The PAMAP2 Physical Activity Monitoring dataset contains data of 18 different physical activities (such as walking, cycling, playing soccer, etc.), performed by 9 subjects wearing 3 inertial measurement units and a heart rate monitor. The dataset can be used for activity recognition and intensity estimation, while developing and applying algorithms of data processing, segmentation, feature extraction and classification.
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WEAR is an outdoor sports dataset for both vision- and inertial-based human activity recognition (HAR). The dataset comprises data from 18 participants performing a total of 18 different workout activities with untrimmed inertial (acceleration) and camera (egocentric video) data recorded at 10 different outside locations. Unlike previous egocentric datasets, WEAR provides a challenging prediction scenario marked by purposely introduced activity variations as well as an overall small information overlap across modalities.
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The Online Action Detection Dataset (OAD) was captured using the Kinect V2 sensor, which collects color images, depth images and human skeleton joints synchronously. This dataset includes 59 long sequences and 10 actions.
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HASCD (Human Activity Segmentation Challenge Dataset) contains 250 annotated multivariate time series capturing 10.7 h of real-world human motion smartphone sensor data from 15 bachelor computer science students. The recordings capture 6 distinct human motion sequences designed to represent pervasive behaviour in realistic indoor and outdoor settings. The data set serves as a benchmark for evaluating machine learning workflows.
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MOSAD (Mobile Sensing Human Activity Data Set) is a multi-modal, annotated time series (TS) data set that contains 14 recordings of 9 triaxial smartphone sensor measurements (126 TS) from 6 human subjects performing (in part) 3 motion sequences in different locations. The aim of the data set is to facilitate the study of human behaviour and the design of TS data mining technology to separate individual activities using low-cost sensors in wearable devices.
MPHOI-72 is a multi-person human-object interaction dataset that can be used for a wide variety of HOI/activity recognition and pose estimation/object tracking tasks. The dataset is challenging due to many body occlusions among the humans and objects. It consists of 72 videos captured from 3 different angles at 30 fps, with totally 26,383 frames and an average length of 12 seconds. It involves 5 humans performing in pairs, 6 object types, 3 activities and 13 sub-activities. The dataset includes color video, depth video, human skeletons, human and object bounding boxes.
The MPII Human Pose Descriptions dataset extends the widely-used MPII Human Pose Dataset with rich textual annotations. These annotations are generated by various state-of-the-art language models (LLMs) and include detailed descriptions of the activities being performed, the count of people present, and their specific poses.