The proposed GSM and GPS-based system achieved the highest accuracy in classifying stress levels compared to previous algorithms for remote physiological monitoring.
A proposed GSM and GPS-based remote monitoring system using fuzzy logic accurately classifies stress levels, offering a potential telemedicine solution for remote areas without WLAN/Bluetooth access.
Absolute Event Rate: 0% vs 0%
Physiological signals are significant indicators that can help anticipate harmful underlying conditions in humans. Recent advancements in medicine and electronics have allowed monitoring of physiological signals cost effectively and noninvasively. People living in remote areas are usually deprived of basic healthcare facilities and the available remote physiological signals monitoring techniques make use of Bluetooth and WLAN technologies which are inoperable in such areas. The system proposed in this paper solves this issue by making use of GSM and GPS communication techniques due to their vast availability even at remote locations. The proposed system monitors three physiological signals namely; heart rate, skin conductance and skin temperature non-invasively and also classifies stress levels. Finally, the physiological signals and stress levels data is stored for record maintenance and sent to a doctor so that he/she may monitor the patient remotely. A rule-based fuzzy logic algorithm is used for stress classification and the results shows that it achieved the highest accuracy when compared to other algorithms found in previous works. In addition to that, a stress levels dataset is also presented in this paper which can be further refined in future research.
Airij et al. (Sun,) reported a other. The proposed GSM and GPS-based system achieved the highest accuracy in classifying stress levels compared to previous algorithms for remote physiological monitoring.