Human activity recognition by inertial signals obtained from a smartphone

Madrid García, Alfredo (2016). Human activity recognition by inertial signals obtained from a smartphone. Proyecto Fin de Carrera / Trabajo Fin de Grado, E.T.S.I. Telecomunicación (UPM), Madrid.

Description

Title: Human activity recognition by inertial signals obtained from a smartphone
Author/s:
  • Madrid García, Alfredo
Contributor/s:
  • San Segundo Hernández, Rubén
Item Type: Final Project
Date: 15 June 2016
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - Non commercial - Share

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Abstract

Human Activity Recognition (HAR) is an emerging research field with the aim to identify the actions carried out by a person given a set of observations and the surrounding environment. The wide growth in this research field inside the scientific community is mainly explained by the high number of applications that are arising in the last years. A great part of the most promising applications are related to the healthcare field, where it is possible to track the mobility of patients with motor dysfunction as also the physical activity in patients with cardiovascular risk. Until a few years ago, by using distinct kind of sensors, a patient follow-up was possible. However, far from being a long-term solution and with the smartphone irruption, that monitoring can be achieved in a non-invasive way by using the embedded smartphone’s sensors. For these reasons this Final Degree Project arises with the main target to evaluate new feature extraction techniques in order to carry out an activity and user recognition, and also an activity segmentation. The recognition is done thanks to the inertial signals integration obtained by two widespread sensors in the greater part of smartphones: accelerometer and gyroscope. In particular, six different activities are evaluated walking, walking-upstairs, walking-downstairs, sitting, standing and lying. Furthermore, a segmentation task is carried out taking into account the activities performed by thirty users. This can be done by using Hidden Markov Models and also a set of tools tested satisfactory in speech recognition: HTK (Hidden Markov Model Toolkit).

More information

Item ID: 41512
DC Identifier: http://oa.upm.es/41512/
OAI Identifier: oai:oa.upm.es:41512
Deposited by: Alfredo Madrid García
Deposited on: 20 Jun 2016 05:19
Last Modified: 20 Jun 2016 05:19
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