Find research datasets worth reusing

opencc-by-4.0Dec 2019View details →

Continuous multi-sensor wearable data and daily subject-reported fatigue of heathy adults

Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are limited. We collected a total of 28 healthy adult subjects and 973 recording days. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters at 1-minute temporal resolution, and daily questionnaires (patient-reported outcome scores, PROs) on fatigue. When matching both sensor data and PROs, the datasets contains data from 27 subjects and 405 recording days. Analysis of these multimodal digital data to inform, quantify, and augment subjectively captured non-pathological fatigue measures were published at Luo H., et. al. (2020), Assessment of Fatigue Using Wearable Sensors: A Pilot Study. Digit Biomark. Demographics, sensor parameters and other information on this dataset can be found in the aforementioned manuscript and related supplementary material. Files included are <ul> <li>fatiguePROs.csv: daily PROs for all subjects</li> <li>subjectID_*.csv: sensor time series for each subject</li> </ul>

opencc-by-4.0Jan 2021View details →

Impact of delayed response on Wearable Cognitive Assistance

This dataset contains the data associated with our research project titled Impact of delayed response on Wearable Cognitive Assistance. A preprint of the associated paper can be found at https://arxiv.org/abs/2011.02555. See the README.txt file for dataset details.

opencc-by-4.0Apr 2024View details →

Two Wearable Sensor Datasets recording the Countermovement Jump

These datasets come from two independent studies using wearable inertial sensors to estimate countermovement jump performance. The participants were healthy sports science students, free of injury, all of whom had given their prior written consent. Ethical approval was given by the governing institutions’ ethics committees, which included further analysis of the data. <ul> <li>Smartphone Dataset: <ul> <li>119 valid jumps</li> <li>Peak power 40.7 +/- 8.9 W/kg</li> <li>22 males, 10 females (26.5 +/- 4.1 yrs; standing height 1.74 +/- 0.08 m; body mass 70.0 +/- 10.9 kg)</li> <li>Redmi 9T phone (Xiaomi Technology, Beijing, China)</li> <li>128 Hz sampling frequency</li> <li>Accelerometer & gyroscope</li> <li>Handheld at sternum level</li> <li>Mascia, G.; De Lazzari, B.; Camomilla, V. Machine learning aided jump height estimate democratization through smartphone measures. Frontiers in Sports and Active Living 2023, 5, 1112739. <a href="https://doi.org/10.3389/fspor.2023.1112739">https://doi.org/10.3389/fspor.2023.1112739</a>.</li> </ul> </li> <li>Accelerometer Dataset: <ul> <li>347 valid jumps</li> <li>Peak power 45.1 +/- 7.6 W/kg</li> <li>48 males, 25 females (21.6 +/- 3.3 yrs; standing height 1.75 +/- 0.10 m; body mass 71.2 +/- 15.1 kg)</li> <li>Trigno sensor (Delsys Inc, MA, USA)</li> <li>250 Hz sampling frequency</li> <li>Accelerometer</li> <li>Taped to lower back (L4)</li> <li>White, M.G.E.; Bezodis, N.E.; Neville, J.; Summers, H.; Rees, P. Determining jumping performance from a single body-worn accelerometer using machine learning. PLOS ONE 2022, 17, e0263846. <a href="https://doi.org/10.1371/journal.pone.0263846">https://doi.org/10.1371/journal.pone.0263846</a></li> </ul> </li> </ul> MATLAB .mat files This repository was used by the paper currently under review for the open journal Mathematics: White, M.; De Lazzari, B.; Bezodis, N., Camomilla, V. Title. Mathematics 2024, 1, 0. Wearable Sensors for Athletic Performance: A Comparison of Discrete and Continuous Feature Extraction Methods for Prediction Models

opencc-by-4.0Dec 2021View details →

AnDy suit: human weight lifting wearable data

This dataset comprises wearable data, collected using <a href="https://andy-project.eu/results/andysuit">An.Dy. suit</a>, from two weight lifting experiments of a human subject. Wearable data include kinematic measurements acquired with the <a href="https://www.xsens.com/">Xsens Motion Tracking system</a> (composed by 17 IMUs) and <a href="https://ifeeltech.eu/">iFeel shoes</a> (force/torque sensorized shoes developed by Istituto Italiano di Tecnologia). The experimental design is the following: Experiment 01 Lifting task Geometry, accordingly to NIOSH convention: <ul> <li>H = 63 cm</li> <li>V = 30 cm</li> <li>D = 40 cm</li> <li>CM = 0.9</li> <li>Load = 7 kg</li> </ul> The task is executed 10 times. Experiment 02 Lifting task Geometry, accordingly to NIOSH convention: <ul> <li>H = 31 cm</li> <li>V = 66 cm</li> <li>D = 42 cm</li> <li>CM = 1</li> <li>Load = 5 kg</li> </ul> The task has been executed: <ul> <li>5 minutes: Lifting with back only</li> <li>5 minutes: Lifting with back plus leg</li> </ul> Data Structure Data structure is the following: - experiment0x   - wearable_data     - FTshoes     - xsens - subject_model   Data Interpretation Data have been collected using <a href="https://www.yarp.it//v3.5/yarpdatadumper.html">YARP datadumper tool</a> using the thrift message implemented in <a href="https://github.com/robotology/wearables">wearables library</a>. Data Usage Data can be used by <a href="https://github.com/robotology/human-dynamics-estimation">human-dynamics-estimation</a> devices for replicating the results presented in: <ul> <li>Rapetti, L.; Tirupachuri, Y.; Darvish, K.; Dafarra, S.; Nava, G.; Latella, C.; Pucci, D. Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics. Algorithms 2020, 13, 266. https://doi.org/10.3390/a13100266</li> <li>Latella, C.; Traversaro, S.; Ferigo, D.; Tirupachuri, Y.; Rapetti, L.; Andrade Chavez, F.J.; Nori, F.; Pucci, D. Simultaneous Floating-Base Estimation of Human Kinematics and Joint Torques. Sensors 2019, 19, 2794. https://doi.org/10.3390/s19122794</li> <li>Tirupachuri, Y. ; Ramadoss, P. ; Rapetti, L. ; Latella, C. ; Darvish, K. ; Traversaro, S. ; Pucci D. Online Non- Collocated Estimation of Payload and Articular Stress for Real-Time Human Ergonomy Assessment. IEEE Access, pp. 1–1, Aug. 2021, https://ieeexplore.ieee.org/document/9526592.</li> </ul>

opencc-by-4.0Jul 2023View details →

HRV-ACC: a dataset with R-R intervals and accelerometer data for the diagnosis of psychotic disorders using a Polar H10 wearable sensor

ABSTRACT The issue of diagnosing psychotic diseases, including schizophrenia and bipolar disorder, in particular, the objectification of symptom severity assessment, is still a problem requiring the attention of researchers. Two measures that can be helpful in patient diagnosis are heart rate variability calculated based on electrocardiographic signal and accelerometer mobility data. The following dataset contains data from 30 psychiatric ward patients having schizophrenia or bipolar disorder and 30 healthy persons. The duration of the measurements for individuals was usually between 1.5 and 2 hours. R-R intervals necessary for heart rate variability calculation were collected simultaneously with accelerometer data using a wearable Polar H10 device. The Positive and Negative Syndrome Scale (PANSS) test was performed for each patient participating in the experiment, and its results were attached to the dataset. Furthermore, the code for loading and preprocessing data, as well as for statistical analysis, was included on the corresponding GitHub repository. BACKGROUND Heart rate variability (HRV), calculated based on electrocardiographic (ECG) recordings of R-R intervals stemming from the heart's electrical activity, may be used as a biomarker of mental illnesses, including schizophrenia and bipolar disorder (BD) [Benjamin et al]. The variations of R-R interval values correspond to the heart's autonomic regulation changes [Berntson et al, Stogios et al]. Moreover, the HRV measure reflects the activity of the sympathetic and parasympathetic parts of the autonomous nervous system (ANS) [Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, Matusik et al]. Patients with psychotic mental disorders show a tendency for a change in the centrally regulated ANS balance in the direction of less dynamic changes in the ANS activity in response to different environmental conditions [Stogios et al]. Larger sympathetic activity relative to the parasympathetic one leads to lower HRV, while, on the other hand, higher parasympathetic activity translates to higher HRV. This loss of dynamic response may be an indicator of mental health. Additional benefits may come from measuring the daily activity of patients using accelerometry. This may be used to register periods of physical activity and inactivity or withdrawal for further correlation with HRV values recorded at the same time. EXPERIMENTS In our experiment, the participants were 30 psychiatric ward patients with schizophrenia or BD and 30 healthy people. All measurements were performed using a Polar H10 wearable device. The sensor collects ECG recordings and accelerometer data and, additionally, prepares a detection of R wave peaks. Participants of the experiment had to wear the sensor for a given time. Basically, it was between 1.5 and 2 hours, but the shortest recording was 70 minutes. During this time, evaluated persons could perform any activity a few minutes after starting the measurement. Participants were encouraged to undertake physical activity and, more specifically, to take a walk. Due to patients being in the medical ward, they received instruction to take a walk in the corridors at the beginning of the experiment. They were to repeat the walk 30 minutes and 1 hour after the first walk. The subsequent walks were to be slightly longer (about 3, 5 and 7 minutes, respectively). We did not remind or supervise the command during the experiment, both in the treatment and the control group. Seven persons from the control group did not receive this order and their measurements correspond to freely selected activities with rest periods but at least three of them performed physical activities during this time. Nevertheless, at the start of the experiment, all participants were requested to rest in a sitting position for 5 minutes. Moreover, for each patient, the disease severity was assessed using the PANSS test and its scores are attached to the dataset. The data from sensors were collected using Polar Sensor Logger application [Happonen]. Such extracted measurements were then preprocessed and analyzed using the code prepared by the authors of the experiment. It is publicly available on the GitHub repository [Książek et al]. Firstly, we performed a manual artifact detection to remove abnormal heartbeats due to non-sinus beats and technical issues of the device (e.g. temporary disconnections and inappropriate electrode readings). We also performed anomaly detection using Daubechies wavelet transform. Nevertheless, the dataset includes raw data, while a full code necessary to reproduce our anomaly detection approach is available in the repository. Optionally, it is also possible to perform cubic spline data interpolation. After that step, rolling windows of a particular size and time intervals between them are created. Then, a statistical analysis is prepared, e.g. mean HRV calculation using the RMSSD (Root Mean Square of Successive Differences) approach, measuring a relationship between mean HRV and PANSS scores, mobility coefficient calculation based on accelerometer data and verification of dependencies between HRV and mobility scores. DATA DESCRIPTION The structure of the dataset is as follows. One folder, called HRV_anonymized_data contains values of R-R intervals together with timestamps for each experiment participant. The data was properly anonymized, i.e. the day of the measurement was removed to prevent person identification. Files concerned with patients have the name treatment_X.csv, where X is the number of the person, while files related to the healthy controls are named control_Y.csv, where Y is the identification number of the person. Furthermore, for visualization purposes, an image of the raw RR intervals for each participant is presented. Its name is raw_RR_{control,treatment}_N.png, where N is the number of the person from the control/treatment group. The collected data are raw, i.e. before the anomaly removal. The code enabling reproducing the anomaly detection stage and removing suspicious heartbeats is publicly available in the repository [Książek et al]. The structure of consecutive files collecting R-R intervals is following: <table> <tbody> <tr> <td>Phone timestamp</td> <td>RR-interval [ms]</td> </tr> <tr> <td>12:43:26.538000</td> <td>651</td> </tr> <tr> <td>12:43:27.189000</td> <td>632</td> </tr> <tr> <td>12:43:27.821000</td> <td>618</td> </tr> <tr> <td>12:43:28.439000</td> <td>621</td> </tr> <tr> <td>12:43:29.060000</td> <td>661</td> </tr> <tr> <td>...</td> <td>...</td> </tr> </tbody> </table> The first column contains the timestamp for which the distance between two consecutive R peaks was registered. The corresponding R-R interval is presented in the second column of the file and is expressed in milliseconds.   The second folder, called accelerometer_anonymized_data contains values of accelerometer data collected at the same time as R-R intervals. The naming convention is similar to that of the R-R interval data: treatment_X.csv and control_X.csv represent the data coming from the persons from the treatment and control group, respectively, while X is the identification number of the selected participant. The numbers are exactly the same as for R-R intervals. The structure of the files with accelerometer recordings is as follows: <table> <tbody> <tr> <td>Phone timestamp</td> <td>X [mg]</td> <td>Y [mg]</td> <td>Z [mg]</td> </tr> <tr> <td>13:00:17.196000</td> <td>-961</td> <td>-23</td> <td>182</td> </tr> <tr> <td>13:00:17.205000</td> <td>-965</td> <td>-21</td> <td>181</td> </tr> <tr> <td>13:00:17.215000</td> <td>-966</td> <td>-22</td> <td>187</td> </tr> <tr> <td>13:00:17.225000</td> <td>-967</td> <td>-26</td> <td>193</td> </tr> <tr> <td>13:00:17.235000</td> <td>-965</td> <td>-27</td> <td>191</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> The first column contains a timestamp, while the next three columns correspond to the currently registered acceleration in three axes: X, Y and Z, in milli-g unit. We also attached a file with the PANSS test scores (PANSS.csv) for all patients participating in the measurement. The structure of this file is as follows: <table> <tbody> <tr> <td>no_of_person</td> <td>PANSS_P</td> <td>PANSS_N</td> <td>PANSS_G</td> <td>PANSS_total</td> </tr> <tr> <td>1</td> <td>8</td> <td>13</td> <td>22</td> <td>43</td> </tr> <tr> <td>2</td> <td>11</td> <td>7</td> <td>18</td> <td>36</td> </tr> <tr> <td>3</td> <td>14</td> <td>30</td> <td>44</td> <td>88</td> </tr> <tr> <td>4</td> <td>18</td> <td>13</td> <td>27</td> <td>58</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>..</td> </tr> </tbody> </table> The first column contains the identification number of the patient, while the three following columns refer to the PANSS scores related to positive, negative and general symptoms, respectively. USAGE NOTES All the files necessary to run the HRV and/or accelerometer data analysis are available on the GitHub repository [Książek et al]. HRV data loading, preprocessing (i.e. anomaly detection and removal), as well as the calculation of mean HRV values in terms of the RMSSD, is performed in the main.py file. Also, Pearson's correlation coefficients between HRV values and PANSS scores and the statistical tests (Levene's and Mann-Whitney U tests) comparing the treatment and control groups are computed. By default, a sensitivity analysis is made, i.e. running the full pipeline for different settings of the window size for which the HRV is calculated and various time intervals between consecutive windows. Preparing the heatmaps of correlation coefficients and corresponding p-values can be done by running the utils_advanced_plots.py file after performing the sensitivity analysis. Furthermore, a detailed analysis for the one selected set of hyperparameters may be prepared (by setting sensitivity_analysis = False), i.e. for 15-minute window sizes, 1-minute time intervals between consecutive windows and without data interpolation method. Also, patients taking quetiapine may be excluded from further calculations by setting exclude_quetiapine = True because this medicine can have a strong impact on HRV [Hattori et al]. The accelerometer data processing may be performed using the utils_accelerometer.py file. In this case, accelerometer recordings are downsampled to ensure the same timestamps as for R-R intervals and, for each participant, the mobility coefficient is calculated. Then, a correlation coefficient between mean HRV values and mobility coefficient is computed. The plotting of the pure accelerometer signal may be done by running the utils_loading.py file. The comparison of age distribution between the tested groups can be made by the histogram plotted with the use of the utils_basic_plots.py file.

opencc-by-4.0Apr 2024View details →

GEDII Wearable Sensors Dataset of 10 Research Teams

The dataset contains Bluetooth (proximity), Infrared (face-to-face), Speech (microphone) and Accelerometer (body activity) data of 10 research teams collected during 5 working days in each team. Altogether N=105 team members. Socio-demographic data as well as round-robin ratings regarding friendship and advice seeking is included. Data was collected using Sociometric badges by Humanyze (formerly Sociometric Solutions). The present dataset has been produced within the context of a EU funded H2020 research project called “Gender-Diversity-Impact: Improving Research and Innovation through Gender Diversity. (GEDII)”. The project has been running from 2015 to 2018 with the aim to develop new tools and methods for doing research on the impact of gender diversity in R&D teams. In order to address these questions, GEDII makes use of a variety of research methods, including a cross country survey, bibliometric & patent analysis and detailed case studies with R&D teams. The dataset is distributed as R package.

opencc-by-4.0May 2024View details →

SWIFTIES (Subject and Wearables data for Investigation of Fatiguing Tasks In Extension/flexion of Shoulder/elbow)

32 subjects (17 males, 15 females) performed 8 shoulder and elbow flexion-extension tasks with the dominant arm until fatigue. Tasks were performed at 25% and 45% of the maximum voluntary contraction (MVC) force in a static and dynamic manner. Every combination of movement (static or dynamic) x joint (shoulder or elbow) x load (25% or 45% of MVC Force) was performed by every subject. Subject data includes information on demographic and anthropometrics, rate of perceived exertion (RPE), endurance time of tasks, MVC forces, and loads lifted. Sensor data includes inertial measurement unit (IMU), electromyography (EMG), motion capture (MC) and pressure insole (PI) data.

opencc-by-4.0Feb 2022View details →

A database of physical therapy exercises with variability of execution collected by wearable sensors

The PHYTMO database contains data from physical therapy exercises and gait variations recorded with magneto-inertial sensors, including information from an optical reference system. PHYTMO includes the recording of 30 volunteers, aged between 20 and 70 years old. A total amount of 6 exercises and 3 gait variations commonly prescribed in physical therapies were recorded. The volunteers performed two series with a minimum of 8 repetitions in each one. Four magneto-inertial sensors were placed on the lower-or upper-limbs for the recording of the motions together with passive optical reflectors. The files include the specifications of the inertial sensors and the cameras. The database includes magneto-inertial data (linear acceleration, turn rate and magnetic field), together with a highly accurate location and orientation in the 3D space provided by the optical system (errors are lower than 1mm). The database files were stored in CSV format to ensure usability with common data processing software. The main aim of this dataset is the availability of inertial data for two main purposes: the analysis of different techniques for the identification and evaluation of exercises monitored with inertial wearable sensors and the validation of inertial sensor-based algorithms for human motion monitoring that obtains segments orientation in the 3D space. Furthermore, the database stores enough data to train and evaluate Machine Learning-based algorithms. The age range of the participants can be useful for establishing age-based metrics for the exercises evaluation or the study of differences in motions between different aged groups. Finally, the MATLAB function features_extraction, developed by the authors, is also given. This function splits signals using a sliding window, returning its segments, and extract signal features, in the time and frequency domains, based on prior studies of the literature.

opencc-by-4.0Jul 2022View details →

Supplementary Materials for "What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit"

Supplementary Materials for "What Your Wearable Devices Revealed About You and Possibilities of Non-Cooperative 802.11 Presence Detection During Your Last IPIN Visit" This package contains an anonymized packet of 802.11 probe requests captured in Lloret de Mar during the Indoor Positioning and Indoor Navigation 2021 conference. The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).

opencc-by-4.0May 2024View details →

Time Series data from wearable sensors to capture the onset of Fatigue in Runners

The data captured came from mounting a single Shimmer3 IMU on the lumbar of 19 recreational runners. The participants were all regular runners and injury free. The study protocol was reviewed and approved by the human research ethics committee at University College Dublin. The data was collected in three segments; in the first, the participant completed a 400m run at a comfortable pace; the second segment consisted of a beep test which acted as the fatiguing protocol for this study; and the last segment where the runner was required to complete the 400m run at their comfortable pace, this time in their fatigued state. The beep test requires the runner to continuously run between two points 20m apart following an audio which produces `beeps' indicating when the person should begin running from one end to the other. The test eventually requires the runner to increase their pace as the interval between the `beeps' reduces as the test progresses. The fatiguing protocol ends when the runner is unable to keep up the increase in pace. The runs were all done on an outdoor running track. The sensor captured acceleration, angular velocity and magnetometer data throughout the three stages of the trials at a sampling rate of 256Hz. The data included here consists of the raw readings from the sensors across the three phases of the run. The data is saved seperately as 'F' for Fatigued, 'NF' for Not Fatigued, and 'BeepTest' for the data collected during the fatiguing process. For the processed and labelled fatigue and non fatigue data, see: https://zenodo.org/records/7997851 Kindly cite one of the following papers when using this data: B. Kathirgamanathan, B. Caulfield and P. Cunningham, "Towards Globalised Models for Exercise Classification using Inertial Measurement Units," 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023, pp. 1–4, doi: 10.1109/BSN58485.2023.10331612 B. Kathirgamanathan, T. Nguyen, G. Ifrim, B. Caulfield, P. Cunningham. Explaining Fatigue in Runners using Time Series Analysis on Wearable Sensor Data, XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining, ECML PKDD, 2023, <a href="http://xkdd2023.isti.cnr.it/papers/223.pdf">http://xkdd2023.isti.cnr.it/papers/223.pdf</a>

opencc-by-4.0Oct 2023View details →

WSD4FEDSRM (Wearable sensor data for fatigue estimation during shoulder rotation movements)

The dataset comprises a collection of many data types during shoulder internal rotation, and external rotation exercises from 34 participants, including demographic information, anthropometric measurements, maximum voluntary isometric contraction force measurements, inertial measuring unit data, surface electromyography recordings, photoplethysmogram data from wearable sensors, as well as measurements from the Borg rating of perceived exertion scale and the Karolinska sleepiness scale.

opencc-by-4.0Jan 2020View details →

Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))

Data for paper titled : Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification (published in Sensors (MDPI))

opencc-by-4.0Nov 2023View details →

OCDetect - A Real-World Dataset to Detect Handwashing in Daily-Life using Wrist Motion Data from Wearables

Handwashing detection is a relevant research topic with applications in healthcare and professional environments. While usually related to hygiene improvement, handwashing detection could also be used to support individuals with obsessive-compulsive disorder (OCD). For these individuals, compulsive, long, and frequent handwashing has a negative impact. An automated system could spot compulsive handwashing in real-time and augment the therapy process. No activity recognition datasets containing in-the-wild-recorded compulsive handwashing are available. With this work, we present the OCDetect Dataset, the first dataset with unscripted, compulsive handwashing. It contains recordings from inertial measurement units (IMUs) of 22 participants over 28 days, with ~3000 recorded hand washes. For each hand wash, we supply its user-annotated kind (compulsive / routine). We provide an overview of related datasets and describe the recording, cleaning, labeling, and final features of our dataset. We reach a maximum F1 score of 0.77 (avg.: 0.33, chance level: 0.03) when spotting handwashing from all background activities on unseen participants. Our dataset and code for the reproduction of our results are publicly available.

opencc-by-4.0Jan 2024View details →

Dataset for Monitoring and Visualizing Stroke Rehabilitation Progress using Wearable Sensors (IMU)

<div> This dataset is associated with a manuscript that is currently under peer review.   Article Abstract: Stroke is one of the leading causes of death and disability worldwide, and recovering mobility is an important goal during post-stroke rehabilitation. In this work, we present a study to verify the feasibility of monitoring and visualizing longitudinal stroke gait rehabilitation progress using wearable sensors. Wearable devices such as inertial measurement units (IMUs) are easy-to-use and cost-effective tools for quantifying mobility. However, there is a need for research on longitudinal monitoring of stroke rehabilitation progress with wearables, as well as generating clinically relevant insights using appropriate visualizations. To this aim, we recruited ten stroke patients in their early rehabilitation stage. We collected and analyzed the IMU-derived gait features across two visits, and presented visualizations of the foot movement trajectories as well as the spatio-temporal gait parameters in the average, symmetry, and variation domains to quantify changes in gait. Our visualization and quantification methods are evaluated and validated by clinical experts, and prove to be promising in aiding clinicians to monitor rehabilitation progression.   Data description: The dataset consists data from ten stroke patients who completed both visits. The "raw" data folder contains tri-axial acceleration and angular velocity data from the IMUs. In addition, information about the participants such as demographics (e.g., body height and body weight), FAC scores at both visits, and evaluations of gait improvement are documented in the file "participant_info.csv". The “interim” folder contains IMU data that has been manually segmented to remove irrelevant movements before and after each walking session during a visit, based on visual inspection of raw IMU signals. For quality control, the segmented accelerometer and gyroscope data of each sensor were plotted, and the plots were saved in the same folder as the IMU signals. In addition, during the first execution of gait parameter extraction, calculated 3D feet trajectories were cached in the "interim" folder, so that for future executions, the cached trajectories can be loaded directly, reducing the computational efforts for re-calculation. The file "stance_magnitude_thresholds_manual.csv" documents the angular velocity thresholds used to identify stance phases for the gait analysis algorithm for each participant. The threshold values were determined manually by observing the angular velocity signals.  The “processed” folder contains stride-by-stride spatio-temporal gait parameters extracted for each of the four walking conditions, and aggregated gait parameters in terms of coefficients of variation and symmetry for all walking conditions for each participant.    </div>

openmit-licenseJun 2023View details →

Benchmark for energy efficient obstacle detection on head mounted wearable for the vision impaired

Here we present a novel benchmark dataset with the associated challenge, that is to detect obstacles based on head-mounted sensors and lightweight wearable devices to assist Blind and Visually Impaired individuals (BVIs) navigate in indoor environments.  The challenge encompasses three objectives: (1) as accurately as possible to detect the obstacles on the pathway that likely lead to a collision; (2) as durably as possible on a given amount of battery power for the detection algorithm or model to run; (3) as reliably as possible to compensate natural head turns so nearby objects would not trigger false alarms.  The data provided in the benchmark are collected from the following head mounted sensors: (i) nine low-cost ultrasonic sensors; (ii) one high-end ultrasonic sensor with a larger detection range but higher power consumption; (iii) a 9-Degrees of Freedom (DOF) Inertial Measurement Unit (IMU).  The resulting dataset consists of more than 188,000 unique sequences obtained from multiple subjects walking in three different indoor scenarios.  This benchmark is to facilitate and encourage accurate yet fast obstacle detection solutions that can really benefit BVIs.

opencc-zeroJun 2024View details →

Dataset: Wearable Devices Ltd. (WLDSW) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

Dataset: Wearable Devices Ltd. (WLDS) Stock Performance

opencc-by-4.0Jun 2019View details →

SwarmTouch: Guiding a Swarm of Micro-Quadrotors with Impedance Control using a Wearable Tactile Interface

The dataset "Flight_Experiments_Data.zip" represents flight experiments information of the SwarmTouch project. During the experiment, involved participants were given a task to navigate a formation of drones through the obstacles maze relying only on tactile or visual feedback. For the experiment, users overcame two different unknown setups of obstacles, each setup firstly with tactile and then with vision feedback (two trials with tactile and two with visual (without glove) feedback in total). The dataset "Patterns_Recognition_Data.zip " represents tactile patterns recognition experimental results. The tactile patterns were developed to represent the static and dynamic parameters of the drone swarm. This information is sent further to a human operator guiding a formation of drones with the help of tactile glove. During the experiment, each pattern was repeated once, and the subject was asked to enter the number of experienced stimuli. Each of the subjects experienced 64 stimuli (8 patterns were repeated 8 times in random order). The time of user response was also recorded.