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Analysis of ‘Classify gestures by reading muscle activity.’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kyr7plus/emg-4 on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

My friends and I are creating an open source prosthetic control system which would enable prosthetic devices to have multiple degrees of freedom. https://github.com/cyber-punk-me

VIDEO

The system is built of several components. It connects a muscle activity (EMG, Electromyography) sensor to a user Android/Android Things App. The app collects data, then a server builds a Tensorflow model specifically for this user. After that the model can be downloaded and executed on the device to control motors or other appendages.

This dataset can be used to map user residual muscle gestures to certain actions of a prosthetic such as open/close hand or rotate wrist.

For a reference please watch a video on this topic : Living with a mind-controlled robot arm

Content

Four classes of motion were written from MYO armband with the help of our app https://github.com/cyber-punk-me/nukleos. The MYO armband has 8 sensors placed on skin surface, each measures electrical activity produced by muscles beneath.

Each dataset line has 8 consecutive readings of all 8 sensors. so 64 columns of EMG data. The last column is a resulting gesture that was made while recording the data (classes 0-3) So each line has the following structure:

[8sensors][8sensors][8sensors][8sensors][8sensors][8sensors][8sensors][8sensors][GESTURE_CLASS]

Data was recorded at 200 Hz, which means that each line is 8*(1/200) seconds = 40ms of record time.

A classifier given 64 numbers would predict a gesture class (0-3). Gesture classes were : rock - 0, scissors - 1, paper - 2, ok - 3. Rock, paper, scissors gestures are like in the game with the same name, and OK sign is index finger touching the thumb and the rest of the fingers spread. Gestures were selected pretty much randomly.

Each gesture was recorded 6 times for 20 seconds. Each time recording started with the gesture being already prepared and held. Recording stopped while the gesture was still being held. In total there is 120 seconds of each gesture being held in fixed position. All of them recorded from the same right forearm in a short timespan. Every recording of a certain gesture class was concatenated into a .csv file with a corresponding name (0-3).

Inspiration

Be one of the real cyber punks inventing electronic appendages. Let's help people who really need it. There's a lot of work and cool stuff ahead =)

--- Original source retains full ownership of the source dataset ---

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Description

Road development in the Congo Basin forest is continuously monitored from 2019 onwards in high spatial and temporal detail. A deep learning method is applied to 10 m scale Sentinel-1 and Sentinel-2 imagery for automated road detections on a monthly basis. This version presents 5 years of road development (46,311 km) from 2019-2023.

The data is composed of line features distributed in .shp and .geojson formats. The following attributes are stored for the line features:

• NetworkID: A unique ID for each connected road network.
• SegLenM: The length of the road segment (in meters).
• NetLenM: The length of the connected road network (in meters).
• Month: The road segment opening month.
• Year: The road segment opening year.
• MonthNum: The road segment opening month, depicted as a continuing count since the start of monitoring (e.g. 13 = January 2020). This attribute can be used for smooth and continuous temporal analyses or visualizations.

Additional information

• More information about the forest road mapping project can be found at: https://wur.eu/forest-roads
• Continuously updated road maps can be interactively viewed at: https://nrtwur.users.earthengine.app/view/forest-roads
• The dataset can be accessed in Google Earth Engine at: ee.FeatureCollection('projects/wurnrt-loggingroads/assets/distribution/forestroads_afr_2019-01_2023-12')
• The scientific paper (Slagter et al., 2024) describing the methods to produce this dataset can be found at: https://doi.org/10.1016/j.rse.2024.114380" target="_blank" rel="noopener">https://doi.org/10.1016/j.rse.2024.114380

Citation

Please cite the following when referring to this dataset:

Demo

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1086574%2Feee5467d2935e8c3aaa9af5d58e6bbf4%2Fxarpai-opacity.gif?generation=1681225192483507&alt=media" alt="">

Demo showing what happens after a user submits three images

Summary

The XarpAi Lung Opacity Detector is a proof-of-concept for a free, open-source desktop app that uses artificial intelligence to detect opacities on chest x-rays.

Opacities are characteristic signs of lung diseases like TB and Pneumonia. This app analyses chest x-rays and draws bounding boxes around opacities. Radiologists can then review these areas of interest and use their clinical judgement to make a final diagnosis.

There’s a shortage of radiologists in poor countries. In 2015, Rwanda had 11 radiologists to serve its population of 12 million people. Liberia, with a population of four million, had two practising radiologists. This app provides high volume diagnosis support. It can help overwhelmed radiologists triage x-rays and speed up their workflows.

The predictions are made by a Pytorch Faster R-CNN model. The model was fine tuned on data from four chest x-ray datasets:

• The TBX11K Tuberculosis dataset
• The Kaggle RSNA Pneumonia Detection Challenge
• The Kaggle VinBigData Chest X-ray Abnormalities Detection competition
• The Kaggle SIIM-FISABIO-RSNA COVID-19 Detection competition

Although the app displays opacity bounding boxes, the model was also trained to detect lungs i.e. it predicts a bounding box that surrounds both lungs. If the model fails to detect the lungs then the app outputs an error message.

The model was validated on an 80/20 train test split. It was also tested on three out of sample datasets:

• The Shenzhen and Montgomery Tuberculosis datasets
• The DA and DB Tuberculosis datasets
• Child Chest X-Ray Pneumonia dataset

These out of sample datasets don’t have annotated opacity bounding boxes. Therefore, accuracy was used as a rough metric - if the target was positive (e.g. positive for TB) and the model predicted a bounding box, the model was deemed to have made a correct prediction. This validation approach is not rigorous. But it’s a quick and simple way to get a feel for the model’s capability.

Results on the 20% validation data: - map@0.5: 0.776 accuracy: 0.91

Accuracy on out of sample datasets: - Shenzhen and Montgomery TB datasets: 0.84 - DA and DB TB datasets: 0.85 - Child Chest X-Ray Pneumonia dataset: 0.83

Chest x-rays can be difficult for humans to read. One study (TBX11k paper) found that radiologists have a 68.7% accuracy when diagnosing TB on chest x-rays. Using that number for context, the model’s test results look very good. The good performance on the child pneumonia data is surprising because the training data didn’t include a large number of child x-rays.

These results show that this opacity detection app could be helpful when diagnosing lung diseases like TB and Pneumonia.

The complete project folder, including the trained models, is stored in this Kaggle dataset.

For a full project description please refer to the GitHub repo: https://github.com/vbookshelf/XarpAi-Lung-Opacity-Detector

For info on model training and validation, please refer to the model card. I've included a summary of the datasets, confusion matrices and classification reports. https://github.com/vbookshelf/XarpAi-Lung-Opacity-Detector/blob/main/xarpai-lung-opacity-detector-v1.0/static/assets/Model-Card-and-App-Info-v1.0.pdf

How to run this app

I suggest that you download the project folder from Kaggle instead of from the GitHub repo. This is because the project folder on Kaggle includes the trained model. The project folder in the GitHub repo does not include the trained model because GitHub does not allow files larger than 25MB to be uploaded.

System Requirements

You'll need about 1.5GB of free disk space. Other than that there are no special system requirements. This app will run on a CPU. I have an old 2014 Macbook Pro laptop with 8GB of RAM. This app runs on it without any issues.

Overview

This is a standard flask app. The steps to set up and run the app are the same for both Mac and Windows.

1. Download the project folder.
2. Use the command line to pip install the requirements listed in the requirements.txt file. (It’s located inside the project folder.)
3. Run the app.py file from the command line.
4. Copy the url that gets printed in the console.
5. Paste that url into your chrome browser and press Enter. The app will open in the browser.

This app is based on Flask and Pytorch, both of which are pure python. If you encounter any errors during installation you should be able to solve them quite easily. You won’t have to deal with the package dependency issues that happen when using Tensorflow.

Detailed setup instructions

The instructions below are for...

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Reasons for Needing Support Users often experience common problems such as:

Issues accessing accounts

Delayed transactions or pending orders

Staking glitches

2FA setup issues

Locked Crypto.com Visa Cards

Having quick access to the Crypto.com helpline number is valuable in these situations.

Should You Call 1-888-416-9087 for Crypto.com Support? While 1-888-416-9087 is listed by some online sources, Crypto.com’s official support does not confirm any phone line. Their preferred channels are the in-app messaging system and Help Center tickets.

Steps to Safely Contact Crypto.com

Launch the Crypto.com app → Tap “Help” → Start a live chat.

Visit the Help Center → Submit a detailed support ticket.

These methods connect you with trained agents from the actual Crypto.com team.

Support Efficiency Tips

Be as detailed as possible in your explanation.

Don’t ever reveal your private keys or recovery phrases.

Only engage with support from the official app or verified web links.

Final Thoughts If you come across a Crypto.com support number (1-888-416-9087) online, treat it with caution. The safest and fastest assistance will always come from Crypto.com’s verified communication tools.

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The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census Blocks are statistical areas bounded on all sides by visible features, such as streets, roads, streams, and railroad tracks, and/or by nonvisible boundaries such as city, town, township, and county limits, and short line-of-sight extensions of streets and roads. Census blocks are relatively small in area; for example, a block in a city bounded by streets. However, census blocks in remote areas are often large and irregular and may even be many square miles in area. A common misunderstanding is that data users think census blocks are used geographically to build all other census geographic areas, rather all other census geographic areas are updated and then used as the primary constraints, along with roads and water features, to delineate the tabulation blocks. As a result, all 2010 Census blocks nest within every other 2010 Census geographic area, so that Census Bureau statistical data can be tabulated at the block level and aggregated up to the appropriate geographic areas. Census blocks cover all territory in the United States, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands). Blocks are the smallest geographic areas for which the Census Bureau publishes data from the decennial census. A block may consist of one or more faces.