Wearable Sensing
This database has been developed with reference to the problem of gesture recognition. It contains inertial data related to five gestures: 1) Up: sharp movement upwards, 2) Down: sharp movement downwards, 3) Circle: movement in a circular shape, 4) Left: sharp movement to the left, 5) Right: sharp movement to the right. Data have been recorded with a smartwatch, namely Samsung Gear S, worn on right wrist.
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726 Views
The MAUS dataset focused on collecting easy-acquired physiological signals under different mental demand conditions. We used the N-back task to stimuli different mental workload statuses. This dataset can help in developing a mental workload assessment system based on wearable device, especially for that PPG-based system. MAUS dataset provides ECG, Fingertip-PPG, Wrist-PPG, and GSR signal. User can make their own comparison between Fingertip-PPG and Wrist-PPG. Some study can be carried out in this dataset
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5392 Views
The data set is collected using Neurosky MindWave 2.0 Headset. It uses a single dry electrode placed at FP-1 position for the acquisition of EEG signals. The data is collected from Healthy Individuals and Epileptic Patients performing different Activities of Daily Living (ADLs) in an unconstraint environment.
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1371 Views
This dataset is proposed for human activity recognition tasks. The static activities including sitting, standing, and laying, as well as walking, running, cycling, and walking upstairs/downstairs. Each activity lasts for 2 minutes, 50 subjects were involved in the experiments.
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532 Views
Wearable sensors can efficiently detect human body movements with the ability to be extended to an Internet-of-Things (IoT) platform. In this platform, integration of wearable sensors, smartphones, and activity recognition takes place on a web-based application. A SensorTile kit from STMicroelectronics is an IoT module that packs powerful processing capabilities and is mostly used for activity detection. It consists of a 3-axis accelerometer, 3-axis gyroscope, 3-axis Magnetometer and Pressure, Temperature, and Humidity sensors.
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287 Views
Reliable fatigue assessment is desired in many different fields and environments. An efficient fatigue evaluation tool is promising in reducing fatal errors and economic loss in industrial settings. This dataset contains electroencephalographic (EEG) signals obtained
from an 8-channel OpenBCI headset, as well as biometric measurements obtained from the Empatica E4 wristband. Signals obtained from the E4 include: Photopletismography (PPG), heart rate, inter-beat interval (IBI), skin temperature and Electrodermic Activity
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1278 Views
A wide range of wearable sensors exist on the market for continuous physiological health monitoring. The type and scope of health data that can be gathered is a function of the sensor modality. Blumio presents a dataset of synchronized data from a reference blood pressure device along with several wearable sensor types: PPG, applanation tonometry, and the Blumio millimeter-wave radar. Data collection was conducted under set protocol with subjects seated at rest. 115 study subjects were included (age range 20-67 years), resulting in over 19 hours of data acquired.
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3350 Views
Real-time gesture recognition with bio-impedance measurement. Two videos , one for hand gesture, another for pinch gesture
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435 Views
The UBFC-Phys dataset is a public multimodal dataset dedicated to psychophysiological studies. 56 participants followed a three-step experience where they lived social stress through a rest task T1, a speech task T2 and an arithmetic task T3. During the experience, the participants were filmed and were wearing a wristband that measured their Blood Volume Pulse (BVP) and ElectroDermal Activity (EDA) signals. Before the experience started and once it finished, the participants filled a form allowing to compute their self-reported anxiety scores.
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25983 Views
Human Neck movements data acquired using Meatwear - CPRO device - Accelerometer-based Kinematic data. Data fed to OpenSim simulation software extracted Kinematics and Kinetics (Muscles, joints - Forces, Acceleration, Position)
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331 Views