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
Towards a geometric understanding of Spatio Temporal Graph Convolution Networks
In this paper, we first propose to use a local Dataset Graph (DS-Graph) obtained from the feature representation of input data at each layer to develop an understanding of the layer-wise embedding geometry of the STGCN.
Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition
However, the high computational cost of optimization-based TTA algorithms makes it intractable to run on resource-constrained edge devices.
Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning
eth-siplab/Finding_Order_in_Chaos
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NeurIPS 2023
The success of contrastive learning is well known to be dependent on data augmentation.
Human Activity Segmentation Challenge @ ECML/PKDD’23
Despite its importance, existing methods demonstrate limited efficacy on real-world multivariate time series data.
Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient
luyg45/hardnoboxattack
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ICCV 2023
Specifically, we first learn a motion manifold where we define an adversarial loss to compute a new gradient for the attack, named skeleton-motion-informed (SMI) gradient.
Evaluating Spiking Neural Network On Neuromorphic Platform For Human Activity Recognition
Energy efficiency and low latency are crucial requirements for designing wearable AI-empowered human activity recognition systems, due to the hard constraints of battery operations and closed-loop feedback.
Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity
getalp/smartcomp2023-har-supervised-pretraining
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In this paper, we propose a novel strategy to combine publicly available datasets with the goal of learning a generalized HAR model that can be fine-tuned using a limited amount of labeled data on an unseen target domain.
MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction
information-fusion-lab-umass/multiwave
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To address these issues, we introduce MultiWave, a novel framework that enhances deep learning time series models by incorporating components that operate at the intrinsic frequencies of signals.
TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning
Limited availability of labeled physiological data often prohibits the use of powerful supervised deep learning models in the biomedical machine intelligence domain.
Robust Framework for Explanation Evaluation in Time Series Classification
This paper provides a framework to quantitatively evaluate and rank explanation methods for time series classification.
