Human Activity Recognition (HAR)

The GestDoor dataset contains wearable sensor data collected to support research in biometric authentication through arm movements during door-opening interactions. Using two 6-degree-of-freedom (6-DOF) inertial measurement units (IMUs) worn on the wrist and upper arm, 11 participants performed four types of door-opening tasks—left-hand pull, left-hand push, right-hand pull, and right-hand push—across up to three sessions. The dataset includes 3,330 samples comprising accelerometer and gyroscope signals at 100 Hz, along with session metadata.

Wi-Fi sensing has emerged as a promising non-intrusive approach for human activity recognition (HAR), leveraging the widespread availability of Wi-Fi-enabled devices. While machine learning tools help extract meaningful features from complex wireless propagation patterns, developing robust and domain-adaptive models remains challenging without diverse datasets.

1.Dataset overview

This dataset is designed to support the HAR task of this study. Covered by (a) walking, (b) running, (c) going upstairs, (d) going downstairs, (e) high leg lifting, (f) skipping rope, and (g) rhombic extension Seven types of human movement data.

The files D.xlsx, E.xlsx, H.xlsx, R.xlsx, S.xlsx, U.xlsx, and W.xlsx are one-dimensional time series data of seven sports, with a data length of 400, and the number of data categories of 7.

This data set contains three activities of laying up, passing, and shooting of nine professional basketball players, collected and processed by Yu Zhou, Chuanshi Xie, and Yufan Wang.

The dataset contains basketball activity data for nine varsity basketball players of professional skill levels. Each player wore a smart bracelet on their right wrist to record activity data during the event. The smart bracelet contains an accelerometer and gyroscope that collects acceleration and angular velocity information, and it has a sampling frequency of 50 Hz. The basketball activities of the players are laying up, passing and shooting, which are defined as shown in Table 1.

This dataset provides Channel Impulse Response (CIR) measurements from standard-compliant IEEE 802.11ay packets to validate Integrated Sensing and Communication (ISAC) methods. The CIR sequences contain reflections of the transmitted packets on people moving in an indoor environment. They are collected with a 60 GHz software-defined radio experimentation platform based on the IEEE 802.11ay Wi-Fi standard, which is not affected by frequency offsets by operating in full-duplex mode.

The dataset is divided into three parts:

This database includes subject-specific daily living activity data obtained from smartphones' inbuilt accelerometer, gyroscope, and linear acceleration sensors.