Wearable Sensing
Anatomical landmark trajectories are commonly used to define joint coordinate systems in human kinematic analysis according to standards proposed by the International Society of Biomechanics (ISB). However, most inertial motion capture (IMC) studies focus only on joint angle measurement, which limits its application. Therefore, this paper proposes a new method to calculate the trajectories of anatomical landmarks based on IMC data. The accuracy and reliability of this method were investigated by comparative analysis based on measurement data from 16 volunteers.
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10 soccer supporters gathered to watch a live broadcasted Premier League
match between Liverpool and Manchester United (4 - 0) on 19th of March 2022, all
equipped with wrist-worn accelerometers. All participants were aware of the purpose of this experiment and consented to participate
by attendance at the event, and by wearing the accelerometer. No personally sensitive
information was collected, all data is fully anonymised following the GDPR guidelines
and all procedures were in accordance with the recommendations of the data protection
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135 Views
Grasp intention recognition is a vital problem for controlling assistive robots to help the elderly and infirm people restore arm and hand function. This dataset contains gaze data and scene image data of healthy individuals and hemiplegic patients while performing different grasping tasks. It can be used for gaze-based grasp intention recognition studies.
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186 Views
This data was collected during a validation study of our Torso-Dynamics Estimation System (TES). The TES consisted of a Force Sensing Seat (FSS) and an inertial measurement unit (IMU) that measured the kinetics and kinematics of the subject's torso motions. The FSS estimated the 3D forces, 3D moments, and 2D COPs while the IMU estimated the 3D torso angles. To validate the TES, the FSS and IMU estimates were compared to gold standard research equipment (AMTI force plate and Qualisys motion capture system, respectively).
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106 Views
The dataset contains pressure insole data from twenty subjects who performed five tasks, comprising of two common daily activities (standing and walking), and three industry-focussed tasks (manual handling, assembly and pick and place). The speed and order in which a given task was completed was not prescribed. The data pertains to the areas of human factors, ergonomics and occupational health and safety research, among others, and enables an understanding of the force distributions involved in common tasks as well as physical and manufacturing type tasks.
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IREYE4TASK is a dataset for wearable eye landmark detection and mental state analysis. Sensing the mental state induced by different task contexts, where cognition is a focus, is as important as sensing the affective state where emotion is induced in the foreground of consciousness, because completing tasks is part of every waking moment of life. However, few datasets are publicly available to advance mental state analysis, especially those using the eye as the sensing modality with detailed ground truth for eye behaviors.
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808 Views
Wearable long-term monitoring applications are becoming more and more popular in both the consumer and the medical market. In wearable ECG monitoring, the data quality depends on the properties of the electrodes and on how they contact the skin. Dry electrodes do not require any action from the user. They usually do not irritate the skin, and they provide sufficiently high-quality data for ECG monitoring purposes during low-intensity user activity. We investigated prospective motion artifact–resistant dry electrode materials for wearable ECG monitoring.
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125 Views
Supplementary material for the article "Three-electrode double-differential biopotential amplifier for surface EMG measurements". Connection diagrams, intermediate results, images of the designed prototype and a video of the working prototype are presented.
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117 Views
The main objective of this project is to design and develop a collaborative framework which facilitates real-time tracking of a target person even when GPS signal is not available, while collecting motion data to infer his or her lifestyle and health status. The framework orchestrates a wide range of technologies such as localization technologies, machine learning and AI, sensor data analytics and cloud computing. The overall framework design also takes into consideration the culture, lifestyles, behaviours and infrastructures of ASEAN countries.
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523 Views
The dataset contains motion capture data of the human hand of 20 healthy subjects acquired using two different motion capture technology (wearable IMU and camera-based). This database provides an opportunity to expand the fields of research involving the hands or their range of mobility. Indeed, using this database to train AI's net to recognise gestures/tasks is an excellent beginning point for expanding the field of human-robot collaboration.
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936 Views