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Found 13 papers, 7 papers with code
Temporal Action Localization for Inertial-based Human Activity Recognition
Our results show that state-of-the-art TAL models are able to outperform popular inertial models on 4 out of 6 wearable activity recognition benchmark datasets, with improvements ranging as much as 25% in F1-score.
Do predictability factors towards signing avatars hold across cultures?
Extrinsic factors include users technology experience, their hearing status, age and their sign language fluency.
Hang-Time HAR: A Benchmark Dataset for Basketball Activity Recognition using Wrist-Worn Inertial Sensors
We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games.
A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors
Furthermore, we discuss the advantages and disadvantages of the methods compared in our study, the biases they may could introduce and the consequences of their usage on human activity recognition studies and as well as possible solutions.
WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition
Though research has shown the complementarity of camera- and inertial-based data, datasets which offer both egocentric video and inertial-based sensor data remain scarce.
Egocentric Activity Recognition
Human Activity Recognition
+2
An Embedded and Real-Time Pupil Detection Pipeline
For evaluation on our hardware-specific camera frames, we also contribute a dataset of 35000 images, from 20 participants.
Using Multivariate Linear Regression for Biochemical Oxygen Demand Prediction in Waste Water
The performance indices for the input variables of Dissolved Oxygen, Nitrogen, Fecal Coliform and Total Coliform in prediction of BOD are: RMSE=6. 77mg/L, r=0. 60 and accuracy 70. 3% for training dataset of 80% and RMSE=6. 74mg/L, r=0. 60 and accuracy of 87. 5% for training set of 90% of the dataset.
Tutorial on Deep Learning for Human Activity Recognition
Activity recognition systems that are capable of estimating human activities from wearable inertial sensors have come a long way in the past decades.
Detecting Handwritten Mathematical Terms with Sensor Based Data
The second classifier is a Deep Neural Network that combines convolution layers with recurrent layers to predict windows with a single label, out of the 15 possible classes, at an F1 score of >60%.
Transformer Networks for Data Augmentation of Human Physical Activity Recognition
It improves generalization and reduces amount of annotated human activity data needed for training which reduces labour and time needed with the dataset.
Improving Deep Learning for HAR with shallow LSTMs
Recent studies in Human Activity Recognition (HAR) have shown that Deep Learning methods are able to outperform classical Machine Learning algorithms.
On-site Online Feature Selection for Classification of Switchgear Actuations
Process- and design-specific features can be learned locally (e. g. on a sensor system) without the need of prior offline training.
Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks
We show that on large datasets the deep learning model significantly outperforms other methods: The mean absolute error could be reduced by 31% on the new dataset PPG-DaLiA, and by 21% on the dataset WESAD.
Ranked #1 on
Heart rate estimation
on PPG-DaLiA
