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- A review on methods for random motion detection and compensation in bio-radar systemsPublication . Gouveia, Carolina; Vieira, José; Pinho, PedroThe bio-radar system can measure vital signals accurately, by using the Doppler effect principle, which relates the received signal properties to the distance change between the radar antennas and the subject chest-wall. These systems have countless applications, from short range detection to assist in rescue missions, to long-term applications as for the continuous sleeping monitoring. Once the main applications of these systems intend to monitor subjects during long periods of time and under noisy environments, it is impossible to guarantee the patient immobilization, hence its random motion, as well as other clutter sources, will interfere in the acquired signals. Therefore, the signal processing algorithms developed for these applications have been facing several challenges regarding the random motion detection and mitigation. In this paper, an extended review on the already implemented methods is done, considering continuous wave radars. Several sources of random motion are considered, along with different approaches to compensate the distortions caused by them.
- Study on the usage feasibility of continuous-wave radar for emotion recognitionPublication . Gouveia, Carolina; Tome, Ana; Barros, Filipa; Soares, Sandra C.; Vieira, José; Pinho, PedroNon-contact vital signs monitoring has a wide range of applications, such as in safe drive and in health care. In mental health care, the use of non-invasive signs holds a great potential, as it would likely enhance the patient's adherence to the use of objective measures to assess their emotional experiences, hence allowing for more individualized and efficient diagnoses and treatment. In order to evaluate the possibility of emotion recognition using a non-contact system for vital signs monitoring, we herein present a continuous wave radar based on the respiratory signal acquisition. An experimental set up was designed to acquire the respiratory signal while participants were watching videos that elicited different emotions (fear, happiness and a neutral condition). Signal was registered using a radar-based system and a standard certified equipment. The experiment was conducted to validate the system at two levels: the signal acquisition and the emotion recognition levels. Vital sign was analysed and the three emotions were identified using different classification algorithms. Furthermore, the classifier performance was compared, having in mind the signal acquired by both systems. Three different classification algorithms were used: the support-vector machine, K-nearest neighbour and the Random Forest. The achieved accuracy rates, for the three-emotion classification, were within 60% and 70%, which indicates that it is indeed possible to evaluate the emotional state of an individual using vital signs detected remotely.