TY - GEN
T1 - PRIDE-2
T2 - HPI Future SOC Lab 2017
AU - Rodríguez, Jorge
AU - Medina-Pérez, Miguel Angel
AU - Trejo, Luis A.
AU - Barrera-Animas, Ari Y.
AU - Monroy, Raúl
AU - López-Cuevas, Armando
AU - Ramírez-Márquez, José
N1 - Publisher Copyright:
© 2019 Universitatsverlag Potsdam. All rights reserved.
PY - 2019
Y1 - 2019
N2 - In our previous project [2], we defined personal risk detection as the timely identification of a situation when someone is at imminent peril, such as a health crisis or a car accident. A risk-prone situation should produce sudden and significant deviations in user patterns, and the changes can be captured by a group of sensors, such as an accelerometer, gyroscope, and heart rate monitor, which are normally found in current wearable devices. Previous research findings were published in [2, 11] and presented at HPI Future SOC Lab. The present work rises with the aim of improving our previous results. In order to achieve it, the following three approaches were tested: 1) a visualization method in real-time of PRIDE users leveraged with a one-class classifier called Bagging-TPMiner, 2) the addition of frequency-domain features to the time-domain features embraced in the PRIDE dataset, and 3) improve the accuracy obtained by previous one-class classifiers through testing a cluster validation algorithm. We were able to report part of our results in [8], which have been recently submitted for publication. Although experiment results reported in this document are encouraging, due to the sheer amount of data, the results presented in this report are partial. In order to fulfil the experiments, we are submitting an extension at HPI Future SOC Lab for the period that ends on April 2017.
AB - In our previous project [2], we defined personal risk detection as the timely identification of a situation when someone is at imminent peril, such as a health crisis or a car accident. A risk-prone situation should produce sudden and significant deviations in user patterns, and the changes can be captured by a group of sensors, such as an accelerometer, gyroscope, and heart rate monitor, which are normally found in current wearable devices. Previous research findings were published in [2, 11] and presented at HPI Future SOC Lab. The present work rises with the aim of improving our previous results. In order to achieve it, the following three approaches were tested: 1) a visualization method in real-time of PRIDE users leveraged with a one-class classifier called Bagging-TPMiner, 2) the addition of frequency-domain features to the time-domain features embraced in the PRIDE dataset, and 3) improve the accuracy obtained by previous one-class classifiers through testing a cluster validation algorithm. We were able to report part of our results in [8], which have been recently submitted for publication. Although experiment results reported in this document are encouraging, due to the sheer amount of data, the results presented in this report are partial. In order to fulfil the experiments, we are submitting an extension at HPI Future SOC Lab for the period that ends on April 2017.
UR - http://www.scopus.com/inward/record.url?scp=85160685457&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85160685457&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85160685457
T3 - Technische Berichte des Hasso-Plattner-Instituts fur Softwaresystemtechnik an der Universitat Potsdam
SP - 105
EP - 116
BT - HPI Future SOC Lab - Proceedings 2017
A2 - Meinel, Christoph
A2 - Polze, Andreas
A2 - Beins, Karsten
A2 - Strotmann, Rolf
A2 - Seibold, Ulrich
A2 - Rodszus, Kurt
A2 - Muller, Jurgen
Y2 - 15 November 2017
ER -