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. 2014 Sep 16;14(9):17212-34.
doi: 10.3390/s140917212.

Smart multi-level tool for remote patient monitoring based on a wireless sensor network and mobile augmented reality

Fernando Cornelio Jiménez González  1 , Osslan Osiris Vergara Villegas  2 , Dulce Esperanza Torres Ramírez  3 , Vianey Guadalupe Cruz Sánchez  4 , Humberto Ochoa Domínguez  5
Affiliations

Smart multi-level tool for remote patient monitoring based on a wireless sensor network and mobile augmented reality

Fernando Cornelio Jiménez González et al. Sensors (Basel). .

Abstract

Technological innovations in the field of disease prevention and maintenance of patient health have enabled the evolution of fields such as monitoring systems. One of the main advances is the development of real-time monitors that use intelligent and wireless communication technology. In this paper, a system is presented for the remote monitoring of the body temperature and heart rate of a patient by means of a wireless sensor network (WSN) and mobile augmented reality (MAR). The combination of a WSN and MAR provides a novel alternative to remotely measure body temperature and heart rate in real time during patient care. The system is composed of (1) hardware such as Arduino microcontrollers (in the patient nodes), personal computers (for the nurse server), smartphones (for the mobile nurse monitor and the virtual patient file) and sensors (to measure body temperature and heart rate), (2) a network layer using WiFly technology, and (3) software such as LabView, Android SDK, and DroidAR. The results obtained from tests show that the system can perform effectively within a range of 20 m and requires ten minutes to stabilize the temperature sensor to detect hyperthermia, hypothermia or normal body temperature conditions. Additionally, the heart rate sensor can detect conditions of tachycardia and bradycardia.

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Figures

Figure 1.
Figure 1.
General schematic of the levels of the SMTRPM.
Figure 2.
Figure 2.
Sink node connected to BT and HR sensors: (a) general view of the sink node; (b) sink node attached to HR sensor; (c) sink node attached to BT sensor.
Figure 3.
Figure 3.
BT prototype: (a) BT sensor based on an NTC-CL80 thermistor and (b) elastic band with the thermistor located to acquire the patient's BT.
Figure 4.
Figure 4.
Heart rate prototype: (a) the photoplethysmographic sensor located on the patient's finger and (b) an example of an ECG curve showing how to identify the R wave and the N-N interval.
Figure 5.
Figure 5.
Flow diagram for the BT algorithm.
Figure 6.
Figure 6.
Flow diagram for the HR algorithm.
Figure 7.
Figure 7.
Specific anomaly detection via AODV (ad hoc on-demand vector) routing.
Figure 8.
Figure 8.
General anomaly detection via AODV routing.
Figure 9.
Figure 9.
Monitor screen of the NSI showing the BT behavior of the last patient node measured in °C.
Figure 10.
Figure 10.
Main screen of the NSI showing the monitoring of two rooms and four patients.
Figure 11.
Figure 11.
Patient hyperthermia condition alarm displayed on the mobile nurse monitor.
Figure 12.
Figure 12.
Example of VPF application: (a) VPF shows current BT value, and (b) VPF shows current HR value.
Figure 13.
Figure 13.
BT measurement trials with different β values to determine the response time of the BT prototype.
Figure 14.
Figure 14.
Tests of HR measurements in patients following physical activity.
Figure 15.
Figure 15.
Tests of HR measurements in patients without previous physical activity.
See this image and copyright information in PMC

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