The Internet of Things is a key enabler of mobile health-care applications. However, the inherent constraints of mobile devices, such as limited availability of energy, can impair their ability to produce accurate data and, in turn, degrade the output of algorithms processing them in real-time to evaluate the patient's state. This paper presents an edge-assisted framework, where models and control generated by an edge server inform the sensing parameters of mobile sensors. The objective is to maximize the probability that anomalies in the collected signals are detected over extensive periods of time under battery-imposed constraints. Although the proposed concept is general, the control framework is made specific to a use-case where vital signs -heart rate, respiration rate and oxygen saturation- are extracted from a Photoplethysmogram (PPG) signal to detect anomalies in real-time. Experimental results show a 16.9% reduction in sensing energy consumption in comparison to a constant energy consumption with the maximum misdetection probability of 0.17 in a 24-hour health monitoring system.