53 datasets found

Human Activity Recognition (HAR) from wearable sensor data identifies movements or activities in unconstrained environments.

The dataset used for activity recognition using wearable sensors.

A dataset of videos of human activities captured by calibrated cameras.

The UCI HAR dataset contains data collected from multiple sensors during different activities such as sitting, walking, etc.

Sensor-based human activity recognition (HAR) aims to recognize human activities based on the availability of rich time-series data collected from multi-modal sensors such as...

Human Activity Recognition (HAR), Gas sensor array drift, Isolet, Internet advertisements, Spambase

HHAR: Heterogeneous Human Activity Recognition Dataset

The UniMiB SHAR dataset was created based on collected data from 30 participants using smartphone accelerometer.

The dataset is used for human activity recognition on mobile devices. It consists of accelerometer and gyroscope data from a Samsung Galaxy S3 smartphone.

The WISDM dataset was collected using Android based smartphones. The data captured with the embedded tri-axial accelerometer was recorded while the users performed 6 different...

Human Activity Recognition (HAR) has broad applications in various domains such as healthcare, athletic competitions, smart cities, and smart home. While researchers focus on...

The EmoPain dataset consists of sequential data from multiple modalities and in unconstrained settings where there are bound to be uncertainties (e.g. in form of sensor noise)...

A new public dataset for early recognition of human activities from first-person videos.

Human activity recognition has wide applications in medical research and human survey system. In this project, we design a robust activity recognition system based on a smartphone.

The HARUS dataset is a human activity recognition dataset, containing 10,299 signals w.r.t. 6 activities.

Human Activity Recognition Using Smartphones Dataset (UCI HAR) includes 3-axial linear acceleration, 3-axial angular velocity, and gyroscope sensor data.

The dataset is used for human activity recognition on mobile devices. It consists of accelerometer and gyroscope data from smartphones and smartwatches.

The Human Activity Recognition dataset contains data gathered from accelerometers and gyroscopes of cell phones from 30 individuals performing six different activities including...

Unsupervised domain adaptation studies the problem of utilizing a relevant source domain with abundant labels to build predictive modeling for an unannotated target domain.

Human activity recognition using multiple sensors is a challenging but promising task in recent decades. In this paper, we propose a deep multimodal fusion model for activity...