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opencc-by-4.0Aug 2019View details →

Wrist-mounted IMU data towards the investigation of free-living human eating behavior - the Free-living Food Intake Cycle (FreeFIC) dataset

Introduction The Free-living Food Intake Cycle (FreeFIC) dataset was created by the <a href="http://mug.ee.auth.gr">Multimedia Understanding Group</a> towards the investigation of in-the-wild eating behavior. This is achieved by recording the subjects’ meals as a small part part of their everyday life, unscripted, activities. The FreeFIC dataset contains the $3D$ acceleration and orientation velocity signals ($6$ DoF) from $22$ in-the-wild sessions provided by $12$ unique subjects. All sessions were recorded using a commercial smartwatch ($6$ using the Huawei Watch 2™ and the MobVoi TicWatch™ for the rest) while the participants performed their everyday activities. In addition, FreeFIC also contains the start and end moments of each meal session as reported by the participants. Description FreeFIC includes $22$ in-the-wild sessions that belong to $12$ unique subjects. Participants were instructed to wear the smartwatch to the hand of their preference well ahead before any meal and continue to wear it throughout the day until the battery is depleted. In addition, we followed a self-report labeling model, meaning that the ground truth is provided from the participant by documenting the start and end moments of their meals to the best of their abilities as well as the hand they wear the smartwatch on. The total duration of the $22$ recordings sums up to $112.71$ hours, with a mean duration of $5.12$ hours. Additional data statistics can be obtained by executing the provided python script stats_dataset.py. Furthermore, the accompanying python script viz_dataset.py will visualize the IMU signals and ground truth intervals for each of the recordings. Information on how to execute the Python scripts can be found below. <pre><code># The script(s) and the pickle file must be located in the same directory. # Tested with Python 3.6.4 # Requirements: Numpy, Pickle and Matplotlib # Calculate and echo dataset statistics $ python stats_dataset.py # Visualize signals and ground truth $ python viz_dataset.py</code></pre> FreeFIC is also tightly related to Food Intake Cycle (FIC), a dataset we created in order to investigate the in-meal eating behavior. More information about FIC can be found <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>. Publications If you plan to use the FreeFIC dataset or any of the resources found in this page, please cite our work: <pre><code>@article{kyritsis2020data, title={A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, journal={IEEE Journal of Biomedical and Health Informatics}, year={2020}, publisher={IEEE}}</code></pre> <pre><code>@inproceedings{kyritsis2017automated, title={Detecting Meals In the Wild Using the Inertial Data of a Typical Smartwatch}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, booktitle={2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, year={2019}, organization={IEEE}} </code></pre> Technical details We provide the FreeFIC dataset as a <a href="https://docs.python.org/3/library/pickle.html">pickle</a>. The file can be loaded using Python in the following way: <pre><code class="language-python">import pickle as pkl import numpy as np with open('./FreeFIC_FreeFIC-heldout.pkl','rb') as fh: dataset = pkl.load(fh)</code></pre> The dataset variable in the snipet above is a dictionary with $5$ keys. Namely: <ul> <li>'subject_id'</li> <li>'session_id'</li> <li>'signals_raw'</li> <li>'signals_proc'</li> <li>'meal_gt'</li> </ul> The contents under a specific key can be obtained by: <pre><code class="language-python">sub = dataset['subject_id'] # for the subject id ses = dataset['session_id'] # for the session id raw = dataset['signals_raw'] # for the raw IMU signals proc = dataset['signals_proc'] # for the processed IMU signals gt = dataset['meal_gt'] # for the meal ground truth </code></pre> The sub, ses, raw, proc and gt variables in the snipet above are lists with a length equal to $22$. Elements across all lists are aligned; e.g., the $3$rd element of the list under the 'session_id' key corresponds to the $3$rd element of the list under the 'signals_proc' key. sub: list Each element of the sub list is a scalar (integer) that corresponds to the unique identifier of the subject that can take the following values: $[1, 2, 3, 4, 13, 14, 15, 16, 17, 18, 19, 20]$. It should be emphasized that the subjects with ids $15, 16, 17, 18, 19$ and $20$ belong to the held-out part of the FreeFIC dataset (more information can be found in the publication titled "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al). Moreover, the subject identifier in FreeFIC is in-line with the subject identifier in the FIC dataset (more info <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>); i.e., FIC’s subject with id equal to $2$  is the same person as FreeFIC’s subject with id equal to $2$. ses: list Each element of this list is a scalar (integer) that corresponds to the unique identifier of the session that can range between $1$ and $5$. It should be noted that not all subjects have the same number of sessions. raw: list Each element of this list is dictionary with the 'acc' and 'gyr' keys. The data under the 'acc' key is a $N_{acc} \times 4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw accelerometer measurements in $g$ (second, third and forth columns - representing the $x, y $ and $z$ axis, respectively). The data under the 'gyr' key is a $N_{gyr} \times 4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw gyroscope measurements in ${degrees}/{second}$(second, third and forth columns - representing the $x, y $ and $z$ axis, respectively). All sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>). Finally, the length of the raw accelerometer and gyroscope numpy.ndarrays is different $(N_{acc} \neq N_{gyr})$. This behavior is predictable and is caused by the Android platform. proc: list Each element of this list is an $M\times7$  numpy.ndarray that contains the timestamps, $3D$ accelerometer and gyroscope measurements for each meal. Specifically, the first column contains the timestamps in seconds, the second, third and forth columns contain the $x,y$ and $z$ accelerometer values in $g$ and the fifth, sixth and seventh columns contain the $x,y$ and $z$ gyroscope values in ${degrees}/{second}$. Unlike elements in the raw list, processed measurements (in the proc list) have a constant sampling rate of $100$ Hz and the accelerometer/gyroscope measurements are aligned with each other. In addition, all sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FIC dataset (more info <a href="https://zenodo.org/record/4421861">here</a> and <a href="https://mug.ee.auth.gr/intake-cycle-detection/">here</a>). No other preprocessing is performed on the data; e.g., the acceleration component due to the Earth's gravitational field is present at the processed acceleration measurements. The potential researcher can consult the article "A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches" by Kyritsis et al. on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component). meal_gt: list Each element of this list is a $K\times2$ matrix. Each row represents the meal intervals for the specific in-the-wild session. The first column contains the timestamps of the meal start moments whereas the second one the timestamps of the meal end moments. All timestamps are in seconds. The number of meals $K$ varies across recordings (e.g., a recording exist where a participant consumed two meals). Ethics and funding Informed consent, including permission for third-party access to anonymised data, was obtained from all subjects prior to their engagement in the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 727688 - <a href="https://bigoprogram.eu/">BigO: Big data against childhood obesity</a>. Contact Any inquiries regarding the FreeFIC dataset should be addressed to: Dr. Konstantinos KYRITSIS Multimedia Understanding Group (MUG) Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124 Tel: +30 2310 996359, 996365  Fax: +30 2310 996398 E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr

opencc-by-4.0Sep 2017View details →

Wrist-mounted IMU data towards the investigation of in-meal human eating behavior - the Food Intake Cycle (FIC) dataset

Introduction The Food Intake Cycle (FIC) dataset was created by the <a href="http://mug.ee.auth.gr">Multimedia Understanding Group</a> towards the investigation of in-meal eating behavior. The FIC dataset contains the triaxial acceleration and orientation velocity signals ($6$ DoF) from $21$ meal sessions provided by $12$ unique subjects. All meals were recorded in the restaurant of Aristotle University of Thessaloniki using a commercial smartwatch, the Microsoft Band $2$™ for ten out of the twenty-one meals and the Sony Smartwatch $2$™ for the remaining meals. In addition, the start and end moments of each food intake cycle as well as of each micromovement are annotated throughout the FIC dataset. Description A total of $12$ subjects were recorded while eating their launch at the university’s cafeteria. The total duration of the $21$ meals sums up to $246$ minutes, with a mean duration of $11.7$ minutes. Each participant was free to select the food of their preference, typically consisting of a starter soup, a salad, a main course and a desert. Prior to the recording, the participant was asked to wear the smartwatch to the hand that he typically uses in his everyday life to manipulate the fork and/or the spoon. A GoPro™ Hero $5$ camera was already set at the table of the participant using a small, $23$ cm in height, tripod facing the participant, including both the food tray and upper body part in it’s field of view. The purpose of video recording was to obtain ground truth data by manually annotating the IMU sequences based on the video stream. Participants were also asked to perform a clapping hand movement both at the start and end of the meal, for synchronization purposes (as this movement is distinctive in the accelerometer signal). No other instructions were given to the participants. It should be noted that the FIC dataset does not contain instances related with liquid consumption or eating without the fork, knife and spoon (e.g. eating directly with hands). The accompanying python script viz_dataset.py will visualize the IMU signals and food intake cycle (i.e., bite) ground truth intervals for each of the recordings. Information on how to execute the Python scripts can be found below. <pre><code class="language-python"># The script(s) and the pickle file must be located in the same directory. # Tested with Python 3.6.4 # Requirements: Numpy, Pickle and Matplotlib # Visualize signals and ground truth $ python viz_dataset.py</code></pre> FIC is also tightly related to FreeFIC, a dataset we created in order to investigate the in-the-wild eating behavior. More information on FreeFIC can be found <a href="https://zenodo.org/record/4421951">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>. Annotation Micromovements For all recordings, the start and end points of all $6$ micromovements of interest were manually labeled. The micromovements of interest include: <ul> <li>pick food, wrist manipulates a fork to pick food from the plate</li> <li>upwards, wrist moves upwards, towards the mouth area</li> <li>downwards, wrist moves downwards, away from the mouth area</li> <li>mouth, wrist inserts food in mouth</li> <li>no movement, wrist exhibits no movement</li> <li>other movement, every other wrist movement</li> </ul> The annotation process was performed in such a way that the start and end times of each micro-movement span the whole meal session, without overlapping each other. Food intake cycles For all recordings, we annotated the start and end points for each intake cycle (i.e. every bite). Each food intake cycle starts with a p, ends with a d and contains an m micromovement. Publications If you plan to use the FIC dataset or any of the resources found in this page, please cite our work: <pre><code>@article{kyritsis2019modeling, title={Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, journal={IEEE journal of biomedical and health informatics}, year={2019}, publisher={IEEE}}</code></pre> <pre><code>@inproceedings{kyritsis2017food, title={Food intake detection from inertial sensors using lstm networks}, author={Kyritsis, Konstantinos and Diou, Christos and Delopoulos, Anastasios}, booktitle={International Conference on Image Analysis and Processing}, pages={411--418}, year={2017}, organization={Springer}}</code></pre> <pre><code>@inproceedings{kyritsis2017automated, title={Automated analysis of in meal eating behavior using a commercial wristband IMU sensor}, author={Kyritsis, Konstantinos and Tatli, Christina Lefkothea and Diou, Christos and Delopoulos, Anastasios}, booktitle={2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, pages={2843--2846}, year={2017}, organization={IEEE}}</code></pre> Technical details We provide the FIC dataset as a <a href="https://docs.python.org/3/library/pickle.html">pickle</a>. The file can be loaded using Python in the following way: <pre><code class="language-python">import pickle as pkl import numpy as np with open('./FIC.pkl','rb') as fh: dataset = pkl.load(fh)</code></pre> The dataset variable in the snipet above is a dictionary with $6$ keys. Namely: <ul> <li>'subject_id'</li> <li>'session_id'</li> <li>'signals_raw'</li> <li>'signals_proc'</li> <li>'meal_gt'</li> <li>'bite_gt'</li> </ul> The contents under a specific key can be obtained by: <pre><code>sub = dataset['subject_id'] # for the subject id ses = dataset['session_id'] # for the session id raw = dataset['signals_raw'] # for the raw IMU signals proc = dataset['signals_proc'] # for the processed IMU signals mm = dataset['mm_gt'] # for the micromovement ground truth bite = dataset['bite_gt'] # for the bite ground truth</code></pre> The sub, ses, raw, proc, mm and gt variables in the snipet above are lists with a length equal to $21$. Elements across all lists are aligned; e.g., the 3rd element of the list under the 'session_id' key corresponds to the 3rd element of the list under the 'signals_proc' key. sub: list Each element of the sub list is a scalar (integer) that corresponds to the unique identifier of the subject that can take values between $1$ and $12$. Moreover, the subject identifier in FIC is in-line with the subject identifier in the FreeFIC dataset (information available <a href="https://zenodo.org/record/4421951">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>); i.e., FIC’s subject with id equal to 2  is the same person as FreeFIC’s subject with id equal to 2. ses: list Each element of this list is a scalar (integer) that corresponds to the unique identifier of the session that can range between 1 and $3$. It should be noted that not all subjects have the same number of sessions. raw: list Each element of this list is dictionary with the 'acc', 'gyr' and 'offset' keys. The data under the 'acc' key is a $N_{acc}\times4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw accelerometer measurements in $g$ (second, third and forth columns - representing the $x, y$ and $z$ axis, respectively). The data under the 'gyr' key is a $N_{gyr} \times 4$ numpy.ndarray that contains the timestamps in seconds (first column) and the $3D$ raw gyroscope measurements in $degrees/second$(second, third and forth columns - representing the $x, y$ and $z$ axis, respectively). All sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FreeFIC dataset (information available <a href="https://zenodo.org/record/4420039">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>). Finally, the length of the raw accelerometer and gyroscope numpy.ndarrays is different $N_{acc} \neq N_{gyr}$. This behavior is predictable and is caused by the Android/MS Band platforms. The offset key contains a float that is used to align the IMU sensor streams with the videos that were used for annotation purposes (videos are not provided). proc: list Each element of this list is an $M \times 7$  numpy.ndarray that contains the timestamps, $3D$ accelerometer and gyroscope measurements for each meal. Specifically, the first column contains the timestamps in seconds, the second, third and forth columns contain the $x,y$ and $z$ accelerometer values in $g$ and the fifth, sixth and seventh columns contain the $x, y$ and $z$ gyroscope values in $degrees/second$. Unlike elements in the raw list, processed measurements (in the proc list) have a constant sampling rate of 100 Hz and the accelerometer/gyroscope measurements are aligned with each other. In addition, all sensor streams are transformed in such a way that reflects all participants wearing the smartwatch at the same hand with the same orientation, thusly achieving data uniformity. This transformation is in par with the signals in the FreeFIC dataset (information available <a href="https://zenodo.org/record/4420039">here</a> and <a href="https://mug.ee.auth.gr/free-food-intake-cycle-detection/">here</a>). No other preprocessing is performed on the data; e.g., the acceleration component due to the Earth's gravitational field is present at the processed acceleration measurements. The potential researcher can consult the article "Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data" by Kyritsis et al. on how to further preprocess the IMU signals (i.e., smooth and remove the gravitational component). mm: list Each element of this list is a $K \times 3$ numpy.ndarray. Each row represents a single micromovement interval. The first column contains the timestamps of the start moments in seconds, the second column the timestamps of the end moments in seconds and the third column a number representing the type of the micromovement. The identifier to micromovement mapping is provided below: $[1] \rightarrow$ no movement $[2] \rightarrow$ upwards $[3] \rightarrow$ downwards $[4] \rightarrow$ pick food $[5] \rightarrow$ mouth $[6] \rightarrow$ other movement bite: list Each element of this list is a $L\times2$ numpy.ndarray. Each row represents a single food intake event (i.e., a bite). The first column contains the start moments while the second column contains the end moments of each intake event. Both the start and end moments are provided in seconds. Ethics and funding Informed consent, including permission for third-party access to anonymised data, was obtained from all subjects prior to their engagement in the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 727688 - <a href="https://bigoprogram.eu/">BigO: Big data against childhood obesity</a>. Contact Any inquiries regarding the FIC dataset should be addressed to: Dr. Konstantinos KYRITSIS Multimedia Understanding Group (MUG) Department of Electrical & Computer Engineering Aristotle University of Thessaloniki University Campus, Building C, 3rd floor Thessaloniki, Greece, GR54124 Tel: +30 2310 996359, 996365  Fax: +30 2310 996398 E-mail: kokirits [at] mug [dot] ee [dot] auth [dot] gr

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-4.0Dec 2023View details →

Wrist and Tibia/Shoe Mounted IMU Measurement Results for Gait Analysis

This document describes a dataset of measurement results collected using wrist, tibia and shoe mounted inertial sensors. The main purpose of the dataset was to test signal translation algorithms converting signals registered using the wrist-worn sensor e.g. a smartwatch to signals which would be measured with a shoe or tibia - mounted device. The dataset includes tri-axial acceleration and angular velocity registered during several walks. More details are included in the dataset_description pdf file. When using the data, please consider also citing the original paper, for which it was collected: Kolakowski, M.; Djaja-Josko, V.; Kolakowski, J.; Cichocki, J. Wrist-to-Tibia/Shoe Inertial Measurement Results Translation Using Neural Networks. Sensors 2024, 24, 293. <a href="https://doi.org/10.3390/s24010293" target="_blank" rel="noopener">https://doi.org/10.3390/s24010293</a> The dataset will be gradually updated as new data are gathered.

opencc-by-4.0Nov 2021View details →

AI4EU Robotics Pilot: Vibration sensor measurements in a robotic wrist

The robotic wrist demonstrator represents a mechanical wrist with three axes that can hold tools, e.g. for spray painting in combination with a pump. On this robotic wrist, two accelerometers are mounted for vibration monitoring and recording: one in the movable front part of the wrist and one in the shaft. The wrist can be controlled through the torque or the designated position of each axis’ motor. The dataset consists of 1.8 billion measurements of several sensor data of the robotic wrist in 1-second intervals over six months in 2020. The data is split by the recording date over 98 files.

opencc-by-4.0May 2023View details →

Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-worn Inertial Sensors

In this paper we present a benchmark dataset for evaluation of physical human activity recognition from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport-relevant activities could be used in applications toward game analysis, guided training, and personal physical activity tracking. The dataset was recorded for two teams from separate countries (USA and Germany) with a total of 24 players who wore an inertial sensor on their wrist and spanned both repetitive basketball training sessions and full games. Particular features of this dataset include an inherent variance through cultural differences in game rules and styles as the data was recorded in two countries, as well as different sport skill levels, since the participants were heterogeneous in terms of prior basketball experience. We illustrate the datasets' features in several time-series analyses and report on a baseline classification performance study with a state-of-the-art deep learning architecture.

opencc-by-sa-4.0Jun 2023View details →

Wrist-worn sensor validation for heart rate variability and electrodermal activity detection in a stressful driving environment

The current dataset contributes to assess the accuracy of the Empatica 4 (E4) wristband for the detection of heart rate variability (HRV) and electrodermal activity (EDA) metrics in stress-inducing conditions and growing-risk driving scenarios. Heart Rate Variability (HRV) and ElectroDermal Activity (EDA) signals were recorded over six experimental conditions (i.e., Baseline, Video Clip, Scream, No Risk Driving, Low-Risk Driving, and High-Risk Driving) and by means of two measurement systems: the E4 device and a gold standard system. The raw quality of the physiological signals was enhanced by means of robust semi-automatic reconstruction algorithms. Heart Rate Variability time-domain parameters showed high accuracy in motion-free experimental conditions, while Heart Rate Variability frequency-domain parameters reported sufficient accuracy in almost every experimental condition.

Project Engage: A Wrist Biosensor-based mHealth Suite to Support Alcohol Intervention in Young People Living With HIV

ClinicalTrials.gov study NCT05431855. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

Dorsal Wrist Ganglia; Aspiration Alone vs Aspiration and Injection of Platelet Rich Plasma

ClinicalTrials.gov study NCT03408808. IPD Sharing: NO. Countries: 1. Publications: 7.

controlledIPD-YESFeb 2026View details →

The Swedish Hockey League Hand and Wrist Injury Tracking Trial

ClinicalTrials.gov study NCT07031869. IPD Sharing: YES. Countries: 1. Publications: 5.

closedIPD-NOFeb 2026View details →

Taping to Control Edema in Patients With Forearm Plaster for Wrist Fracture.

ClinicalTrials.gov study NCT04683887. IPD Sharing: NO. Countries: 1. Publications: 2.

opencc-by-4.0Jan 2020View details →

Wrist Vascular Biometric Recognition Using a Portable Contactless System - Video

Human wrist vein biometric recognition is one of the least used vascular biometric modalities. Nevertheless, it has similar usability and is as safe as the two most common vascular variants in the commercial and research worlds: hand palm vein and finger vein modalities. Besides, the wrist vein variant, with wider veins, provides a clearer and better visualization and definition of the unique vein patterns. In this paper, a novel vein wrist non-contact system has been designed, implemented, and tested. For this purpose, a new contactless database has been collected with the software algorithm TGS-CVBR®. The database, called UC3M-CV1, consists of 1200 near-infrared contactless images of 100 different users, collected in two separate sessions, from the wrists of 50 subjects (25 females and 25 males). Environmental light conditions for the different subjects and sessions have been not controlled: different daytimes and different places (outdoor/indoor). The software algorithm created for the recognition task is PIS-CVBR®. The results obtained by combining these three elements, TGS-CVBR®, PIS-CVBR®, and UC3M-CV1 dataset, are compared using two other different wrist contact databases, PUT and UC3M (best value of Equal Error Rate (EER) = 0.08%), taken into account and measured the computing time, demonstrating the viability of obtaining a contactless real-time-processing wrist system.

opencc-by-4.0Jun 2020View details →

Upper body biomechanical model for the analysis of the wrist motion amplitude

The following files are available: - The Visual3D files (.cmo) corresponding to the upper body biomechanical model - Videos of a pilot data acquisition from a healthy individual - Videos of the biomechanical models of 11 individuals with Parkinson's disease, used to assess the motion amplitude of the wrist with and without the use of an active orthosis, for four scenarios: Extension Without Orthosis (ENO), Extension With Orthosis (EWO), Flexion Without Orthosis (FNO) and Flexion With Orthosis (FWO)

opennotspecifiedNov 2017View details →

On following pages: 248. Merriam''s Deermouse (Peromyscus merriami); 249. Cactus Deermouse (Peromyscus eremicus); 250. San Lorenzo Deermouse (Peromyscus interparietalis); 251. Southern Baja Deermouse (Peromyscus eva); 252. Northern Baja Deermouse (Peromyscus fraterculus); 253. Monserrat Island Deermouse (Peromyscus caniceps); 254. Dickey's Deermouse (Peromyscus dickeyi); 255. La Guarda Deermouse (Peromyscus guardia); 256. Coronados Deermouse (Peromyscus pseudocrinitus); 257. Cotton Deermouse (Peromyscus gossypinus); 258. White-footed Deermouse (Peromyscus leucopus); 259. Santa Cruz Deermouse (Peromyscus sejugis); 260. North-western Deermouse (Peromyscus keeni): 261. Oldfield Deermouse (Peromyscus polionotus); 262. North American Deermouse (Peromyscus maniculatus); 263. Black-eared Deermouse (Peromyscus melanotis); 264. Black-tailed Deermouse (Peromyscus melanurus); 265. Broad-faced Deermouse (Peromyscus megalops); 266. Black-wristed Deermouse (Peromyscus melanocarpus); 267. Catalina Deermouse (Peromyscus slevini); 268. Tawny Deermouse (Peromyscus perfulvus); 269. Plateau Deermouse (Peromyscus melanophrys); 270. Puebla Deermouse (Peromyscus mekisturus); 271. Mayan Deermouse (Peromyscus mayensis); 272. Stirton's Deermouse (Peromyscus stirtoni); 273. Yucatan Deermouse (Peromyscus yucatanicus); 274. Chimoxan Deermouse (Peromyscus tropicalis); 275. Talamancan Deermouse (Peromyscus nudipes); 276. Mexican Deermouse (Peromyscus mexicanus); 277. Naked-eared Deermouse (Peromyscus gymnotis); 278. Chiapan Deermouse (Peromyscus zarhynchus); 279. Gardner's Deermouse (Peromyscus gardneri); 280. Nicaraguan Deermouse (Peromyscus nicaraguae); 281. Salvadorean Deermouse (Peromyscus salvadorensis); 282. Guatemalan Deermouse (Peromyscus guatemalensis); 283. Large Deermouse (Peromyscus grandis). in Cricetidae

On following pages: 248. Merriam''s Deermouse (Peromyscus merriami); 249. Cactus Deermouse (Peromyscus eremicus); 250. San Lorenzo Deermouse (Peromyscus interparietalis); 251. Southern Baja Deermouse (Peromyscus eva); 252. Northern Baja Deermouse (Peromyscus fraterculus); 253. Monserrat Island Deermouse (Peromyscus caniceps); 254. Dickey's Deermouse (Peromyscus dickeyi); 255. La Guarda Deermouse (Peromyscus guardia); 256. Coronados Deermouse (Peromyscus pseudocrinitus); 257. Cotton Deermouse (Peromyscus gossypinus); 258. White-footed Deermouse (Peromyscus leucopus); 259. Santa Cruz Deermouse (Peromyscus sejugis); 260. North-western Deermouse (Peromyscus keeni): 261. Oldfield Deermouse (Peromyscus polionotus); 262. North American Deermouse (Peromyscus maniculatus); 263. Black-eared Deermouse (Peromyscus melanotis); 264. Black-tailed Deermouse (Peromyscus melanurus); 265. Broad-faced Deermouse (Peromyscus megalops); 266. Black-wristed Deermouse (Peromyscus melanocarpus); 267. Catalina Deermouse (Peromyscus slevini); 268. Tawny Deermouse (Peromyscus perfulvus); 269. Plateau Deermouse (Peromyscus melanophrys); 270. Puebla Deermouse (Peromyscus mekisturus); 271. Mayan Deermouse (Peromyscus mayensis); 272. Stirton's Deermouse (Peromyscus stirtoni); 273. Yucatan Deermouse (Peromyscus yucatanicus); 274. Chimoxan Deermouse (Peromyscus tropicalis); 275. Talamancan Deermouse (Peromyscus nudipes); 276. Mexican Deermouse (Peromyscus mexicanus); 277. Naked-eared Deermouse (Peromyscus gymnotis); 278. Chiapan Deermouse (Peromyscus zarhynchus); 279. Gardner's Deermouse (Peromyscus gardneri); 280. Nicaraguan Deermouse (Peromyscus nicaraguae); 281. Salvadorean Deermouse (Peromyscus salvadorensis); 282. Guatemalan Deermouse (Peromyscus guatemalensis); 283. Large Deermouse (Peromyscus grandis).

openother-ncMay 2024View details →

Long-term electrocardiogram and wrist-based photoplethysmogram recordings with annotated atrial fibrillation episodes

Rationale Atrial fibrillation (AF) has emerged as a worldwide cardiovascular epidemic affecting more than 33 million individuals worldwide and carrying a 5-fold increased risk of brain stroke and a 3-fold increased risk of heart failure (Hindricks et al. 2021). AF is a progressive disease, with primary paroxysmal episodes being self-terminating; therefore, the success of complication management highly depends on early arrhythmia detection, which often requires long-term AF monitoring (Keach et al. 2015). Unfortunately, existing devices for long-term AF monitoring are either expensive (implantable cardiac monitors) or inconvenient due to skin irritation (Holter monitors, electrocardiogram (ECG) patches). Thus, it is desirable to develop inexpensive technologies ensuring wearing comfort. Recently, biooptical photoplethysmography (PPG) signal has emerged as such technology with immense potential for convenient long-term AF monitoring (Pereira et al. 2020). However, due to the lack of guidelines for arrhythmia interpretation in PPG, simultaneous ECG recording is needed for verification of the episodes detected in PPG. The present dataset contains simultaneously acquired wrist-based PPG and reference ECG signals with annotated AF episodes, and thus, is particularly suitable for use in the development and testing of automatic PPG-based AF detectors. Subjects and data acquisition protocol The dataset contains long-term ECG and PPG signals from 45 patients with suspected AF monitored for 5 to 8 days (306 days in total). Detailed demographic (sex, age, height, weight) and clinical (diagnosed comorbidities, medications) characteristics of the patients are provided in the supplementary file subject_info.xlsx. The acquisition of the PPG and ECG signals was started at Vilnius University Hospital Santaros Klinikos (Vilnius, Lithuania) and continued for a week at the patient’s home. The PPG signal was acquired at a sampling frequency of 100 Hz using a green LED embedded in a wrist-worn device developed at the Biomedical Engineering Institute (Kaunas, Lithuania). The reference ECG signal was acquired at a sampling frequency of 500 Hz using the Bittium Faros™ 180 ECG device together with the Bittium OmegaSnap™ patch electrode (Oulu, Finland). Additionally, triaxial acceleration signals were acquired with both devices at sampling frequencies of 50 and 25 Hz using wrist-worn and reference ECG devices, respectively. The occurrence times of QRS-complexes in ECG signals were obtained using an open-source QRS detector (Moeyersons et al. 2020), and initial AF episodes were automatically detected using a low-complexity AF detector relying on rhythm irregularity information (Petrėnas et al. 2015). Then, the AF detector output was visually inspected and manually corrected by medical specialists experienced in arrhythmia diagnosis with the aim to find undetected and discard falsely detected episodes. The data acquisition protocol was in accordance with the ethical principles of the Declaration of Helsinki and was approved by Vilnius Region Biomedical Research Ethics Committee (No. 158200-18/7-1052-557). All patients gave written informed consent to participate. Technical details The acquired signals are provided in MAT-files named as follows: XX_YYY, where XX is the patient ID, YYY is ECG for the signals from the Bittium Faros™ 180 ECG device and PPG for the signals from the wrist-worn device. For each patient, there are a single continuous ECG recording and multiple PPG recordings because the acquisition of the PPG could be interrupted for a short time due to technical reasons or for a longer time to allow battery charging of the wrist-worn device. In addition to PPG, ECG, and acceleration signals, each file contains a signal header, the day when the recording started with respect to the first monitoring day of the patient, and the time of day when the recording started. The ECG files also contain QRS time indices and calculated RR intervals together with AF annotations on a beat-to-beat basis. PPG and acceleration signals from a wrist-worn device were synchronized to correspond time of  ECG acquisition device.  In the subject_info.xlsx file, physical inactivity is defined as < 5000 steps/day or < 150 min/week of moderate-intensity exercise or < 75 min/week of high-intensity exercise, excessive physical activity is defined as > 750 min/week of moderate-intensity exercise, and hypertension is classified into stages based on systolic/diastolic blood pressure: stage I corresponding to 140/90–159/99 mmHg, stage II to 160/100–179/109 mmHg, and stage III to ≥ 180/100 mmHg. Limitations When using the resource, researchers should be aware that the PPG and ECG acquisition devices have been synchronized, however, the exact alignment of the signals cannot be reached due to physiological features, e.g., the heart rate obtained from the PPG and ECG signals. Users should also be aware that the sampling frequencies of the devices can vary slightly.

opencc-by-4.0Jun 2023View details →

An OpenSim-based closed-loop biomechanical wrist model for pathological tremor simulation (dataset)

5 subjects (4 PD and 1 ET) IMU and sEMG raw data supporting the conclusions of the article "An OpenSim-based closed-loop biomechanical wrist model for pathological tremor simulation" name standard: pacientnumber_age_sex_disease_arm_timesincediagnose.mat Xs, Xs1 ... Xs17 are the time vectors for sEMG and IMU.    If you use any part of this data for your research, please cite our paper: <pre>@article{pinheiro2024opensim, title={An OpenSim-based closed-loop biomechanical wrist model for subject-specific pathological tremor simulation}, author={Pinheiro, Wellington C and Ferraz, Henrique B and Castro, Maria Claudia F and Menegaldo, Luciano L}, journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, year={2024}, publisher={IEEE} }</pre>   Contact: wellington@peb.ufrj.br

Ultrasonographic Evaluation of the Effects of Different Wrist Positions on the Median Nerve in Patients With Generalized Joint Hypermobility

ClinicalTrials.gov study NCT06918613. IPD Sharing: Not stated. Countries: 1. Publications: 3.

closedIPD-NOFeb 2026View details →

Effect of Kinesiotape Technique on Wrist Among Chronic Stroke Patients

ClinicalTrials.gov study NCT05526859. IPD Sharing: NO. Countries: 1. Publications: 9.

Investigation of the Effect of Epicondylitis Band and Wrist Orthosis

ClinicalTrials.gov study NCT05731869. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Effect of Laser Acupuncture on Forearm Bone Mineral Density and Wrist Pain in Osteoporotic Postmenopausal Women

ClinicalTrials.gov study NCT05559619. IPD Sharing: Not stated. Countries: 1. Publications: 1.