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Bosch CNC Machining Dataset
Manufacturing processes have undergone tremendous technological progress in recent decades. To meet the agile philosophy in industry, data-driven algorithms need to handle growing complexity, particularly in Computer Numerical Control machining. To enhance the scalability of machine learning in real-world applications, this paper presents a benchmark dataset for process monitoring of brownfield milling machines based on acceleration data. The data is collected from a real-world production plant using a smart data collection system over a two-years period. In this work, the edge-to-cloud setup is presented followed by an extensive description of the different normal and abnormal processes. An analysis of the dataset highlights the challenges of machine learning in industry caused by the environmental and industrial factors. The new dataset is published with this paper and available at: https://github.com/boschresearch/CNC_Machining.
HARTH
The Human Activity Recognition Trondheim (HARTH) dataset is a professionally-annotated dataset containing 22 subjects wearing two 3-axial accelerometers for around 2 hours in a free-living setting. The sensors were attached to the right thigh and lower back. The professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.
HAR70+
The Human Activity Recognition 70+ (HAR70+) dataset is a professionally-annotated dataset containing 18 fit-to-frail older-adult subjects (70-95 years old) wearing two 3-axial accelerometers for around 40 minutes during a semi-structured free-living protocol. The sensors were attached to the right thigh and lower back.
Physical Therapy Exercises
This dataset contains wearable inertial and magnetic sensor data during the execution of physical therapy exercises. There are eight types of physical therapy exercises, each of which has three execution types (correct, fast, and low-amplitude). Each execution type of each type of exercise was performed multiple times by five subjects. The subjects wore five MTx sensor units manufactured by XSens. Each unit contains three tri-axial sensors: an accelerometer, a gyroscope, and a magnetometer, sampled at 25 Hz.
Accelerometer
Accelerometer data from vibrations of a cooler fan with weights on its blades. It can be used for predictions, classification and other tasks that require vibration analysis, especially in engines.
Accelerometer Gyro Mobile Phone
data collected on 2022, in King Saud University in riyadh for recognizing human activities using mobile phone IMU sensors (Accelerometer, and Gyroscope). these activity is calssified to standing(stop), or walking.
SoDA
Dataset of "Social Distancing Alert with Smartwatches"
MaskReminder
Dataset of "Mask Wearing Status Estimation with Smartwatches"
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