Wild-SHARD: Samrtphone Sensor-based Human Activity Recognition Dataset in Wild

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.The sensor module, consisting of accelerometers and gyroscopes, was mounted in the front pockets (vertically, phone earpiece side up) of 470 adult subjects of diverse ages, genders, weights, and heights. Subjects performed activities naturally to capture authentic ADL data, including sitting, walking, standing, running, and navigating stairs indoors and outdoors.The dataset was collected at a sampling frequency of 100 Hz, covering six ADLs: sitting, walking, standing, running, going upstairs, and going downstairs. The attributes recorded in the dataset include acceleration due to gravity, linear acceleration, gravity, rotational rate, rotational vector, and the cosine of the rotational vector. This comprehensive dataset structure, as detailed in the provided equations, aims to improve real-time activity recognition and overall system performance by offering high-quality, realistic sensor data.