Evaluation of a model to detect vital signs of a subject trapped in hard-to-reach environment using a laser doppler vibrometry technique

Vital signs detection and monitoring is very key for monitoring the state of life for a patient in a clinical setting or subject trapped in a hard-to-reach environment like war zone, radiation leaked areas etc. In the clinical setting, the contact method is widely used whereas in the hard-to-reach environment, the most feasible method is contactless. In this study, cardiorespiratory signal was acquired contactlessly using the Laser doppler vibrometer (LDV). The experiment recruited 17 subjects and 213 data sets of 60 second long were obtained. The LDV signals were preprocessed by filtering out noise at 40Hz, heart rate between (1 to 5 Hz), and Respiratory rate at (0.1 to 0.5Hz). Features extracted from the signal included, power spectral density (PSD), root mean square (RMS), peak to peak intervals. Using the PSD, the behavior of the signal with various variables of distances, angles, anatomical positions, skin color and cloth tightness were analyzed as shown in Figure 11-16. Data was then divided into two set. One set was for data obtained horizontally from the chest position at a standard distance of 0.5m and angle of 0 degrees. The other set had data collected from all the protocol variables used in this study. Data from random environmental objects and a resuscitation baby mannequin were as well acquired to simulate a hard-to-reach environment and a dead person. The two data sets were used to create two databases in .cvs format. 70% of both databases was used in training the model and 30% for model validation using a decision tree algorithm with 8 nodes and 42 random. Model 1 produced a classification accuracy of 89.0% and model 2 presented an accuracy of 92.0% when classifying data from the random objects and baby mannequin with data from the human subjects. The second model had the best performance when compared to the first model due to the presence of a large data set.