Wearable Sensing
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 obtainedfrom 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(EDA).
- Categories:
673 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.
- Categories:
1126 Views
Real-time gesture recognition with bio-impedance measurement. Two videos , one for hand gesture, another for pinch gesture
- Categories:
315 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.
- Categories:
7209 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)
- Categories:
219 Views
The dataset is part of the MIMIC database and specifically utilise the data corresponding to two patients with ids 221 and 230.
- Categories:
406 Views
The purpose of this data collection was for the validation of a cuffless blood pressure estimation model during activities of daily living. Data were collected on five young healthy individuals (four males, age 28 ± 6.6 yrs) of varied fitness levels, ranging from sedentary to regularly active, and free of cardiovascular and peripheral vascular disease. Arterial blood pressure was continuously measured using finger PPG (Portapres; Finapres Medical Systems, the Netherlands).
- Categories:
1227 Views
The PD-BioStampRC21 dataset provides data from a wearable sensoraccelerometry study conducted for studying activity, gait, tremor, andother motor symptoms in individuals with Parkinson's disease (PD). Inaddition to individuals with PD, the dataset also includes data forcontrols that also went through the same study protocol as the PDparticipants. Data were acquired using lightweight MC 10 BioStamp RCsensors (MC 10 Inc, Lexington, MA), five of which were attached toeach participant for gathering data over a roughly two dayinterval.
- Categories:
2050 Views
Falls are a major health problem with one in three people over the age of 65 falling each year, oftentimes causing hip fractures, disability, reduced mobility, hospitalization and death. A major limitation in fall detection algorithm development is an absence of real-world falls data. Fall detection algorithms are typically trained on simulated fall data that contain a well-balanced number of examples of falls and activities of daily living. However, real-world falls occur infrequently, making them difficult to capture and causing severe data imbalance.
- Categories:
516 Views
Inertial measurement units (IMUs) are used in biomechanical and clinical applications for quantifying joint kinematics. This study aimed to assist researchers who are new to IMUs and want to develop inexpensive IMU system to estimate the relative angle between IMUs, while understanding the effect of different computational algorithms for estimating angular kinematics.
- Categories:
1460 Views