In this paper, an ultra-sensitive metamaterial terahertz sensor is proposed. The resonance sensor is designed based on a novel double corrugation form to enhance the ability of the sensor in the terms of sensitivity, Q-factor and the maximum sensible thickness of an analyte. The introduced structure can support the spoof surface plasmon and can resonate strongly at the tuned frequencies. Moreover, the structure of the terahertz sensor is investigated thoroughly from different points of view including frequency shifts due to variations in the thickness or refractive index of the analyte. In addition, the sensitivity of the sensor is approximated with a biharmonic fitting function for different combinations of refractive index and analyte thickness as “sensitivity surface”. The sensor shows the maximum sensitivity of 1.75 THz/RIU for refractive index between 1–1.2 with a maximum thickness of 80 μm. Moreover, the simulation results approved that the double corrugation on the metal stripe improves the electromagnetic field interaction in the metal part greatly in comparison with the previously reported works. According to this work, the proposed structure can be applied for terahertz sensing with more abilities to sense even thicker biologic tissues.

The monitoring of epileptic seizures is mainly done by means of electroencephalogram (EEG) monitoring. Although this method is accurate, it is not comfortable for the patient as the EEG-electrodes have to be attached to the scalp which hampers the patient's movement. This makes long-term home monitoring not feasible. In this paper, the aim is to propose a seizure detection system based on accelerometry for the detection of epileptic seizure. The used sensors are wireless, which can improve quality of life for the patients. In this system, three 2D accelerometer sensors are positioned on the right arm, left arm, and left thigh of an epileptic patient. Datasets from three patients suffering from severe epilepsy are used in this paper for the development of an automatic detection algorithm. This monitoring system is based on Wireless Sensor Networks and can determine the location of the patient when a seizure is detected and then send an alarm to hospital staff or the patient's relatives. Our wireless sensor nodes are MICAz Motes developed by Crossbow Technology. The proposed system can be used for patients living in a clinical environment or at their home, where they do only their daily routines. The analysis of the recorded data is done by an Artificial Neural Network and K Nearest-Neighbor to recognize seizure movements from normal movements. The results show that K Nearest Neighbor performs better than Artificial Neural Network for detecting these seizures. The results also show that if at least 50% of the signal consists of seizure samples, we can detect the seizure accurately. In addition, there is no need for training the algorithm for each new patient.