Title
Upper Limb Motion Tracking and Classification: A Smartphone Approach
Abstract
Due to the evolution of motion capture devices, natural user interfaces have been applied in several areas, such as neuromotor rehabilitation supported by virtual environments. This paper presents a smartphone application that allows the user to interact with the virtual environment and enables the captured data to be stored, processed, and used in machine learning models. The application submits the recordings to the remote database with information about the movement and in order to apply supervised machine learning. As a proof of concept, we generated a dataset capturing movement data using our application with 232 instances divided into 8 classes of movements. Moreover, we used this dataset for training models that classifies these movements. The remarkable accuracy of the models shows the feasibility of using body articulation data for a classification task after some data transformations.
Year
DOI
Venue
2021
10.1145/3470482.3479618
PROCEEDINGS OF THE 27TH BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA '21)
Keywords
DocType
Citations 
Computer vision, motion capture, augmented reality, supervised machine learning
Conference
0
PageRank 
References 
Authors
0.34
0
7