A remote mobile health monitoring system with mobile phone and web service capabilities is proposed in this paper. It provides an end‐to‐end solution; specifically, (1) physiologic parameters, including respiration rate and heart rate, are measured by wearable sensors and recorded by a mobile phone which presents the graphical interface for the user to observe his/her health status more easily; (2) it provides doctors and family members with necessary data through a web interface and enables authorized personnel to monitor the patient’s condition and to facilitate remote diagnosis; and (3) it supports real‐time alarming and positioning services during an urgent situation, such as a tumble or a heart attack, so that unexpected events can be handled in a timely manner. Experimental results show that the proposed system can reliably monitor the physiologic parameters and conveniently report the user’s position.

Different from conventional spectral method, complexity analysis treaded with the signals' time domain structure feature. Before, due to the no stationary and the unevenness of state space of mechanical signals, the state information is likely to get a loss. Now, the time-window complexity is proposed to overcome certain limitations of complexity itself in some extent. It will help to extract the state features of mechanical systems in different states. The concept and algorithm of time-window complexity is introduced detailed. The way of applying the time-window complexity for fault diagnosis is discussed. The mechanical signals of ball bearing with slight flaw and that of four kinds of impact-rubbing states of a typical rotor are then studied. The results show that time-window complexity can reflect the early fault of ball bearing, and differentiate the four kinds of rotor impact-rubbing states properly, which provides another effective way for fault diagnosis of mechanical systems.