UCF101 dataset is an extension of UCF50 and consists of 13,320 video clips, which are classified into 101 categories. These 101 categories can be classified into 5 types (Body motion, Human-human interactions, Human-object interactions, Playing musical instruments and Sports). The total length of these video clips is over 27 hours. All the videos are collected from YouTube and have a fixed frame rate of 25 FPS with the resolution of 320 × 240. Source: Two-stream Collaborative Learning with Spatial-Temporal Attention for Video Classification Image Source: https://www.crcv.ucf.edu/data/UCF101.php

The HMDB51 dataset is a large collection of realistic videos from various sources, including movies and web videos. The dataset is composed of 6,766 video clips from 51 action categories (such as “jump”, “kiss” and “laugh”), with each category containing at least 101 clips. The original evaluation scheme uses three different training/testing splits. In each split, each action class has 70 clips for training and 30 clips for testing. The average accuracy over these three splits is used to measure the final performance. Source: Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors Image Source: https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database

The Human Activity Recognition Dataset has been collected from 30 subjects performing six different activities (Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing, Laying). It consists of inertial sensor data that was collected using a smartphone carried by the subjects. Source: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones Image Source: https://www.youtube.com/watch?v=XOEN9W05_4A

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. ** Sensors ** 3 Colibri wireless inertial measurement units (IMU): - sampling frequency: 100Hz - position of the sensors: - 1 IMU over the wrist on the dominant arm - 1 IMU on the chest - 1 IMU on the dominant side's ankle HR-monitor: - sampling frequency: ~9Hz ** Data collection protocol ** Each of the subjects had to follow a protocol, containing 12 different activities. The folder Protocol contains these recordings by subject. Furthermore, some of the subjects also performed a few optional activities. The folder Optional contains these recordings by subject. ** Data files ** Raw sensory data can be found in space-separated text-files (.dat), 1 data file per subject per session (protocol or optional). Missing values are indicated with NaN. One line in the data files correspond to one timestamped and labeled instance of sensory data. The data files contain 54 columns: each line consists of a timestamp, an activity label (the ground truth) and 52 attributes of raw sensory data. Source: UCI

WEAR is an outdoor sports dataset for both vision- and inertial-based human activity recognition (HAR). The dataset comprises data from 22 participants performing a total of 18 different workout activities with untrimmed inertial (acceleration) and camera (egocentric video) data recorded at 11 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.

FLAG3D is a large-scale 3D fitness activity dataset with language instruction containing 180K sequences of 60 categories. FLAG3D features the following three aspects: 1) accurate and dense 3D human pose captured from advanced MoCap system to handle the complex activity and large movement, 2) detailed and professional language instruction to describe how to perform a specific activity, 3) versatile video resources from a high-tech MoCap system, rendering software, and cost-effective smartphones in natural environments. Source: FLAG3D: A 3D Fitness Activity Dataset with Language Instruction Image Source: https://arxiv.org/pdf/2212.04638v1.pdf

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 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.

Abstract In the view of national security, radar micro-Doppler (m-D) signatures-based recognition of suspicious human activities becomes significant. In connection to this, early detection and warning of terrorist activities at the country borders, protected/secured/guarded places and civilian violent protests is mandatory. Designing an automated human suspicious activities: army crawling, army jogging, jumping with holding a gun, army marching, boxing, and stone-pelting/grenades-throwing, recognition system using a suitable deep convolutional neural network (DCNN) model is rapidly growing due to its inherent in-depth features extraction capability. As a value addition to this research, an X-band continuous wave (CW) 10 GHz radar has been developed at our radar systems laboratory and used to acquire the m-D signatures, to prepare a dataset (DIAT-μRadHAR) corresponding to above mentioned suspicious activities. In order to prepare a realistic dataset, human targets of different heights, weights, and gender are directed to perform the suspicious activities in front of the radar at different ranges between 10 m - 0.5 km and at different target aspect angles (0°, ±15°, ±30° and ±45°).

The Robot House Multi-View dataset (RHM) contains four views: Front, Back, Ceiling, and Robot Views. There are 14 classes with 6701 video clips for each view, making a total of 26804 video clips for the four views. The lengths of the video clips are between 1 to 5 seconds. The videos with the same number and the same classes are synchronized in different views.

The Wallhack1.8k dataset comprises 1,806 CSI amplitude spectrograms (and raw WiFi packet time series) corresponding to three activity classes: "no presence," "walking," and "walking + arm-waving." WiFi packets were transmitted at a frequency of 100 Hz, and each spectrogram captures a temporal context of approximately 4 seconds (400 WiFi packets). To assess cross-scenario and cross-system generalization, WiFi packet sequences were collected in LoS and through-wall (NLoS) scenarios, utilizing two different WiFi systems (BQ: biquad antenna and PIFA: printed inverted-F antenna). The dataset is structured accordingly: LOS/BQ/ <- WiFi packets collected in the LoS scenario using the BQ system LOS/PIFA/ <- WiFi packets collected in the LoS scenario using the PIFA system NLOS/BQ/ <- WiFi packets collected in the NLoS scenario using the BQ system NLOS/PIFA/ <- WiFi packets collected in the NLoS scenario using the PIFA system These directories contain the raw WiFi packet time series (see Table 1). Each row represents a single WiFi packet with the complex CSI vector H being stored in the "data" field and the class label being stored in the "class" field. H is of the form [I, R, I, R, ..., I, R], where two consecutive entries represent imaginary and real parts of complex numbers (the Channel Frequency Responses of subcarriers). Taking the absolute value of H (e.g., via numpy.abs(H)) yields the subcarrier amplitudes A.

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.

IM-SportingBehaviors Dataset ## Dataset Overview The IM-SportingBehaviors dataset, developed by researchers at Air University Pakistan, provides detailed motion data from participants engaged in various sports activities. This dataset captures human movement through triaxial accelerometers attached to multiple parts of the body, specifically the knee, wrist, and below-neck areas. The dataset includes motion data from six sports: cycling, badminton, skipping, basketball, football, and table tennis, with participants comprising both professional and amateur athletes aged between 20 and 30 years, weighing between 60 and 100 kilograms. ## Dataset Characteristics - Total Samples: 62,500 - Sports Activities: Cycling, badminton, skipping, basketball, football, and table tennis - Data Imbalance: The dataset is imbalanced, with the ‘Skipping’ class as the most represented (22.4%) and ‘Football’ as the least represented (12.3%). - Sensors Used: Triaxial accelerometers providing 9-dimensional data from three sensor placements; however, only 6-dimensional data is utilized in analyses, corresponding to two accelerometers. - Data Distribution: - Skipping: 22.4% - Cycling: 19% - Table Tennis: 16.8% - Badminton: 16% - Basketball: 13.4% - Football: 12.3% ## Motivation and Summary of Content The IM-SportingBehaviors dataset aims to enhance the understanding of human mobility patterns and biomechanics across a range of sports. By collecting and analyzing motion data through wearable accelerometers, researchers can gain insights into activity-specific movement dynamics and variability in performance between professional and amateur athletes. This dataset's detailed sensor data captures essential aspects of motion and posture, which are crucial for applications in sports science, human-computer interaction, and motion analysis. ## Potential Use Cases The IM-SportingBehaviors dataset is valuable for: 1. Activity Recognition: Developing and validating algorithms to classify sports activities based on sensor data. 2. Performance Analysis: Analyzing movement efficiency and style differences between professional and amateur athletes. 3. Biomechanics Research: Studying human movement patterns, energy expenditure, and motion efficiency in various sports. 4. Wearable Device Optimization: Improving the accuracy of wearable fitness trackers in recognizing and categorizing sports-specific movements. 5. Injury Prevention: Identifying movement patterns or deviations that could contribute to sports-related injuries. This dataset provides a robust foundation for advancing both theoretical and applied research in fields that depend on accurate motion and activity classification.

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.

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. The dataset consists of the same image splits as provided in MMPose, with 14644 training samples and 2723 validation samples. Each image is accompanied by one or more pose descriptions generated by different LLMs. The descriptions are also accompanied by additional annotation information, including the activity type, people count, and pose keypoints, which are derived from the original MPII Human Pose Dataset annotations. By adding textual annotations to the existing human pose dataset, this extended version supports novel research in multi-modal learning, where both visual and textual cues can be explored.

MuscleMap136 is a dataset for video-based Activated Muscle Group Estimation (AMGE) aiming at identifying currently activated muscular regions of humans performing a specific activity. Video-based AMGE is an important yet overlooked problem. To this intent, the MuscleMap136 dataset features 15K video clips with 136 different activities and 20 labeled muscle groups. Source: MuscleMap: Towards Video-based Activated Muscle Group Estimation Image Source: https://arxiv.org/pdf/2303.00952v1.pdf