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 with no code
Text me the data: Generating Ground Pressure Sequence from Textual Descriptions for HAR
We show that the combination of vector quantization of sensor data along with simple text conditioned auto regressive strategy allows us to obtain high-quality generated pressure sequences from textual descriptions with the help of discrete latent correlation between text and pressure maps.
From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition
The advancement of deep learning in human activity recognition (HAR) using 3D skeleton data is critical for applications in healthcare, security, sports, and human-computer interaction.
MAGNETO: Edge AI for Human Activity Recognition -- Privacy and Personalization
Human activity recognition (HAR) is a well-established field, significantly advanced by modern machine learning (ML) techniques.
DS-MS-TCN: Otago Exercises Recognition with a Dual-Scale Multi-Stage Temporal Convolutional Network
A cohort of 36 older adults participated in laboratory settings, supplemented by an additional 7 older adults recruited for at-home assessments.
Phase-driven Domain Generalizable Learning for Nonstationary Time Series
Monitoring and recognizing patterns in continuous sensing data is crucial for many practical applications.
iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition
While IMUs are currently the prominent fitness tracking modality, through iMove, we show bio-impedence can help improve IMU-based fitness tracking through sensor fusion and contrastive learning. To evaluate our methods, we conducted an experiment including six upper body fitness activities performed by ten subjects over five days to collect synchronized data from bio-impedance across two wrists and IMU on the left wrist. The contrastive learning framework uses the two modalities to train a better IMU-only classification model, where bio-impedance is only required at the training phase, by which the average Macro F1 score with the input of a single IMU was improved by 3. 22 \% reaching 84. 71 \% compared to the 81. 49 \% of the IMU baseline model.
Sensor-Based Data Acquisition via Ubiquitous Device to Detect Muscle Strength Training Activities
Maintaining a high quality of life through physical activities (PA) to prevent health decline is crucial.
Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data
To address these challenges, we propose a multilevel heterogeneous neural network, called MHNN, for sensor data analysis.
Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review
In this study, a systematic review of the literature is presented on fall detection and Human Activity Recognition (HAR) for the elderly, as the two main tasks to solve to guarantee the safety of elderly people living alone.
Transfer Learning in Human Activity Recognition: A Survey
In this survey, we focus on these transfer learning methods in the application domains of smart home and wearables-based HAR.
