Datasets
The datasets published by the lab are organised according to their project group affiliation.
Applied Machine Learning
Description
The images have been taken at the polar bear enclosure at Nuremberg Zoo, which is home to two mature animals (Vera, female adult and Nanuq, male adult). The three on-site cameras acquire videos with a frame rate of 12.5 fps and a resolution of 3840×2160 pixels. For the aim of this project, a period of five days of data (27 April – 1 May 2020) has been selected. During this period, the polar bears shared both enclosures and thus might both be present in a single image. A total of 4450 frames were randomly selected and stored for further labeling. Three biologists annotated all images to provide labels of high quality by assigning labeled bounding boxes to the animals visible in the picture. We provide the resulting mean annotations. As the 4450 images were randomly selected from the video data, only 2099 instances show one or more animals. 167 images show both animals, 1932 only one. 2266 bounding boxes are provided, 1082 for the male and 1184 for the female bear.
For further information, please refer to the associated paper.
Publication
Please cite this publication when using the dataset:
- Zürl M., Stoll P., Brehm I., Raab R., Zanca D., Kabri S., Happold J., Nille H., Prechtel K., Wünsch S., Krause M., Seegerer S., von Fersen L., Eskofier B.:
Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning—A Study on Polar Bears
In: Animals 12 (2022), p. 692
ISSN: 2076-2615
DOI: 10.3390/ani12060692
BibTeX: Download
Download Link
To download the dataset, please refer to the official zenodo website.
Dataset Coordinator
Biomechanical Motion Analysis and Creation
Description
This data set contains raw and processed data of gait analysis experiments of level and inclined walking at two speeds for 12 participants. The slopes were uphill and downhill with 8% incline. The raw data contains the output of the force plates and marker data, as well as raw measurements from an K4B2 system. Mat files are processed data: measured metabolic rate, and measured and calculated metabolic cost, as well as kinetic and kinematic data of an averaged gait cycle: joint angles, velocities and moments, ground reaction forces, muscle activation, contractile element length and stimulation, and the duration of the gait cycle.
For further information, please refer to the official zenodo website.
Publication
Please cite this publication when using the dataset:
- Koelewijn A., Heinrich D., van den Bogert AJ.:
Metabolic cost calculations of gait using musculoskeletal energy models, a comparison study
In: PLoS ONE 14 (2019), Article No.: e0222037
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0222037
BibTeX: Download
Download Link
To download the dataset, please refer to the official zenodo website.
Dataset Coordinator
Description
In this dataset, a full-body three dimensional musculoskeletal model is extended to be specialized for running with directional changes. Model dynamics were implemented implicitly and trajectory optimization problems were solved with direct collocation to enable efficient computation. Standing, straight running, and curved running were simulated starting from a random initial guess to confirm the capabilities of our model and approach: efficacy, tracking and predictive power. Altogether the simulations required 1 h 17 min and corresponded well to the reference data.
For further information, please refer to the associated publication.
Publication
Please cite this publication when using the dataset:
- Nitschke M., Dorschky E., Heinrich D., Schlarb H., Eskofier B., Koelewijn A., van den Bogert AJ.:
Efficient trajectory optimization for curved running using a 3D musculoskeletal model with implicit dynamics
In: Scientific Reports (2020), Article No.: 17655
ISSN: 2045-2322
DOI: 10.1038/s41598-020-73856-w
URL: https://www.nature.com/articles/s41598-020-73856-w
BibTeX: Download
Download Link
To download the dataset, please refer to the official SimTK website.
Dataset Coordinator
Description
This study investigated the feasibility and accuracy of reconstructing, especially change of direction motions with a 3D full-body musculoskeletal model by tracking marker and ground reaction force (GRF) data in optimal control simulations. We recorded in total 30 trials with optical motion capture. Using this data, we compared inverse methods (inverse kinematics and dynamics) to coordinate tracking simulations and marker tracking simulations.
For further information, please refer to the associated publication.
Publication
Please cite this publication when using the dataset:
- Nitschke M., Marzilger R., Leyendecker S., Eskofier B., Koelewijn A.:
Change the direction: 3D optimal control simulation by directly tracking marker and ground reaction force data
In: PeerJ (2023)
ISSN: 2167-8359
DOI: 10.7717/peerj.14852
URL: https://peerj.com/articles/14852/
BibTeX: Download
Download Link
To download the dataset, please refer to the official zenodo website.
Dataset Coordinator
Digital Health – Biosignals
Description
While convolutions are known to be invariant to (discrete) translations, scaling continues to be a challenge and most image recognition networks are not invariant to them. To explore these effects, we have created the Scaled and Translated Image Recognition (STIR) dataset. This dataset contains objects of size s∈[17,64], each randomly placed in a 64×64 pixel image.
For further information, please refer to the official zenodo website.
Publication
Please cite this publication when using the dataset:
[1] Altstidl, T., Nguyen, A., Schwinn, L., Köferl, F., Mutschler, C., Eskofier, B., & Zanca, D. (2022). Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks. arXiv preprint arXiv:2211.10288.
Download Link
To download the dataset, please refer to the official zenodo website.
Dataset Coordinator
Description
The DaLiAc (Daily Life Activities) database consists of data from 19 subjects (8 female and 11 male, age 26 ± 8 years, height 177 ± 11 cm, weight 75.2 ± 14.2 kg, mean ± standard deviation (SD)) that performed 13 daily life activities. These activities were chosen according to their different Metabolic Equivalent of Task (MET) values. Four SHIMMER (Shimmer Research, Dublin, Ireland) sensors were used for data acquisition. Each sensor node was equipped with a triaxial accelerometer and a triaxial gyroscope. Data were sampled with 200 Hz and were stored on SD card. The sensor nodes were placed on the left ankle, the right hip, the chest, and the right ankle.
For further information, please refer to the associated publication.
Publication
Please cite this publication when using the dataset:
Download Link
The download link is currently not available, please contact the dataset coordinator. For further information, please visit the official MaD Lab website.
Dataset Coordinator
Description
Two SHIMMER (Shimmer Research, Dublin, Ireland) sensors were used for data acquisition. Each sensor node was equipped with a triaxial accelerometer and a triaxial gyroscope. Data were sampled with 200 Hz and were stored on SD card. The Basic Step Activities database consists of data from 15 subjects (8 female and 7 male, age 23 ± 2 years, height 178 ± 12.5 cm, weight 75.0 ± 16.5 kg, mean ± standard deviation (SD)) that performed 7 daily life activities. These activities were chosen according to their different Metabolic Equivalent of Task (MET) values.
For further information, please refer to the associated publication.
Publication
Please cite this publication when using the dataset:
- Leutheuser H., Dölfel S., Schuldhaus D., Reinfelder S., Eskofier B.:
Performance Comparison of Two Step Segmentation Algorithms using Different Step Activities
11th International Conference on Wearable and Implantable Body Sensor Networks (BSN) (Zürich, Switzerland, 16. June 2014 - 19. June 2014)
In: Wearable and Implantable Body Sensor Networks (BSN), 2014 11th International Conference on 2014
DOI: 10.1109/BSN.2014.37
BibTeX: Download
Download Link
The download link is currently not available, please contact the dataset coordinator. For further information, please visit the official MaD Lab website.
Dataset Coordinator
Description
The EnEx (Energy Expenditure) database consists of data from ten subjects (3 female and 7 male, age 49 ± 12 years, height 178 ± 10 cm, weight 80.7 ± 14.6 kg, mean ± standard deviation (SD)). In one trial, each subject had to run on a traditional treadmill at three different speed levels ([3.2, 4.8, 6.4] km/h). In a second trial, an oscillating treadmill was used imposing different levels of physical activity. Each speed level lasted six minutes. Two SHIMMER sensor nodes were placed on right hip and right ankle. Each sensor node consisted of three accelerometer axes and three gyroscope axes. The sampling rate of the sensor nodes were set to 204.8 Hz. For the expended energy, the oxygen consumption was measured by a spirometry system. The sampling rate was 0.2 Hz.
For further information, please refer to the associated publication.
Publication
Please cite this publication when using the dataset:
- Schuldhaus D., Dorn S., Leutheuser H., Tallner A., Klucken J., Eskofier B.:
An Adaptable Inertial Sensor Fusion-Based Approach for Energy Expenditure Estimation.
The 15th International Conference on Biomedical Engineering (ICBME 2013) (University Town, Singapore, 4. December 2013 - 7. December 2013)
In: Goh James (ed.): The 15th International Conference on Biomedical Engineering, Heidelberg: 2013
URL: http://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2014/Schuldhaus14-AAI.pdf
BibTeX: Download
Download Link
The download link is currently not available, please contact the dataset coordinator. For further information, please visit the official MaD Lab website.
Dataset Coordinator
Digital Health – PsychoSense
Description
In this study, the use of the Cold Face Test (CFT) protocol as an intervention to reduce acute stress responses was investigated. Twenty-eight healthy participants were exposed to acute psychosocial stress via the Montreal Imaging Stress Task (MIST) in a randomized between-subjects design while heart rate (HR), heart rate variability (HRV), and salivary cortisol were assessed.
For further information, please refer to the associated publication.
Publication
Please cite this publication when using the dataset:
- Richer R., Zenkner JJ., Küderle A., Rohleder N., Eskofier B.:
Vagus activation by Cold Face Test reduces acute psychosocial stress responses
In: Scientific Reports 12 (2022), Article No.: 19270
ISSN: 2045-2322
DOI: 10.1038/s41598-022-23222-9
URL: https://www.nature.com/articles/s41598-022-23222-9
BibTeX: Download
Download Link
To download the dataset, please refer to the official OSF website.
Dataset Coordinator
Description
This dataset contains cortisol awakening response (CAR) data, objective saliva sampling time logging from the CARWatch smartphone application, and IMU-based movement during the night. It was used as proof-of-concept validation of the CARWatch application for objective sampling time assessment and to assess the influence of the inner clock on the CAR and pre-awakening movement. For further information, please refer to the associated publications (see below).
Publication
Please consider citing these publications when using the dataset:
- Richer R., Abel L., Küderle A., Eskofier B., Rohleder N.:
CARWatch – A smartphone application for improving the accuracy of cortisol awakening response sampling
In: Psychoneuroendocrinology 151 (2023), p. 106073
ISSN: 0306-4530
DOI: 10.1016/j.psyneuen.2023.106073
URL: https://www.sciencedirect.com/science/article/abs/pii/S0306453023000513
BibTeX: Download - Richer R., Küderle A., Dörr J., Rohleder N., Eskofier B.:
Assessing the Influence of the Inner Clock on the Cortisol Awakening Response and Pre-Awakening Movement
2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (Athens, 28. July 2021 - 30. July 2021)
In: IEEE (ed.): 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) 2021
DOI: 10.1109/BHI50953.2021.9508529
URL: https://www.mad.tf.fau.de/files/2021/08/richer21_car_inner_clock.pdf
BibTeX: Download
Download Link
To download the dataset, please refer to the official OSF website.
Dataset Coordinator
Digital Health – Gait Analytics
Description
A dataset containing IMU recordings with full motion capture reference from 14 participants (approx. 10000 strides). Each participant was equipped with 15 synchronised IMUs (6 at different positions at each shoe, 1 at each ankle, and 1 and the lower back). The main goal of the dataset is to compare the recorded signals of the 6 sensors attached to each foot.
For further information, please refer to the ‘README.md’ file linked on the official zenodo website.
Publication
Please cite this publication when using the dataset:
- Küderle A., Roth N., Zlatanovic J., Zrenner M., Eskofier B., Kluge F.:
The placement of foot-mounted IMU sensors does affect the accuracy of spatial parameters during regular walking
In: PLoS ONE 17 (2022)
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0269567
BibTeX: Download
Download Link
To download the dataset, please refer to official zenodo website.
Dataset Coordinator
Description
This dataset provides spatio-temporal gait parameters recorded from 35 Parkinson’s disease patients during real-world gait and unsupervised 4×10 Meter Walking tests.
The data was collected as part of the FallRiskPD study (DRKS-ID: DRKS00015085) between March 2019 and June 2021 by the University Hospital Erlangen, the Hospital Rummelsberg, and the Ernst von Bergmann Hospital Potsdam. Raw data was recorded with the Mobile GaitLab (Portabiles HealthCare Technologies GmbH, Erlangen, Germany), consisting of two foot-worn inertial measurement units placed to the instep of the shoes. The recordings were acquired over the course of approximately two weeks, while the participants were following their activities of daily living and were additionally asked to perform 4×10 Meter Walking tests three times per day with preferred walking speed. After the gait recordings, fall events were captured over three months in a paper-based diary.
The sensor recordings were processed to stride-level spatio temporal gait parameters using a pipeline of gait sequence detection (Ullrich et al., 2020, JBHI), stride segmentation (Barth et al., 2015, Sensors), and event detection with trajectory reconstruction (Rampp et al., 2015, TBME). The following gait parameters are available: stride time [s], stance time [s], swing time [s], stride length [m], gait speed [m/s], IC foot angle [deg], FC foot angle [deg], maximum foot lift [m].
Three files are provided with the data set:
- strides_realworld_publication.csv: Contains the parameterized strides of the real-world activities including the patient ID, the date and time information, and the IDs of the corresponding walking bouts in which the respective strides can be grouped together.
- strides_4x10m_pref_at_home_publication.csv: Contains the parameterized strides of the unsupervised 4×10 Meter Walking tets including the patient ID, the date and time information, and the IDs of the corresponding gait tests that are counted with a suffix in the ID within one day.
- patient_data_publication.csv: Contains the basic patient information including demographics, UPDRS-III score, Hoehn and Yahr stage, and the occurance of falls in the follow up phase.
For further information, please refer to the associated publication and the official OSF website.
Publication
Please cite this publication when using the dataset:
- Ullrich M., Roth N., Küderle A., Richer R., Gladow T., Gaßner H., Marxreiter F., Klucken J., Eskofier B., Kluge F.:
Fall Risk Prediction in Parkinson’s Disease Using Real-World Inertial Sensor Gait Data
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering (2022)
ISSN: 1534-4320
DOI: 10.1109/jbhi.2022.3215921
URL: https://ieeexplore.ieee.org/document/9924545
BibTeX: Download
Download Link
To download the dataset, please refer to the official OSF website.
Dataset Coordinator
Description
As stairs are an essential part of our everyday lives and are frequently encountered in urban environments, stair ambulation sequences should be included in mobility analysis. Therefore, this dataset was recorded in January 2021 to enable the development and evaluation of algorithms for human gait analysis using wearable inertial sensors for real-world applications, including level-walking gait as well as stair ascending and stair descending.
For further information, please refer to the associated publication.
Publication
Please cite this publication when using the dataset:
- Roth N., Küderle A., Prossel D., Gaßner H., Eskofier B., Kluge F.:
An inertial sensor-based gait analysis pipeline for the assessment of real-world stair ambulation parameters
In: Sensors 21 (2021), Article No.: 6559
ISSN: 1424-8220
DOI: 10.3390/s21196559
BibTeX: Download
Download Link
To download the dataset, please refer to the official OSF website.
Dataset Coordinator
Description
The database consists of data from 15 subjects (eleven healthy subjects and four PD patients) that performed the 4×10 m straight walking tests. Data was acquired using two Shimmer3 (Shimmer Research, Dublin, Ireland) sensors laterally attached to the shoes. Data from 3D accelerometer and gyroscope were transmitted to a mobile device. The reference system was a camera-based markerless motion capture (Simi, Unterschleißheim, Germany). Synchronization was assured at the beginning and end of the measurements and the data was aligned accordingly.
For further information, please refer to the associated publication.
Publication
Please cite this publication when using the dataset:
- Kluge F., Gaßner H., Hannink J., Pasluosta CF., Klucken J., Eskofier B.:
Towards Mobile Gait Analysis: Concurrent Validity and Test-Retest Reliability of an Inertial Measurement System for the Assessment of Spatio-Temporal Gait Parameters
In: Sensors 17 (2017), p. 1522
ISSN: 1424-8220
DOI: 10.3390/s17071522
BibTeX: Download
Download Link
https://osf.io/cfb7e/
Dataset Coordinator
Description
The GaitPhase database consists of gait data from 21 subjects (10 male, 11 female, age: 23.8 yrs ± 3.3 yrs, height: 172.8 cm ± 9.4 cm, weight: 66.6 kg ± 10.9 kg; all values are mean ± standard deviation). In total, 25306 steps were acquired. The performed excercise was walking on a split-belt treadmill at 12 different speeds in the interval [0.6, 1.7] m/s with 0.1 m/s increments for one minute at each speed.
Three-dimensional kinematic data using a Qualisys (Qualisys AB, Gothenburg, Sweden) motion capture system with 8 Oqus cameras sampling at 200 Hz and an instrumented Bertec (Bertec Corporation, Columbus, OH, USA) split-belt treadmill with integrated force plates sampling at 1000 Hz were used for data acquisition. Both systems were synchronized camera frame-wise.
For further information, please refer to the associated publication.
Publication
Please cite this publication when using the dataset:
- Hebenstreit F., Leibold A., Krinner S., Welsch G., Lochmann M., Eskofier B.:
Effect of walking speed on gait sub phase durations
In: Human Movement Science 43 (2015), p. 118-24
ISSN: 0167-9457
DOI: 10.1016/j.humov.2015.07.009
URL: https://www.mad.tf.fau.de/files/2020/12/hebenstreit_2015_hms_proof2.pdf
BibTeX: Download
Download Link
The download link is currently not available, please contact the dataset coordinator. For further information, please visit the official MaD Lab website.
Dataset Coordinator
Description
The dataset consists of 12 different task-driven activities, 10 of which are cyclic. It contains over 150,000 labeled cycles, each with 2 phases, from 80 subjects. The activities include not only straight and steady-state motions, but also transitions, different ranges of bouts, and changing directions. Each participant wore 5 synchronized inertial measurement units (IMUs) on the wrists, shoes, and in a pocket, as well as pressure insoles and video.
For further information, please refer to the associated publication.
Publication
Please cite these publications when using the dataset:
- Martindale C., Sprager S., Eskofier B.:
Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables
In: Sensors 198 (2019), Article No.: 1820
ISSN: 1424-8220
DOI: 10.3390/s19081820
URL: https://www.mdpi.com/1424-8220/19/8/1820
BibTeX: Download - Martindale C., Roth N., Hannink J., Sprager S., Eskofier B.:
Smart Annotation Tool for Multi-sensor gait based daily activity data
In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) 2018
DOI: 10.1109/PERCOMW.2018.8480193
URL: https://www.mad.tf.fau.de/files/2018/09/percom2018_martindale.pdf
BibTeX: Download
Download Link
The download link is currently not available, please contact the dataset coordinator. For further information, please visit the official MaD Lab website.
Dataset Coordinator
Description
The database consists of data from 20 healthy subjects. Their characteristics are as follows: 5 females and 15 males, with an average age of 28 years, an average height of 175 cm and weight of 74 kg. The shoe sizes were limited to the range of 38 to 44 due to the available insole sizes. Data was acquired using five IMU sensors laterally attached to the left and right shoes as well as wrists and within the right pocket. Data from 3D accelerometer and gyroscope were recorded and well as pressure data from Moticon insoles. The data was synchronised and labelled using the smart annotation tool.
For further information, please refer to the associated publication.
Publication
Please cite this publication when using the dataset:
- Martindale C., Roth N., Hannink J., Sprager S., Eskofier B.:
Smart Annotation Tool for Multi-sensor gait based daily activity data
In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) 2018
DOI: 10.1109/PERCOMW.2018.8480193
URL: https://www.mad.tf.fau.de/files/2018/09/percom2018_martindale.pdf
BibTeX: Download
Download Link
The download link is currently not available, please contact the dataset coordinator. For further information, please visit the official MaD Lab website.
Dataset Coordinator
Description
The database consists of data from 18 healthy subjects who performed walking and running. The participants performed walking and running and standing in an outdoor environment on varying surfaces and performed a small circuit around the building. Data was acquired using two IMU sensors laterally attached to the left ankle. Data from 3D accelerometer and gyroscope were recorded. The data was annotated by hand using a camera reference.
For further information, please refer to the associated publication.
Publication
Please cite this publication when using the dataset:
- Martindale C., Hönig FT., Strohrmann C., Eskofier B.:
Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models
In: Sensors 17 (2017)
ISSN: 1424-8220
DOI: 10.3390/s17102328
URL: http://www.mdpi.com/1424-8220/17/10/2328
BibTeX: Download
The download link is currently not available, please contact the dataset coordinator. For further information, please visit the official MaD Lab website.
Dataset Coordinator
Description
The eGaIT database for the stride segmentation validation consists of data from 70 subjects. 25 elderly controls for template generation, 15 elderly controls and 15 patients with Parkinson’s disease (PD) for algorithm evaluation were recorded at the Movement Disorder Unit of the Department of Molecular Neurology, University Hospital Erlangen, Germany. Sensor data from 15 geriatric patients were acquired at the Geriatrics Centre, Waldkrankenhaus St. Marien, Erlangen, Germany. The eGaIT database for the gait parameter validation consists of data from 101 subjects (55 female and 46 male, age 82.1 ± 6.5 years, height 164.0 ± 10.0 cm, mean ± standard deviation (SD)) that performed straight walking tests. Two SHIMMER (Shimmer Research, Dublin, Ireland) sensors were used for data acquisition. Each sensor node was equipped with a triaxial accelerometer and a triaxial gyroscope and was attached to the lateral side of a sports shoe. Data were sampled with 102.4 Hz and were streamed via Bluetooth to a standard windows computer.
For further information, please refer to the associated publication.
Publication
Please cite these publications when using the dataset:
- Barth J., Oberndorfer C., Pasluosta CF., Schülein S., Gaßner H., Reinfelder S., Kugler P., Schuldhaus D., Winkler J., Klucken J., Eskofier B.:
Stride Segmentation During Free Walk Movements Using Multi-dimensional Subsequence Dynamic Time Warping on Inertial Sensor Data
In: Sensors 15 (2015), p. 6419-6440
ISSN: 1424-8220
DOI: 10.3390/s150306419
BibTeX: Download
Rampp, A., Barth, J., Schülein, S., Gaßmann, K. G., Klucken, J., & Eskofier, B. M. (2014). Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients. IEEE transactions on biomedical engineering, 62(4), 1089-1097.
Download Link
The download link is currently not available, please contact the dataset coordinator. For further information, please visit the official MaD Lab website.
Dataset Coordinator
Dr. Jens Barth, Alexander Rampp
Empatho-Kinaesthetic Sensor Technology (EmpkinS)
Sports Analytics