Equipment for the correct identification of living objects entrapped under heavy debris is generally purpose-built, is costly, must be operated by highly trained professionals and is not readily available in a catastrophic event. A more readily available solution for improving the time-to-rescue ratio and logistics issues can be provided with smartphones which, equipped with software to find signs of life, are readily available at any disaster scene. This paper examines whether cardiac and pulmonary-related activities of living objects can provide acceptably accurate readings from a non-contact detection method. Laboratory experiments were conducted with Doppler radar at a 2.4 GHz frequency spectrum similar to smartphone-like devices, with empirical results demonstrating that human vital signs can be clearly identified when using smartphones for non-contact detection of living objects entrapped under debris. Experiments also simulated the psychogenic tremors likely to be experienced by individuals while operating the sensor-equipped devices under crisis conditions. The results show a clear relationship between the wavelength of pulmonary and blood vessel activities and the distance between the trapped human and the sensor in various conditions. The article also reports the design of a pseudo learning algorithm for model-based anomaly detection in time series to detect vital signs during normal and abnormal ventilation based on cardiopulmonary clinical records and datasets. This work significantly contributes to the existing body of research on timely rescue during disaster events.