Past few years have witness a dramatic change in the field of terahertz (THz) technology. The recent advancements in the technology for generation, manipulation and detection exploiting THz radiation have brought revolution in the field. Many researchers around the world have been inspired by the potential of invaluable new applications of THz sensing for food and water contamination detection. The microbial pollution in water and food is one the crucial issues with regard to the sanitary state for drinking water and daily consumption of food. To address this risk, the detection of microbial contamination is of utmost importance since the consumption of insanitary or unhygienic food can lead to catastrophic illness. This paper presents a first-time review of the open literature focused on the advances in the THz sensing for microbiological contamination of food and water and state-of-theart network architectures, applications, industrial trends and recent developments. Finally, open challenges and future research directions are presented with in the field.

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Increasing prevalence of dementia has posed several challenges for care-givers. Patients suffering from dementia often display wandering behavior due to boredom or memory loss. It is considered to be one of the challenging conditions to manage and understand. Traits of dementia patients can compromise their safety causing serious injuries. This paper presents investigation into the design and evaluation of wandering scenarios with patients suffering from dementia using an S-band sensing technique. This frequency band is the wireless channel commonly used to monitor and characterize different scenarios including random, lapping, and pacing movements in an indoor environment. Wandering patterns are characterized depending on the received amplitude and phase information of that measures the disturbance caused in the ideal radio signal. A secondary analysis using support vector machine is used to classify the three patterns. The results show that the proposed technique carries high classification accuracy up to 90% and has good potential for healthcare applications.

A novel study on monitoring and analysis of debilitating condition of patients suffering from neurological disorder is presented. Parkinson's disease is characterized by limited motor-ability of a patient. Freezing-of-gait is a major non-motor condition among ageing patients and its evaluation can reduce the chances of any secondary disorders. In this work, amplitude and phase information of the radio signals observed for a fixed period of time are used to differentiate the motor and non-motor symptoms. The amplitude information is classified using support vector machine while linear transformation is applied to obtain sanitized phase information for detection. The proposed method is highly cost effective and very handy with minimum deployment overhead. The analysis shows that this method also offers a high accuracy level of around 99% based on the observation of a number of patients. These features make it an attractive solution for real-time patient monitoring systems.

Cerebellar dysfunction (CD) is a neurological disorder that involves a number of abnormalities that affect the movement of various parts of the body such as gait abnormality or tremors in limbs such as hands or feet while reaching out for something. A user-friend tool that can objectively evaluate the aforementioned body movements in CD patients can aid the clinicians for an objective assessment in clinical settings. The objective of this work is to develop a method that quantifies the gait abnormality and tremors in hand using S-Band sensing technique. The S-Band sensing essentially leverages small wireless devices such as network interface card, omnidirectional antenna and router operating at 2.4 GHz to record the wireless channel data. Specifically, the aim is to use the variances of amplitude and phase information induced due to the human body movements. Each body movement leaves a unique imprint in the form of wireless channel information that is used to identify abnormalities in body motions. The proposed framework applied a linear transformation on raw phase data for calibrations since the data retrieved using interface card contain noise and is inapplicable for motion detection. The support vector machine used to classify the data achieved high classification accuracy.