge_off.csv</p> <p>Fugure.9b_mobilefuge_on.csv</p> <p>Figure.9c_mobilefuge_with_pad.csv</p> <p>The labels in the first row of each of the file are Time (s), acceleration along the x-direction (m/s^2), acceleration along the y-direction (m/s^2) and acceleration along the z-direction (m/s^2).</p>
At Home Wearable Sensors and Smartphone for Stroke Survivors With Upper Limb Motor Challenges.
ClinicalTrials.gov study NCT06787729. IPD Sharing: NO. Countries: 1. Publications: 0.
Portable smartphone integrated paper sensors for fluorescence detection of As(III) in groundwater
Open the record for dataset details and reuse information.
EgoADL: Multimodal Daily-life Logging in Free-living Environments Using Non-Visual Egocentric Sensors on a Smartphone
<p>EgoADL dataset employs a commodity smartphone as an egocentric sensor hub, which captures the audio, wireless sensing signals (i.e., Wi-Fi), and motion sensor signals continuously. Users can perform arbitrary daily routines with the sensor hub, i.e., an in-pocket smartphone, in free-living environments. EgoADL will recognize ADLs including both human activity and human-object interaction from the sensor data without human intervention.</p>
Using Smartphone Sensor Technology to Characterize Ambulatory Patterns of Participants With Peripheral Artery Disease
ClinicalTrials.gov study NCT04124315. IPD Sharing: NO. Countries: 1. Publications: 0.
V-Sensors for Vitals: Assessment of the Accuracy of a Vital Sign Sensor Mounted on a Smartphone
ClinicalTrials.gov study NCT03321851. IPD Sharing: NO. Countries: 0. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.