Human Activity Recognition
136 papers with code • 4 benchmarks • 10 datasets
Classify various human activities
Benchmarks
These leaderboards are used to track progress in Human Activity Recognition
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Human Activity Recognition models and implementationsDatasets
HAR
PAMAP2
WEAR
MPHOI-72
FLAG3D
HASCD
MPII Human Pose Descriptions
MOSAD
MuscleMap136
Latest papers with no code
Deep Generative Domain Adaptation with Temporal Relation Knowledge for Cross-User Activity Recognition
To bridge this gap, our study introduces a Conditional Variational Autoencoder with Universal Sequence Mapping (CVAE-USM) approach, which addresses the unique challenges of time-series domain adaptation in HAR by relaxing the i. i. d.
Cross-user activity recognition using deep domain adaptation with temporal relation information
To address this challenge, we introduce the Deep Temporal State Domain Adaptation (DTSDA) model, an innovative approach tailored for time series domain adaptation in cross-user HAR.
Cross-user activity recognition via temporal relation optimal transport
$ and do not consider the knowledge of temporal relation hidden in time series data for aligning data distribution.
Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review
Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analysing behaviours through multi-dimensional observations.
Deep Generative Domain Adaptation with Temporal Attention for Cross-User Activity Recognition
Addressing this oversight, our research presents the Deep Generative Domain Adaptation with Temporal Attention (DGDATA) method.
ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models
Neuro-Symbolic AI (NeSy) provides an interesting research direction to mitigate this issue, by infusing common-sense knowledge about human activities and the contexts in which they can be performed into HAR deep learning classifiers.
HARGPT: Are LLMs Zero-Shot Human Activity Recognizers?
Our study, HARGPT, presents an affirmative answer by demonstrating that LLMs can comprehend raw IMU data and perform HAR tasks in a zero-shot manner, with only appropriate prompts.
Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Semi-supervised Cross-domain Neural Networks for Indoor Environment
The label classifier obtained from training the source domain data improves the recognition of target domain activities due to the semi-supervised learning utilized in training the target domain data.
MaskFi: Unsupervised Learning of WiFi and Vision Representations for Multimodal Human Activity Recognition
Benefiting from our unsupervised learning procedure, the network requires only a small amount of annotated data for finetuning and can adapt to the new environment with better performance.
Comparative Analysis of XGBoost and Minirocket Algortihms for Human Activity Recognition
This study investigates the efficacy of two ML algorithms, eXtreme Gradient Boosting (XGBoost) and MiniRocket, in the realm of HAR using data collected from smartphone sensors.
