mmWave-based Fitness Activity Recognition Dataset

Description:

This mmWave Datasets are used for fitness activity identification. This dataset (FA Dataset) contains 14 common fitness daily activities. The data are captured by the mmWave radar TI-AWR1642. The dataset can be used by fellow researchers to reproduce the original work or to further explore other machine-learning problems in the domain of mmWave signals.

Format: .png format

Section 1: Device Configuration

• A commodity mmWave radar TI AWR1642, which integrates a 2 × 4 antenna array. The detailed information of it can be found at https://www.ti.com/product/AWR1642#:~:text=The%20AWR1642%20is%20an%20ideal,of%2076%20to%2081%20GHz.
• A TI DCA1000EVM data capture card is used to collect data from the mmWave device and send data to a laptop. The detailed information can be found at https://www.ti.com/tool/DCA1000EVM?keyMatch=DCA1000EVM.
• mmWave radar work at the frequency in the range of 77~81GHz. The sampling rate is fixed at 100 frames per second and each frame has 17 chirps.

Section 2: Data Format

We provide our mmWave data in heatmaps for this dataset. The data file is in the png format. The details are shown in the following:

• 14 activities are included in the FA Dataset.
• 2 participants are included in the FA Dataset.
• FA_d_p_i_u_j.png:
  • d represents the date to collect the fitness data.
  • p represents the environment to collect the fitness data.
  • i represents fitness activity type index
  • u represents user id
  • j represents sample index
• Example:
  • FA_20220101_lab_1_2_3 represents the 3rd data sample of user 2 of activity 1 collected in the lab

Section 3: Experimental Setup

• We place the mmWave device on a table with a height of 60cm.
• The participants are asked to perform fitness activity in front of a mmWave device with a distance of 2m.
• The data are collected at an lab with a size of (5.0m×3.0m).

Section 4: Data Description

• We develop a spatial-temporal heatmap to integrates multiple activity features, including the range of movement, velocity, and time duration of each activity repetition.

• We first derive the Doppler-range map of the users' activity by calculating Range-FFT and Doppler-FFT. Then, we generate the spatial-temporal heatmap by accumulating the velocity of every distance in every Doppler-range map together. Next, we normalize the derived velocity information and present the velocity-distance relationship in time dimension. In this way, we transfer the original instantaneous velocity-distance relationship to a more comprehensive spatial-temporal heatmap which describes the process of a whole activity.

• As shown in Figure attached, in each spatial-temporal heatmap, the horizontal axis represents the time duration of an activity repetition while the vertical axis represents the range of movement. The velocity is represented by color.

• We create 14 zip files to store the the dataset. There are 14 zip files starting with "FA", each contains repetitions from the same fitness activity.

14 common daily activities and their corresponding files 

File Name               Activity Type                             File Name              Activity Type

FA1                          Crunches                                    FA8                        Squats

FA2                          Elbow plank and reach               FA9                       Burpees

FA3                          Leg raise                                     FA10                     Chest squeezes

FA4                          Lunges                                        FA11                     High knees

FA5                          Mountain climber                        FA12                     Side leg raise

FA6                          Punches                                      FA13                     Side to side chops

FA7                          Push ups                                     FA14                     Turning kicks

Section 5: Raw Data and Data Processing Algorithms

• We also provide the mmWave raw data (.mat format) stored in the same zip file corresponding to the heatmap datasets. Each .mat file can store one set of activity repetitions (e.g., 4 repetations) from a same user.
  • For example: FA_d_p_i_u_j.mat:
    • d represents the data to collect the data.
    • p represents the environment to collect the data.
    • i represents the activity type index
    • u represents the user id
    • j represents the set index
• We plan to provide the data processing algorithms (heatmap_generation.py) to load the mmWave raw data and generate the corresponding heatmap data.

Section 6: Citations

If your paper is related to our works, please cite our papers as follows.

https://ieeexplore.ieee.org/document/9868878/

Xie, Yucheng, Ruizhe Jiang, Xiaonan Guo, Yan Wang, Jerry Cheng, and Yingying Chen. "mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave." In 2022 International Conference on Computer Communications and Networks (ICCCN), pp. 1-10. IEEE, 2022.

Bibtex:

@inproceedings{xie2022mmfit,

  title={mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave},

  author={Xie, Yucheng and Jiang, Ruizhe and Guo, Xiaonan and Wang, Yan and Cheng, Jerry and Chen, Yingying},

  booktitle={2022 International Conference on Computer Communications and Networks (ICCCN)},

  pages={1--10},

  year={2022},

  organization={IEEE}

}