Personalized health capability is limited to the available data from the patient, which is usually dynamic and incomplete. Therefore, it is presenting a critical issue for knowledge mining, analysis and trending. For that reason, this work presents a knowledge acquisition and management platform based on the Internet of Things (IoT). IoT is presenting the capability to connect any object to Internet. These Smart Objects are providing an enormous quantity of data. This work is focused on the personal and mobile health areas, where the integration of clinical devices in the patient's environment, this will enable new services with capabilities to predict health anomalies in real time, send alerts, reminders and offer an enriched feedback to the patient. This feedback will help and motivate to the patients with the treatments adherence and compliance and to follow a healthy lifestyle. In order to reach these services and feedback capabilities, it is required a full integration of the clinical devices and efficient processing of the collected data. The presented knowledge acquisition and management architecture is composed of, on the one hand, a gateway and a personal clinical device used for the integration of clinical devices, wireless transmission of continuous vital signs through 6LoWPAN, and patient identification through RFID. On the other hand, it is complemented with a data model and pre-processing module called YOAPY, which analyses the data from the sensors at the personal devices level. This offers an enriched data to the knowledge-based systems, and this also aggregate the data acquired in order to improve the performance of the communications to the constrains from the Smart Objects in aspects such as low bandwidth, frame size and power consumption. This architecture is being evaluated with an extended set of sensors required for patients with breathing problem in the framework of the AIRE project, it has evaluated the capabilities to provide knowledge acquisition and management from continuous monitored vital signs, the capabilities for diagnosis and detection of anomalies, and finally the support for security and privacy.