# Processed PAMAP2 dataset
This dataset is based on the [PAMAP2 Dataset for Physical Activity Monitoring](https://archive.ics.uci.edu/ml/datasets/PAMAP2+Physical+Activity+Monitoring).

Compared to v0.2.0, this preprocessed dataset contains fewer activities. It only includes: lying, sitting, standing, walking, cycling, vaccuum_cleaning and ironing

The data is processed with the code from [this script]https://github.com/NLeSC/mcfly-tutorial/blob/master/utils/tutorial_pamap2.py), with the following function call:

```python
columns_to_use = ['hand_acc_16g_x', 'hand_acc_16g_y', 'hand_acc_16g_z',
'ankle_acc_16g_x', 'ankle_acc_16g_y', 'ankle_acc_16g_z',
'chest_acc_16g_x', 'chest_acc_16g_y', 'chest_acc_16g_z']
exclude_activities = [5, 7, 9, 10, 11, 12, 13, 18, 19, 20, 24, 0]
outputpath = tutorial_pamap2.fetch_and_preprocess(directory_to_extract_to,columns_to_use,
exclude_activities=exclude_activities,
val_test_size=(100, 1000))

```

## References
A. Reiss and D. Stricker. Introducing a New Benchmarked Dataset for Activity Monitoring. The 16th IEEE International Symposium on Wearable Computers (ISWC), 2012.

Part of MONSTER: https://arxiv.org/abs/2502.15122.

PAMAP2

Category HAR

Num. Examples 38,856

Num. Channels 52

Length 100

Sampling Freq. 100 Hz

Num. Classes 12

License Other

Citations [1]

PAMAP2 is a collection of data obtained from three Inertial Measurement Units (IMUs) placed on the wrist of the dominant arm, chest, and ankle, as well as 1 ECG heart rate [1]. The data was recorded at a frequency of 100Hz. The dataset includes annotated information about human… See the full description on the dataset page: https://huggingface.co/datasets/monster-monash/PAMAP2.

Processed PAMAP2 dataset This dataset is based on the PAMAP2 Dataset for Physical Activity Monitoring. The data is processed with the code from this script, with the following function call: python columns_to_use = ['hand_acc_16g_x', 'hand_acc_16g_y', 'hand_acc_16g_z', 'ankle_acc_16g_x', 'ankle_acc_16g_y', 'ankle_acc_16g_z', 'chest_acc_16g_x', 'chest_acc_16g_y', 'chest_acc_16g_z'] exclude_activities = [9, 10, 11, 18, 19, 20, 0] outputpath = tutorial_pamap2.fetch_and_preprocess(directory_to_extract_to, columns_to_use, exclude_activities=exclude_activities, val_test_size=(100, 1000)) ## References A. Reiss and D. Stricker. Introducing a New Benchmarked Dataset for Activity Monitoring. The 16th IEEE International Symposium on Wearable Computers (ISWC), 2012.

High-frequency wearable sensor data from 9 subjects performing 18 different daily and sports activities, recorded with three 100 Hz IMUs and a heart rate monitor.[web:125][web:128][web:138][web:142]

Dataset

This dataset was created by Joy Dhar

Contents

gem-analysis-pamap2

PAMAP2 physical activity monitoring dataset

  数据集信息

来源路径: datasets/pamap2 数据大小: 1.6 GB 用途: 健身动作识别模型训练

  使用方法

from huggingface_hub import snapshot_download

下载数据集

snapshot_download( repo_id="yonful/gem-analysis-pamap2", repo_type="dataset", local_dir="./datasets/pamap2" )

或使用项目中的下载脚本: python scripts/prepare_data.py --dataset pamap2

  许可证

请参考原始数据源的许可证要求。

Dataset

This dataset was created by Phạm Tiến Sơn

Contents

Dataset

This dataset was created by Sufi Inam Ul Hassan

Released under Apache 2.0

Contents

The data analyses three public datasets for Human Activity Recognition (HAR), with the original data directly downloadable from the Internet: Daphnet Gait Dataset (Freezing of Gait): https://archive.ics.uci.edu/dataset/245/daphnet+freezing+of+gait Opportunity Dataset: https://archive.ics.uci.edu/dataset/226/opportunity+activity+recognition PAMAP2 Dataset: https://archive.ics.uci.edu/dataset/231/pamap2+physical+activity+monitoring However, the data available there cannot be used directly and requires a series of data segmentation and preprocessing. What I have released here are the aforementioned three public datasets after undergoing a series of preprocessing steps. The datasets have been preprocessed with Python, including sliding window cropping, removal of NaN rows, removal of time(ms), normalization, etc. They have been divided into Test, Train, and Validation datasets using mainstream methods and finally saved with Numpy for the convenience of users for quick deployment. Daphnet Gait Dataset(Frozen of Gait): https://archive.ics.uci.edu/dataset/245/daphnet+freezing+of+gait This dataset is a binary classification dataset consisting of recordings from 10 participants diagnosed with Parkinson’s disease (PD). Dataset activities correspond to recognizing whether or not gait freeze occurs based on wearable acceleration sensors. The dataset was recorded in a lab environment with the subjects were instructed to carry out activities with a high likelihood of inducing freezing of gait, which is a common motor complication in PD. Opportunity Dataset: https://archive.ics.uci.edu/dataset/226/opportunity+activity+recognition This dataset contains recordings from various wearables and environment sensors from four participants who carry out common kitchen activities, such as Open/Close Door, Dishwasher, and Fridge, via Inertial Measurement Units (IMUs) at 30Hz. Each participant is recorded in five different runs. PAMAP2 Dataset: https://archive.ics.uci.edu/dataset/231/pamap2+physical+activity+monitoring The physical activity monitoring dataset is similar to the opportunity dataset, consisting of nine participants performing 12 kinds of daily physical activities, such as cycling, walking, sitting. The sensors used in the inertial measurement units (IMUs) include accelerometers, gyroscopes, magnetometers, temperature, and heart rate. The data is accessible via CC BY license.

Dataset

This dataset was created by ermit w

Contents

The authors are: Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, Marinka ZitnikThis PAM dataset is a subset of PAMAP2 dataset (public accessable). - Paper: Graph-Guided Network For Irregularly Sampled Multivariate Time Series, (Accepted by ICLR 2022) - Paper link: https://openreview.net/pdf?id=Kwm8I7dU-l5- Github repo: https://github.com/mims-harvard/Raindrop- Project website: https://zitniklab.hms.harvard.edu/projects/Raindrop/

Dataset

This dataset was created by Mayank Gupta

Released under Apache 2.0

Contents

Datasets available at UCI Machine Learning Repository and other repositories. List of datasets used in the experiment with their sources. ForestCover dataset @ https://archive.ics.uci.edu/ml/datasets/Covertype KDD Cup99 dataset @ https://archive.ics.uci.edu/ml/datasets/KDD+Cup+1999+Data PAMAP dataset @ https://archive.ics.uci.edu/ml/datasets/PAMAP2+Physical+Activity+Monitoring Powersupply @ http://www.cse.fau.edu/~xqzhu/stream.html SEA @ http://www.liaad.up.pt/kdus/products/datasets-for-concept-drift Syn002 & Syn003 (generated) @ http://moa.cms.waikato.ac.nz/details/classification/streams/ MNIST @ https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html News20 @ https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html

Dataset

This dataset was created by Jishnu2001_M

Released under Apache 2.0

Contents

Dataset

This dataset was created by Ez

Contents

Dataset

This dataset was created by Sohom Ghosal

Contents

A curated list of preprocessed & ready to use under a minute Human Activity Recognition datasets.

All the datasets are preprocessed in HDF5 format, created using the h5py python library. Scripts used for data preprocessing are provided as well (Load.ipynb and load_jordao.py)

Each HDF5 file contains at least the keys:

• x a single array of size [sample count, temporal length, sensor channel count], contains the actual sensor data. Metadata contains the names of individual sensor channel count. All samples are zero-padded for constant length in the file, original lengths before padding available under the meta keys.
• y a single array of size [sample count] with integer values for target classes (zero-based). Metadata contains the names of the target classes.
• meta contain various metadata, depends on the dataset (original length before padding, subject no., trial no., etc.)

Usage example

import h5py

with h5py.File(f'data/waveglove_multi.h5', 'r') as h5f:
   x = h5f['x']
   y = h5f['y']['class']
   print(f'WaveGlove-multi: {x.shape[0]} samples')
   print(f'Sensor channels: {h5f["x"].attrs["channels"]}')
   print(f'Target classes: {h5f["y"].attrs["labels"]}')
   first_sample = x[0]
# Output:   
# WaveGlove-multi: 10044 samples
# Sensor channels: ['acc1-x' 'acc1-y' 'acc1-z' 'gyro1-x' 'gyro1-y' 'gyro1-z' 'acc2-x'
# 'acc2-y' 'acc2-z' 'gyro2-x' 'gyro2-y' 'gyro2-z' 'acc3-x' 'acc3-y'
# 'acc3-z' 'gyro3-x' 'gyro3-y' 'gyro3-z' 'acc4-x' 'acc4-y' 'acc4-z'
# 'gyro4-x' 'gyro4-y' 'gyro4-z' 'acc5-x' 'acc5-y' 'acc5-z' 'gyro5-x'
# 'gyro5-y' 'gyro5-z']
# Target classes: ['null' 'hand swipe left' 'hand swipe right' 'pinch in' 'pinch out'
# 'thumb double tap' 'grab' 'ungrab' 'page flip' 'peace' 'metal']

Current list of datasets:

• WaveGlove-single (waveglove_single.h5)
• WaveGlove-multi (waveglove_multi.h5)
• uWave (uwave.h5)
• OPPORTUNITY (opportunity.h5)
• PAMAP2 (pamap2.h5)
• SKODA (skoda.h5)
• MHEALTH (non overlapping windows) (mhealth.h5)
• Six datasets with all four predefined train/test folds
  as preprocessed by Jordao et al. originally in WearableSensorData
  (FNOW, LOSO, LOTO and SNOW prefixed .h5 files)

Dataset

This dataset was created by Panchadip

Released under Apache 2.0

Contents

Dataset

This dataset was created by Joy Dhar

Contents

Dataset

This dataset was created by Jiashuo Wang

Released under Apache 2.0

Contents