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
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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
MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels
Human activity recognition (HAR) will be an essential function of various emerging applications.
DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers
themaxiao/ditmos
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Enabling efficient and accurate deep neural network (DNN) inference on microcontrollers is non-trivial due to the constrained on-chip resources.
Class-incremental Learning for Time Series: Benchmark and Evaluation
zqiao11/tscil
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Real-world environments are inherently non-stationary, frequently introducing new classes over time.
AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture Search
This paper introduces AutoGCN, a generic Neural Architecture Search (NAS) algorithm for Human Activity Recognition (HAR) using Graph Convolution Networks (GCNs).
IMUGPT 2.0: Language-Based Cross Modality Transfer for Sensor-Based Human Activity Recognition
With the emergence of generative AI models such as large language models (LLMs) and text-driven motion synthesis models, language has become a promising source data modality as well as shown in proof of concepts such as IMUGPT.
A Review of Deep Learning Methods for Photoplethysmography Data
In this review, we systematically reviewed papers that applied deep learning models to process PPG data between January 1st of 2017 and July 31st of 2023 from Google Scholar, PubMed and Dimensions.
Uncertainty-aware Bridge based Mobile-Former Network for Event-based Pattern Recognition
event-ahu/uncertainty_aware_mobileformer
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The mainstream human activity recognition (HAR) algorithms are developed based on RGB cameras, which are easily influenced by low-quality images (e. g., low illumination, motion blur).
RSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing
bornabr/rscnet
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Nevertheless, resource-constrained IoT devices and the intricacies of deep neural networks necessitate transmitting CSI to cloud servers for sensing.
Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark
ericssonresearch/uda-4-tsc
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Unsupervised Domain Adaptation (UDA) aims to harness labeled source data to train models for unlabeled target data.
Enhanced Spatio- Temporal Image Encoding for Online Human Activity Recognition
It can be done using 3D skeleton data extracted from a RGB+D camera.
