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### Databases for Human Activity Recognition:
1. **MobiAct**
- URL: [MobiAct GitHub](https://github.com/MatheLi/Fall_Detection_App_AI/blob/master/posts/The_dataset.md)
2. **NHANES Dataset**
- URL: [NHANES](http://www.sal.disco.unimib.it/technologies/unimib-shar/)
3. **UniMiB SHAR**
- URL: [UniMiB SHAR](https://wwwn.cdc.gov/nchs/nhanes/)
4. **UCI Human Activity Recognition Using Smartphones Dataset**
- URL: [UCI HAR Dataset](https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones)
1. **[MobiAct](https://github.com/MatheLi/Fall_Detection_App_AI/blob/master/posts/The_dataset.md)**
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.
5. **ISDM (Wireless Sensor Data Mining)**
- URL: [ISDM on GitHub](https://github.com/topics/wireless-sensor-data-mining)
2. **[NHANES Dataset](http://www.sal.disco.unimib.it/technologies/unimib-shar/)**
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.
6. **HHAR (Heterogeneity Human Activity Recognition)**
- URL: [HHAR on GitHub](https://github.com/Limmen/Distributed_ML)
3. **[UniMiB SHAR](https://wwwn.cdc.gov/nchs/nhanes/)**
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.
7. **PAMAP2 Physical Activity Monitoring**
- URL: [PAMAP2 on UCI](https://archive.ics.uci.edu/dataset/231/pamap2+physical+activity+monitoring)
4. **[UCI Human Activity Recognition Using Smartphones Dataset](https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones)**
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.
8. **Daphnet Freezing of Gait**
- URL: [Daphnet on UCI](https://archive.ics.uci.edu/dataset/245/daphnet+freezing+of+gait)
5. **[ISDM (Wireless Sensor Data Mining)](https://github.com/topics/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.
9. **Actitracker**
- URL: [Actitracker on GitHub](https://github.com/gomahajan/har-actitracker)
6. **[HHAR (Heterogeneity Human Activity Recognition)](https://github.com/Limmen/Distributed_ML)**
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.
10. **Daily and Sports Activities**
- URL: [Daily and Sports Activities on UCI](https://archive.ics.uci.edu/dataset/256/daily+and+sports+activities)
7. **[PAMAP2 Physical Activity Monitoring](https://archive.ics.uci.edu/dataset/231/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.
11. **Smartphone Dataset for Human Activity Recognition (HAR) in Ambient Assisted Living (AAL)**
- URL: [HAR in AAL on UCI](https://archive.ics.uci.edu/dataset/364/smartphone+dataset+for+human+activity+recognition+har+in+ambient+assisted+living+aal)
8. **[Daphnet Freezing of Gait](https://archive.ics.uci.edu/dataset/245/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.
12. **Opportunity Activity Recognition**
- URL: [Opportunity on UCI](https://archive.ics.uci.edu/dataset/226/opportunity+activity+recognition)
9. **[Actitracker](https://github.com/gomahajan/har-actitracker)**
Developed to recognize various physical activities through smartphone sensors, Actitracker houses data on activities such as walking and jogging.
13. **CASAS**
- URL: [CASAS](https://casas.wsu.edu/datasets/)
14. **MSR Daily Activity 3D**
- URL: [MSR Daily Activity 3D](https://wangjiangb.github.io/my_data.html)
10. **[Daily and Sports Activities](https://archive.ics.uci.edu/dataset/256/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.
15. **REALDISP Activity Recognition Dataset**
- URL: [REALDISP](https://mldta.com/dataset/realdisp-activity-recognition-dataset/)
11. **[Smartphone Dataset for Human Activity Recognition (HAR) in Ambient Assisted Living (AAL)](https://archive.ics.uci.edu/dataset/364/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](https://archive.ics.uci.edu/dataset/226/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](https://casas.wsu.edu/datasets/)**
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](https://wangjiangb.github.io/my_data.html)**
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](https://mldta.com/dataset/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: