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
Most implemented papers
Learning Generalizable Physiological Representations from Large-scale Wearable Data
sdimi/Step2heart
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To date, research on sensor-equipped mobile devices has primarily focused on the purely supervised task of human activity recognition (walking, running, etc), demonstrating limited success in inferring high-level health outcomes from low-level signals, such as acceleration.
B-HAR: an open-source baseline framework for in depth study of human activity recognition datasets and workflows
B-HAR-HumanActivityRecognition/B-HAR
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Human Activity Recognition (HAR), based on machine and deep learning algorithms is considered one of the most promising technologies to monitor professional and daily life activities for different categories of people (e. g., athletes, elderly, kids, employers) in order to provide a variety of services related, for example to well-being, empowering of technical performances, prevention of risky situation, and educational purposes.
AdaRNN: Adaptive Learning and Forecasting of Time Series
jindongwang/transferlearning
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This paper proposes Adaptive RNNs (AdaRNN) to tackle the TCS problem by building an adaptive model that generalizes well on the unseen test data.
Transformer Networks for Data Augmentation of Human Physical Activity Recognition
sandeep-189/data-augmentation
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It improves generalization and reduces amount of annotated human activity data needed for training which reduces labour and time needed with the dataset.
Defending Black-box Skeleton-based Human Activity Classifiers
realcrane/robustactionrecogniser
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Our method is featured by full Bayesian treatments of the clean data, the adversaries and the classifier, leading to (1) a new Bayesian Energy-based formulation of robust discriminative classifiers, (2) a new adversary sampling scheme based on natural motion manifolds, and (3) a new post-train Bayesian strategy for black-box defense.
SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing
chenxinyan-sg/wifi-csi-sensing-benchmark
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WiFi sensing has been evolving rapidly in recent years.
HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
event-ahu/hardvs
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The main streams of human activity recognition (HAR) algorithms are developed based on RGB cameras which are suffered from illumination, fast motion, privacy-preserving, and large energy consumption.
Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
In this paper, we newly introduce the concept of temporal attention filters, and describe how they can be used for human activity recognition from videos.
Untrimmed Video Classification for Activity Detection: submission to ActivityNet Challenge
We propose a simple, yet effective, method for the temporal detection of activities in temporally untrimmed videos with the help of untrimmed classification.
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
DigitalBiomarkerDiscoveryPipeline/Human-Activity-Recognition
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For many mobile applications, it is hard to find a distribution that exactly describes the noise in practice.
