accelerometer
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}.
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20 Views
A dataset comprising a total of 21 individuals has been meticulously compiled, with 9 individuals identified as exhibiting Major Depressive Disorder (MDD) based on the outcomes derived from the PHQ-9 Questionnaire. The remaining 12 individuals in the dataset are classified as non-MDD.
The dataset encompasses diverse sensor data, including temperature measurements, SpO2 readings, pulse rates, and accelerometer data. It is important to note that all data points were collected within a controlled environment, ensuring reliability and consistency throughout the dataset.
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1874 Views
Seventeen male participants (age: 22.8 ± 3.0 years old, height: 1.76 ± 6.2 m, weight: 67.7 ± 5.9 kg, resting heart rate: 66.5 ± 7.0 bpm) without cardiovascular and chronic respiratory problems were recruited. None of the gathered participants had a history of neuromuscular disorders within the past six months. Each participant performed three 30-minute treadmill or terrain running experiences every week in order to maintain his aerobic capability. Participants provided their informed consent after receiving an overview of procedures and potential risks.
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44 Views
This research introduces the Open Seizure Database and Toolkit as a novel, publicly accessible resource designed to advance non-electroencephalogram seizure detection research. This paper highlights the scarcity of resources in the non-electroencephalogram domain and establishes the Open Seizure Database as the first openly accessible database containing multimodal sensor data from 49 participants in real-world, in-home environments.
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1504 Views
This dataset is gathered by using Inertial Measurement Unit Sensor (IMU) (MPU-9250) positioned on the seat of vehicle (Van). This dataset represents the real time sensory data collected with the help of vehicle i.e. School Van on a road at different places in Punjab. The objective of this dataset is to provide an accurate data for plain road and a road with pits.
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468 Views
This dataset curbs real time sensory data collected through different vehicles such as Cycle, Car, Bike and Bus on the humpty-dumpty road. This dataset is collected by using Inertial Measurement Unit (IMU) sensor (MPU-9250) placed on the seats of vehicle. Through some vehicles (Cycle and Bike) are not having place to keep sensor, but it was designed to handle all the hurdles of road having potholes. The dataset aims to tell the exact accuracy of pothole and plane road. This dataset can be used in future for government to allocate budget to repair the rough road.
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753 Views
The Firearm Recoil Dataset was collected utilizing a wrist worn accelerometer to record the recoil generated from one subject’s use of 15 different firearms of the Handgun, Rifle and Shotgun class. The type of the firearm based on its ability to auto-load or not is also denoted.
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886 Views
Driving behavior plays a vital role in maintaining safe and sustainable transport, and specifically, in the area of traffic management and control, driving behavior is of great importance since specific driving behaviors are significantly related with traffic congestion levels. Beyond that, it affects fuel consumption, air pollution, public health as well as personal mental health and psychology. Use of Smartphone sensors for data acquisition has emerged as a means to understand and model driving behavior. Our aim is to analyze driving behavior using on Smartphone sensors’ data streams.
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2331 Views
Nurse Care Activity Recognition
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2238 Views
This dataset consists of sensory data of digits, i.e., from 0 to 9. The dataset is collected from 20 volunteers by using a 9−axis Inertial Measurement Unit (IMU) equipped marker pen. The objective of this dataset is to design classification algorithms for recognizing a handwritten digit in real-time.
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2208 Views