Sensors

 Our dataset has a total of 8 actions, 7 people(P1-P7), and 3 experimental environments(Room-A,Room-B,Room-C). There are a total of 3 directions in each environment, with 5 samples of each action taken for each person in each direction, so the number of samples is 360(samples/person)*7 = 2520.

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Preventing heatstroke is of utmost importance as it poses a significant threat to life and can lead to severe health complications and even death. Heatstroke occurs when the body's internal temperature reaches hazardous levels, typically due to prolonged exposure to high temperatures or intense physical activity in hot weather. Recognizable symptoms of heatstroke encompass confusion, rapid heartbeat, accelerated breathing, seizures, and loss of consciousness. Moreover, projections suggest a 260% surge in mortality rates attributed to heat-related incidents by the 2050s.

Last Updated On: 
Thu, 05/25/2023 - 13:50

The signals of a recording belonging to the Frontside task are shown, the first two corresponding to the electromyography sensors while the others correspond to the infrared and red LED readings of the pulse oximetry sensor.

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This data set contains two kinds of road perception information: image and acoustics. It covers four kinds of pavement: asphalt pavement, water pavement, gravel pavement and snow pavement. The image and audio files of the whole data set are too large, and this data set is part of it for researchers' reference. Please contact wangzhangu1@163.com if you need the whole data.

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SWAN is a large-scale outdoor point cloud semantic segmentation, instance segmentation and object detection dataset.  The dataset is targeted explicitly at the challenging urban environment, which aligns well with the needs of the intelligent transportation systems. The data is collected in the Central Business District (CBD) of Perth city in Australia, covering nearly 150km. It additionally used specialized equipment (portable trolley) to capture scenes of no-through roads and narrow streets.

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SWAN is a Large-Scale Outdoor Point Cloud semantic segmentation dataset .  The dataset is targeted explicitly at the challenging urban environment, which aligns well with the needs of the intelligent transportation systems. The data is collected in the Central Business District (CBD) of Perth city in Australia, covering nearly 150km. It additionally used specialized equipment (portable trolley) to capture scenes of no-through roads and narrow streets.

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42 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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50 Views

TEP

TE benchmark has been widely used to evaluate process monitoring performance, which is mainly composed of multiple operation units including continuous stirred tank reactor, condenser, gas-liquid separation tower, stripper, reboiler and centrifugal compressor.

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The dataset for the vehicles collusion detection in internet of vehicles is generated from internet of vehicles enviornment using VSSIM. The detail procedure of the data collection and the preprocessing procedure can be found in the publihsed paper - https://doi.org/10.1016/j.eswa.2022.119033.

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20 falls and 16 daily living activities were performed by 17 volunteers with 5 repetitions while wearing 6 sensors (3.060 instances) that attached to their head, chest, waist, wrist, thigh and ankle.

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