Indoor navigation applied to the detection of allergic reactions during provocation tests

The current method to detect allergies obliges the patient to stay within a room under the observation of the nurse during the allergy tests. Such tests last up to 4 hours. The authors have developed an allergy detection system based on the heart rate variability, which is monitored through electrocardiogram sensors. The detector is based on the correlation between variations of the heart rate and allergic reactions. However, heart rate variations are also due to the patient's physical activity, being necessary to discriminate them. The pocket navigation system will be used to enhance the performance of the allergy detection algorithm, as it is able to identify different physical activities. Additionally, the patient can walk out of the testing room because a 3D localization solution is possible. The experiments described in this article show that it is possible identifying the most important patient's physical activities to avoid false allergic reaction alarms. Moreover, the patient is allowed to move within the hospital during the allergy tests and can be located in case of a severe allergic reaction occurs. The experiments show also long duration walks to assess that our unaided inertial-based navigation system is suited to this particular medical application.

[1]  Alessandro Tognetti,et al.  Heart Rate and Accelerometer Data Fusion for Activity Assessment of Rescuers During Emergency Interventions , 2010, IEEE Transactions on Information Technology in Biomedicine.

[2]  Kwang Suk Park,et al.  Validation of heart rate extraction through an iPhone accelerometer , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Pablo Laguna,et al.  The Integral Pulse Frequency Modulation Model With Time-Varying Threshold: Application to Heart Rate Variability Analysis During Exercise Stress Testing , 2011, IEEE Transactions on Biomedical Engineering.

[4]  Fabian de Ponte Müller,et al.  Evaluation of AHRS algorithms for inertial personal localization in industrial environments , 2015, 2015 IEEE International Conference on Industrial Technology (ICIT).

[5]  Korbinian Frank,et al.  Bayesian recognition of safety relevant motion activities with inertial sensors and barometer , 2014, 2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014.

[6]  William P. Marnane,et al.  Use of the Heart Rate Variability as a Diagnostic Tool , 2016, BIOSTEC 2016.

[7]  Estefania Munoz Diaz Inertial Pocket Navigation System: Unaided 3D Positioning , 2015, Sensors (Basel, Switzerland).

[8]  Pablo Laguna,et al.  Influence of Running Stride Frequency in Heart Rate Variability Analysis During Treadmill Exercise Testing , 2013, IEEE Transactions on Biomedical Engineering.

[9]  Valérie Renaudin,et al.  Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users , 2013, Sensors.

[10]  Eric Foxlin,et al.  Pedestrian tracking with shoe-mounted inertial sensors , 2005, IEEE Computer Graphics and Applications.

[11]  Kenneth Meijer,et al.  Activity identification using body-mounted sensors—a review of classification techniques , 2009, Physiological measurement.

[12]  Estefania Munoz Diaz,et al.  Step detector and step length estimator for an inertial pocket navigation system , 2014, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[13]  Sinziana Mazilu,et al.  ActionSLAM: Using location-related actions as landmarks in pedestrian SLAM , 2012, 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[14]  William P. Marnane,et al.  Low complexity QRS detectors for performance and energy aware applications , 2014, IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).