Year: 2023
Type: article
Abstract: This paper addresses the problem of Human Activity Recognition (HAR) of people wearing inertial sensors using data from the UCI-HAR dataset. We propose a light residual network which obtains an F1-Score of 97.6% that outperforms previous works, while drastically reducing the number of parameters by a factor of 15, and thus the training complexity. In addition, we propose a new benchmark based on leave-one(person)-out cross-validation to standardize and unify future classifications on the same dataset, and to increase reliability and fairness in the comparisons. In addition, we propose a common bechmark for the UCI-HAR dataset based on Leaving On (Participant) Out Cross-Validation (LOOCV) to justify the robustness of the architecture proposed and providing a fairer approach for comparisons.
Source: IEEE Sensors Letters
Institution: Örebro University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,337
OpenAlex ID: W4386432007