Wearable Full-Body Inertial Measurementwith Task Classification Using Deep Learning

JEREMÍAS GAIA; EUGENIO OROSCO; CARLOS SORIA

IEEE LATIN AMERICA TRANSACTIONS

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Lugar: Mexico; Año: 2021 vol. 18 p. 115 - 123

1548-0992

In this work, an embedded system is developed for the non-invasive sensing and storage of biomechanical variables of people. It takes advantage of wearable technology, distributing sensors in strategic points of the body, ergonomically and functionally. The results are verified by recording and analyzing tasks performed by six subjects to form a database. These tasks include being stood up, sitting down or standing up from a chair, going upstairs and downstairs and walking. Additionally, a convolutional neural network is tested for offline task classification. This work aims to initiate a process that ends in assistance-oriented applications, for the development of better injury rehabilitation techniques and support for elder people, among others. In this way, it seeks to open a path towards an improvement in the living conditions of people with and without reduced activities of daily living capacity.