Emotion recognition becomes an important aspect in the development of human-machine interaction (HMI) systems. Positive emotions impact our lives positively, whereas negative emotions may cause a reduction in productivity. Emotionally intelligent systems such as chatbots and artificially intelligent assistant modules help make our daily life routines effortless. Moreover, a system which is capable of assessing the human emotional state would be very helpful to assess the mental state of a person. Hence, preventive care could be offered before it becomes a mental illness or slides into a state of depression. Researchers have always been curious to find out if a machine could assess human emotions precisely. In this work, a unimodal emotion classifier system in which one of the physiological signals, an electrocardiogram (ECG) signal, has been used is proposed to classify human emotions. The ECG signal was acquired using a capacitive sensor-based non-contact ECG belt system. The machine-learning-based classifiers developed in this work are SVM and random forest with 10-fold cross-validation on three different sets of ECG data acquired for 45 subjects (15 subjects in each age group). The minimum classification accuracies achieved with SVM and RF emotion classifier models are 86.6% and 98.2%, respectively.

In this work, simulation modelling of pyramidal horn antennas using "Antenna Magus" and "ANSYS Maxwell HFSS" Software for an operating frequency of 24 GHz is accomplished. The antenna design is done specifically to act as a transmitting and receiving antenna for biomedical signal processing in bio-radar systems. The design parameters were optimized to offer maximum efficiency and maximum gain without compromising other parameters. The horn was first designed in Antenna Magus Software with standard horn parameters and then the design parameters were evaluated and it was modified for optimal responses in ANSYS Maxwell HFSS Software. The brass sheet of 3mm thickness was selected to design pyramidal horns. A rectangular waveguide WR-42 was designed for the selected operating frequency range with some design tweaks to arrive at an optimized gain and reflection coefficient. The responses from both the software were recorded and analyzed. The maximum gain and directivity obtained for pyramidal horn are 25 dBi and 23.5 dB respectively. The value of the return loss S 11 parameter of -30 dB at 24GHz indicates the suitability of the proposed design in biomedical signal processing radar systems. The proposed design offers a decent gain of 25 dB, BW of 9 GHz and efficiency of 95%.