Caregivers and relatives of artificially ventilated persons are under high pressure as errors or miscues in care activities can lead to the death of the patients. Therefore, the joint research project “MeSiB” aims at implementing a safety and alerting system, which analyzes the sensor events, infers whether the situation is dangerous, and, if so, alarms the caregiver and/or a telemedicine station. Among other factors, the number of persons in the bed area of the patient is an essential information for this system. Therefore, a subsystem of this safety system detects the number of persons with an infrared array sensor (Panasonic Grid-EYE). This subsystem is presented in this work. The paper describes the general approach and system design, and how the subsystem integrates into the MeSiB architecture. The structure of the software is presented, and different machine learning approaches are tested. Finally, the results of an evaluation study in a realistic home care environment are presented and discussed. In this evaluation study, the Random Forest classifier was used and detection rates of 84% and 96% for two different scenarios were achieved.
@INPROCEEDINGS{Gerka2018detecting,
author={A. {Gerka} and M. {Pfingsthorn} and C. {Lupkes} and K. {Sparenberg} and M. {Frenken} and C. {Lins} and A. {Hein}},
booktitle={2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)},
title={Detecting the Number of Persons in the Bed Area to Enhance the Safety of Artificially Ventilated Persons},
year={2018},
pages={1-6},
doi={10.1109/HealthCom.2018.8531174},
month={Sep.},}