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Found 6 papers, 5 papers with code
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.
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.
