h4cker/ai_research/ethics_privacy

AI Ethics and Privacy Resources

Databases for Human Activity Recognition:

1. MobiAct
   A dataset optimized for detecting activities such as falls, walking, and jogging. It is primarily used in creating apps that use smartphone sensors to detect falls, particularly in elderly individuals.

2. NHANES Dataset
   Although not exclusively designed for HAR, the NHANES dataset is a rich source of health and nutritional data, which could potentially be utilized to garner insights into human activities and health conditions.

3. UniMiB SHAR
   This repository houses data concerning human activities collected from smartphone accelerometer sensors. It serves as a valuable resource for developing machine learning models capable of recognizing various activities.

4. UCI Human Activity Recognition Using Smartphones Dataset
   This dataset comprises data from smartphone accelerometers and gyroscopes, capturing activities such as walking, sitting, and standing performed by 30 subjects. It is a popular choice for HAR research projects.

5. ISDM (Wireless Sensor Data Mining)
   Although not a database per se, this GitHub topic connects you to various resources and datasets pertaining to wireless sensor data mining, an essential aspect in HAR research.

6. HHAR (Heterogeneity Human Activity Recognition)
   HHAR stands out with its data collected from a range of devices, portraying various human activities. It is particularly beneficial for constructing models adaptable to different data sources.

7. PAMAP2 Physical Activity Monitoring
   Featuring data from wearable sensors monitoring individuals performing diverse physical activities, PAMAP2 is a vital tool for developing predictive HAR models.

8. Daphnet Freezing of Gait
   Focused on Parkinson's patients' gait freezing, this dataset, comprising data from wearable sensors, plays a crucial role in HAR healthcare applications.

9. Actitracker
   Developed to recognize various physical activities through smartphone sensors, Actitracker houses data on activities such as walking and jogging.

10. Daily and Sports Activities
    This dataset contains data on a range of daily and sports activities recorded through wearable sensors, making it a rich resource for HAR research, especially in distinguishing between different physical activities.

11. Smartphone Dataset for Human Activity Recognition (HAR) in Ambient Assisted Living (AAL)
    This dataset focuses on aiding the elderly or disabled, using smartphone sensors to identify their activities, hence fostering safer and more comfortable living environments.

12. Opportunity Activity Recognition
    This dataset is notable for its emphasis on context recognition, using sensor data from various sources to identify complex activities and gestures, thereby advancing research in ambient intelligence.

13. CASAS
    CASAS, a collection of datasets centered on smart home environments, facilitates the creation of algorithms capable of recognizing home-based activities through sensor data.

14. MSR Daily Activity 3D
    This dataset distinguishes itself with its inclusion of depth maps alongside skeletal data for activity recognition, aiding in the development of models capable of identifying activities from 3D data.

15. REALDISP Activity Recognition Dataset
    REALDISP incorporates data on various activities captured through wearable sensors, with a focus on realistic data disposition, which is vital for creating robust HAR models.

Tools & Methods for Data Collection, Cleaning, and Analysis:

• Data Collection:
  • APIs and SDKs
  • Wireless transmission

Data Cleaning:

3. Pandas:

   • Example: Cleaning a dataset with missing values using Pandas before training a machine learning model.
   • Relevant Link: Pandas Documentation
   • Usage in HAR and AI: Pandas can be used to structure and clean sensor data, making it suitable for training AI models capable of recognizing complex patterns in human activity data.

4. Sci-kit learn:

   • Example: Using Sci-kit learn for feature selection and removing irrelevant features from a dataset.
   • Relevant Link: Sci-kit learn Documentation
   • Usage in HAR and AI: Sci-kit learn offers various tools for data preprocessing, which is a vital step in preparing data for AI algorithms, enhancing the performance of the models in HAR applications.

Data Analysis:

5. TensorFlow:

   • Example: Developing a deep learning model using TensorFlow to classify different activities based on sensor data.
   • Relevant Link: TensorFlow Documentation
   • Usage in HAR and AI: TensorFlow provides a comprehensive platform for developing and training AI models capable of analyzing and recognizing patterns in human activity data.

6. Keras:

   • Example: Using Keras to create a convolutional neural network (CNN) for image recognition, an essential task in AI.
   • Relevant Link: Keras Documentation
   • Usage in HAR and AI: Keras simplifies the process of building and optimizing neural networks, a crucial component in AI, to analyze human activity data more effectively and make predictions.

Visualization and Further Analysis:

7. Matplotlib:

   • Example: Using Matplotlib to visualize the distribution of different activities within a dataset.
   • Relevant Link: Matplotlib Documentation
   • Usage in HAR and AI: Visualization of data is essential in AI to understand underlying patterns and trends in data, aiding in the better development and tuning of models for HAR.

8. Seaborn:

   • Example: Creating a heatmap using Seaborn to visualize the correlation between different features in a dataset.
   • Relevant Link: Seaborn Documentation
   • Usage in HAR and AI: Seaborn can enhance data visualization in AI, assisting in identifying relationships and patterns in data which can influence the development and performance of HAR models.