Telemonitoring during the golden hour of patient transportation helps to improve medical care. Presently there are different physiological data acquisition and transmission systems using cellular network and radio communication links. Location monitoring systems and video transmission systems are also commercially available. The emergency patient transportation systems uniquely require transmission of data pertaining to the patient, vehicle, time of the call, physiological signals (like ECG, blood pressure, a body temperature, and blood oxygen saturation), location information, a snap shot of the patient, and voice. These requirements are presently met by using separate communication systems for voice, physiological data, and location that result in a lot of inconvenience to the technicians, maintenance related issues, in addition to being expensive. This paper presents design, development, and implementation of such a telemonitoring system for emergency patient transportation employing ARM 9 processor module. This system is found to be very useful for the emergency patient transportation being undertaken by organizations like the Emergency Management Research Institute (EMRI).

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Drowsiness detection plays a vital role in accidents avoidance systems, thereby saving many precious lives. Many attempts were made to detect the drowsiness by the physiological features such as Electroencephalogram (EEG), Electrooculogram (EOG), and Heart Rate Variability, etc., but a reliable index to determine the drowsiness is not yet a reality. This study contributes in identifying the drowsiness levels by an index called Drowsiness Index (DI) from the EEG signal analysis of the drivers. In this report, the EEG signal is processed to detect the behavioural patterns of the brain and drowsiness state of the drivers while performing monotonous driving for long distances. An eight-channel EEG data acquisition system is used to acquire the EEG data from thirteen male volunteers. The EEG signal is pre-processed and decomposed into various rhythms by applying a digital filter in MATLAB 2007b. Time-Frequency domain analysis has been done to extract certain features namely power within spectrogram, power within the root mean square deviation which are statistically significant (ρ < 0.05) in the detection of drowsiness. The driving profile is classified into active and drowsy by a separable marker with a range of 0.4-0.6, and linear regression analysis has been performed on the features extracted. A Drowsiness index is proposed stating a positive correlation (0.8-0.9) between the Total mean and the drowsy mean of the subject. The final features extracted from the data are classified using an ANN-based classifier system and has achieved a sensitivity of 99.82 per cent and specificity of 99.78 per cent.