Datasets & Competitions
In the domain of gait recognition, the scarcity of non-simulated, real-world data significantly hampers the performance and applicability of recognition systems. To address this limitation, we present a comprehensive gait recognition dataset - GaitMotion- collected using built-in sensors of Android smartphones in an uncontrolled, real-world environment. This dataset captures the walking activity of 24 subjects (14 females and 10 males) above 18 years old and weighing at least 50 kg.
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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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768 Views
Context
This dataset consists of subject wise daily living activity data, which is acquired from the inbuilt accelerometer and gyroscope sensors of the smartphones.
Content
The smartphone was mounted on the waist and front pockets of the users. All the different activities were performed in a laboratory except Running, which was performed on a Football Playground.
Smartphone used: Poco X2 and Samsung Galaxy A32s
Inbuild Sensors used: Accelerometer and Gyroscope
Ages: All subjects are Above 23 years
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3020 Views