Human Activity Recognition

CSL-SHARE: Sensor-based Human Activity Recordings

The CSL-SHARE data corpus contains 22 activities performed by 20 study participants. The participants wore a knee bandage equipped with sensors totaling 19-channels.

The paper publishing this data corpus describes the structure and the usage of the dataset in detail (see under).

The data can be downloaded here.

Privacy Preservation and Data Security:

The participant's consent form stipulates that the use of the data is limited to non-commercial research purposes, and the data users guarantee not to attempt to identify the participating persons. Furthermore, the data users guarantee to pass on the data (or data derived from it) only to third parties who are bound by the same rules of use (for non-commercial research purposes, no identification attempts, restricted disclosure). Data users who violate the usage regulation mentioned above will bear the legal consequences themselves, where the dataset publisher takes no responsibility.

Citation Request:

This dataset is freely available for non-commercial academic research. We would appreciate referencing the below publications if you use this dataset or the implementation approaches related to it:

The CSL-SHARE dataset and the semi-automatic segmentation and annotation mechanism:

• CSL-SHARE: A Multimodal Wearable Sensor-based Human Activity Dataset, (Hui Liu, Yale Hartmann, Tanja Schultz), In Frontiers in Computer Science, Volume 3:90, 2021.

Statistical details and activity duration analysis of the CSL-SHARE dataset:

• How Long Are Various Types of Daily Activities? Statistical Analysis of a Multimodal Wearable Sensor-Based Human Activity Dataset, (Hui Liu, Tanja Schultz), In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, pages 684-692, 2022.

   

Human Activity Recognition Research on the CSL-SHARE dataset:

Comprehensive research on the CSL-SHARE dataset:

• Biosignal processing and activity modeling for multimodal human activity recognition, (Hui Liu). PhD thesis, University of Bremen, 2021. Supervisors: Tanja Schultz, Hugo Gamboa.

More detailed information about the devices, the sensors, the bandage, and the implemented software applied in the CSL-SHARE dataset acquisition:

• ASK: A Framework for Data Acquisition and Activity Recognition, (Hui Liu, Tanja Schultz), In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS, pages 262-268, 2018.
• A Wearable Real-time Human Activity Recognition System using Biosensors Integrated into a Knee Bandage, (Hui Liu, Tanja Schultz), In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - BIODEVICES, pages 47-55, 2019 (Best Student Paper).

HAR research pipeline:

• A Practical Wearable Sensor-Based Human Activity Recognition Research Pipeline, (Hui Liu, Yale Hartmann, Tanja Schultz), In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, pages 851-860, 2022.

Feature extraction examples from the CSL-SHARE dataset using the open-source Time Series Feature Extraction Library (TSFEL):

• TSFEL: Time Series Feature Extraction Library. (Marília Barandas, Duarte Folgado, Letícia Fernandes, Sara Santos, Mariana Abreu, Patrícia Bota, Hui Liu, Tanja Schultz, Hugo Gamboa), In SoftwareX, Elsevier, Volume 11, 2020.

High-level feature extraction from the CSL-SHARE dataset and other datasets:

• High-Level Features for Human Activity Recognition and Modeling, (Yale Hartmann, Hui Liu, Tanja Schultz), In Biomedical Engineering Systems and Technologies (Ana Cecília A. Roque, Denis Gracanin, Ronny Lorenz, Athanasios Tsanas, Nathalie Bier, Ana Fred, Hugo Gamboa, eds.), Springer Nature Switzerland, 2023.
• Interpretable High-Level Features for Human Activity Recognition, (Yale Hartmann, Hui Liu, Steffen Lahrberg, Tanja Schultz), In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, pages 40-49, 2022 (Best Student Paper Award Nomination).

Feature dimensionality study on CSL-SHARE and other datasets:

• Feature Space Reduction for Human Activity Recognition based on Multi-channel Biosignals, (Yale Hartmann, Hui Liu, Tanja Schultz), In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS, pages 215-222, 2021.
• Feature Space Reduction for Multimodal Human Activity Recognition, (Yale Hartmann, Hui Liu, Tanja Schultz), In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS, pages 135-140, 2020.

Novel human activity modeling method of Motion Units on the CSL-SHARE dataset:

• Motion Units: Generalized Sequence Modeling of Human Activities for Sensor-Based Activity Recognition, (Hui Liu, Yale Hartmann, Tanja Schultz), In 29th European Signal Processing Conference (EUSIPCO 2021), 2021.

Real-Time HAR Systems::

• Interactive and Interpretable Online Human Activity Recognition, (Yale Hartmann, Hui Liu, Tanja Schultz), In PERCOM 2022 - IEEE International Conference on Pervasive Computing and Communications, pages 109-111, 2022.
• On a Real Real-Time Wearable Human Activity Recognition System. (Hui Liu, Tingting Xue, Tanja Schultz), .In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - WHC, pages 711-720, 2023.

Algorithms for biosignal processing and automatic segmentation:

• TSSEARCH: Time Series Subsequence Search Library, (Duarte Folgado, Barandas Marília Fernandes, Margarida Antunes, Maria Lua Nunes, Hui Liu, Yale Hartmann, Tanja Schultz, Hugo Gamboa), In SoftwareX, volume 18, 2022.
• Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix: Focus on Automatic Segmentation, (João Rodrigues, Hui Liu, Duarte Folgado, David Belo, Tanja Schultz, Hugo Gamboa), In Biosensors, volume 12, 2022.

General Studies on Human Activity Recognition:

• Robust human locomotion and localization activity recognition over multisensory. (Danyal Khan, Mohammed Alonazi, Maha Abdelhaq, Naif Al Mudawi, Asaad Algarni, Ahmad Jalal, Hui Liu), In Frontiers in Physiology, Frontiers Media SA, volume 15, 2024.
• Sensor-Based Human Activity and Behavior Research: Where Advanced Sensing and Recognition Technologies Meet, (Hui Liu, Hugo Gamboa, Tanja Schultz), In Sensors, volume 23, 2023.
• (Book) Sensors for Human Activity Recognition. (Hui Liu, Hugo Gamboa, Tanja Schultz). MDPI, 2023.
• Hidden Markov Model and Its Application in Human Activity Recognition and Fall Detection: A Review, (Tingting Xue, Hui Liu), In Communications, Signal Processing, and Systems (Qilian Liang, Wei Wang, Xin Liu, Zhenyu Na, Baoju Zhang, eds.), Springer Singapore, 2022.