Wearable Sensing
The video demonstrates an accurate, low-latency body tracking approach for VR-based applications using Vive Trackers. Using a HTC Vive headset and Vive Trackers, an immersive VR experience, by animating the motions of the avatar as smoothly, rapidly and as accurately as possible, has been created. The user can see her from the first-person view.
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735 Views
Recent advances in scalp electroencephalography (EEG) as a neuroimaging tool have now allowed researchers to overcome technical challenges and movement restrictions typical in traditional neuroimaging studies. Fortunately, recent mobile EEG devices have enabled studies involving cognition and motor control in natural environments that require mobility, such as during art perception and production in a museum setting, and during locomotion tasks.
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3984 Views
Prolonged sitting in a single position adversely affects the spine and leads to chronic issues that require extended therapy for recovery. The principal motivation of this study is to ensure good posture, i.e. when a person's body is positioned correctly and supported by the appropriate level of muscle tension. It draws people's attention to the need for good sitting posture and health. Commercially available wearable sensors provide several advantages as the sensors can be embedded with the clothing.
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226 Views
This dataset contains inertial sensor and optical motion capture data from a trial of 20 healthy adult participants performing various upper limb movements. Each subject had an IMU and cluster of relfective markers attached to their sternum, right upper arm, and right forearm (as in the image attached), and IMU and marker data was recorded simultaneously. This trial was carried out with the intention of investigating alternative sensor-to-segment calibration methods, but may be useful for other areas of inertial sensor research. CAD files for the 3D printed mounts are also included.
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398 Views
This dataset is in support of my research paper - Fault Analysis Centrifugal Pump (To download preprint, pls click on the title).
Acknowledgement : The author as such is thankful to none but obliged to his family for bearing his personal endeavours. Pls read Legal Disclosure Statement.
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176 Views
The EmoReIQ (Emotion Recognition for Iraqi Autism Individuals) dataset is a specialized EEG dataset designed to capture emotional responses in individuals with Autism Spectrum Disorder (ASD) and Typically Developed (TD). It focuses on five core emotions: calm, happy, anger, fear, and sad. The dataset is gathered through an experimental setup using video stimuli to elicit these emotions and records corresponding EEG signals from participants.
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368 Views
This is an American Sign Language from 0-9 dataset based on an 8-electrode EIT on the arm. The excitation frequency is 50kHz, the current is 1.81mA, and the measurement circuit is based on a MAX30009 chip and a CH74HC4067 multiplexer.
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496 Views
The Human Activity Recognition (HAR) dataset comprises comprehensive data collected from various human activities including walking, running, sitting, standing, and jumping. The dataset is designed to facilitate research in the field of activity recognition using machine learning and deep learning techniques. Each activity is captured through multiple sensors providing detailed temporal and spatial data points, enabling robust analysis and model training.
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205 Views
Wild-SHARD presents a novel Human Activity Recognition (HAR) dataset collected in an uncontrolled, real-world (wild) environment to address the limitations of existing datasets, which often need more non-simulated data. Our dataset comprises a time series of Activities of Daily Living (ADLs) captured using multiple smartphone models such as Samsung Galaxy F62, Samsung Galaxy A30s, Poco X2, One Plus 9 Pro and many more. These devices enhance data variability and robustness with their varied sensor manufacturers.
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456 Views
The dataset consists of 4-channeled EOG data recorded in two environments. First category of data were recorded from 21 poeple using driving simulator (1976 samples). The second category of data were recorded from 30 people in real-road conditions (390 samples).
All the signals were acquired with JINS MEME ES_R smart glasses equipped with 3-point EOG sensor. Sampling frequency is 200 Hz.
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147 Views