Gait Recognition
This dataset mentioned in the article "Environment Independent Gait Recognition Based on Wi-Fi Signals". This dataset was collected using a pair of Wi-Fi transceivers gathering channel state information of human walking, with the transmitter featuring an omnidirectional antenna and the receiver having three omnidirectional antennas. Data was collected in four indoor environments, where eight users walked along 24 directions. For specific environments and directions arrangements, please refer to the article. Each user walked ten times in each direction.
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MARS-Gait is a new outdoor gait recognition dataset. It is built based on the MARS (Motion Analysis and Re-identification Set) dataset, whose samples are collected in the campus of Tsinghua university. MARS is used for video-based person Re-ID task, which uses 6 near- synchronized cameras located in the campus for recording the pedestrians. It provides the tracklets with person and camera IDs as the annotations. To obtain the gait recognition dataset, we first screened all samples, removing the tracklets where people are obviously not walking, such as cycling or sitting.
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The poor posture is one of the main common health problems in the growth of adolescents, which seriously affects their physical and mental health. The posture gait recognition is a premise for preventing and correcting the poor posture. This paper proposes a gait recognition method for poor posture based on PCA-BP neural network. Using wearable intelligent insoles to measure plantar pressure, a gait recognition model based on PCA-BP neural network model is constructed.
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Radio-Frequency (RF) based User identification enables many attractive applications such as smart homes, and security management. However, laborious data collection is required due to appearance changes, inconsistent walking paths, and environmental variations. Furthermore, multi-user identification persists as an imperative for real-world applications. To this end, we propose an RFID-based user identification system (RF-UI), a few-shot, cross-interference factor, and a continuous user identification system.
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The human gait is unique and so is the impact of a walking human on the propagation of wireless signals within a wireless network. Using appropriate pattern recognition techniques, a person can thus be identified just from a time series of Received Signal Strength (RSS) measurements. This dataset holds bidirectional RSS measurements recorded within a mesh network of four Bluetooth sensor devices. During the measurements, a total of 14 subjects walked individually through the setup. A total of more than 10,000 recordings are provided.
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The Widar3.0 project is a large dataset designed for use in WiFi-based hand gesture recognition. The RF data are collected from commodity WiFi NICs in the form of Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). The dataset consists of 258K instances of hand gestures with a duration of totally 8,620 minutes and from 75 domains. In addition, two sophisticated features from raw RF signal, including Doppler Frequency Shift (DFS) and a new feature Body-coordinate Velocity Profile (BVP) are included.
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