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.

Descripción

Título: Human activity recognition by inertial signals obtained from a smartphone
Autor/es:
  • Madrid García, Alfredo
Director/es:
  • San Segundo Hernández, Rubén
Tipo de Documento: Proyecto Fin de Carrera/Grado
Fecha: 15 Junio 2016
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería Electrónica
Licencias Creative Commons: Reconocimiento - No comercial - Compartir igual

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Resumen

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).

Más información

ID de Registro: 41512
Identificador DC: http://oa.upm.es/41512/
Identificador OAI: oai:oa.upm.es:41512
Depositado por: Alfredo Madrid García
Depositado el: 20 Jun 2016 05:19
Ultima Modificación: 20 Jun 2016 05:19
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