gyroscope

This dataset contains human motion data collected using inertial measurement units (IMUs), including accelerometer and gyroscope readings, from participants performing specific activities. The data was gathered under controlled conditions with verbal informed consent and includes diverse motion patterns that can be used for research in human activity recognition, wearable sensor applications, and machine learning algorithm development. Each sample is labeled and processed to ensure consistency, with raw and augmented data available for use. 

This dataset contains electrocardiography, electromyography, accelerometer, gyroscope and magnetometer signals that were measured in different scenarios using wearable equipment on 13 subjects:

 - Weight movement in a horizontal position at an angle of approximately 45°.

- Vertical movement of the weights from the table to the floor and back.

- Moving the weights vertically from the table to the head and back.

- Rotational movement of the wrist while holding the weights with the arm extended, see Figure ~\ref{fig2}.

The widespread adoption of smartphones has transformed how users engage with digital content, particularly for reading. Unlike desktop systems, which rely on peripherals like a mouse and keyboard, reading on smartphones involves direct interaction with the touchscreen. Actions such as pinch-to-zoom, tapping, scrolling, changing screen orientation, and taking screenshots are key components of smartphone reading behavior. While studies on desktop peripherals have provided insights into implicit feedback from user interactions, similar research for smartphones remains underexplored.

Industrial cyber-physical systems (ICPS), which is the backbone of Industry 4.0, are the result of adapting emerging information communication technologies (ICT) to the industrial control systems (ICS). ICPS utilize autonomous robotic arms to accomplish manufacturing tasks. These arms follow a certain predetermined trajectory during the task. 

In this dataset, we present four files generated from a setup that contains two Universal Robot UR3e collaborative robotic arms:

This dataset is in support of my Research paper 'Design of 6-DoF Combat Quadcopter'.

Preprint:   

The system is basic, on existing designs.It is very simple for any graduate,degree holder.

Related Claim : Novel ß Non-Linear Theory

Image Source: https://www.parrot.com/us/use-cases/military-and-defense

The dataset comprises motion sensor data of 19 daily and sports activities each performed by 8 subjects in their own style for 5 minutes. Five Xsens MTx units are used on the torso, arms, and legs.

The data format is described as follows:

Event: {‘acc’: array([[x_axis], [y_axis], [z_axis], ‘gyr’,array([x_axis], [y_axis], [z_axis], ‘label’: No ]

No =1 means acceleration.

No =2 means normal driving.

No =3 means collision.

No =4 means left turn.

No =5 means right turn.

The dataset was analyzed and disclosed in the paper "Vehicle Driving Behavior Recognition Based on Multi-View Convolutional Neural Network (MV-CNN) with Joint Data Augmentation" for the first time.