From 5df65eee4be017fab0923edc5e97baae2cbe5e27 Mon Sep 17 00:00:00 2001 From: Omar Santos Date: Fri, 8 Sep 2023 11:48:14 -0400 Subject: [PATCH] Update README.md --- ai_research/ethics_privacy/README.md | 68 ++++++++++++++-------------- 1 file changed, 34 insertions(+), 34 deletions(-) diff --git a/ai_research/ethics_privacy/README.md b/ai_research/ethics_privacy/README.md index 66e7b0a..f8310e7 100644 --- a/ai_research/ethics_privacy/README.md +++ b/ai_research/ethics_privacy/README.md @@ -3,50 +3,50 @@ ### 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: