Syed Aziz Shah

Buried Object Sensing Considering Curved Pipeline

This letter presents design and implementation of a system solution, where light weight wireless devices are used to identify a moving object within underground pipeline for maintenance and inspection. The devices such as transceiver operating at S-band are deployed for underground settings. Finer-grained channel information in conjunction with leaky-wave cable (LWC) detects any moving entity. The processing of the measured data over time is analyzed and used for reporting the disturbances. Deploying an LWC as the receiver has benefits in terms of a wider coverage area, covering blind and semiblind zones. The system fully exploits the variances of both amplitude and phase information of channel information as the performance indicators for motion detection. The experimental results demonstrate greater level of accuracy.

Monitoring of atopic dermatitis using leaky coaxial cable

In our daily life, inadvertent scratching may increase the severity of skin diseases (such as atopic dermatitis etc.). However, people rarely pay attention to this matter, so the known measurement behaviour of the movement is also very little. Nevertheless, the behaviour and frequency of scratching represent the degree of itching, and the analysis of scratching frequency is helpful to the doctors clinical dosage. In this Letter, a novel system is proposed to monitor the scratching motion of a sleeping human body at night. The core device of the system is just a leaky coaxial cable (LCX) and a router. Commonly, LCX is used in the blind field or semi-blindfield in wireless communication. The new idea is that the leaky cable is placed on the bed, and then the state information of physical layer of wireless communication channels is acquired to identify the scratching motion and other small body movements in the human sleep process. The results show that it can be used to detect the movement and its duration. Channel state information (CSI) packet is collected by card installed in the computer based on the 802.11n protocol. The characterisation of the scratch motion in the collected CSI is unique, so it can be distinguished from the wireless channel amplitude variation trend.

Utilizing a 5G spectrum for health care to detect the tremors and breathing activity for multiple sclerosis

Utilizing fifth‐generation (5G) sensing in the health care sector with increased capacity and massive spectrum range increases the quality of health care monitoring systems. In this paper, 5G C‐band sensing operating at 4.8 GHz is used to monitor a particular body motion of multiple sclerosis patients, especially the tremors and breathing patterns. The breathing pattern obtained using 5G C‐band technology is compared with the invasive breathing sensor to monitor the subtle chest movements caused due to respiration. The 5G C‐band has a huge spectrum from 1 to 100 GHz, which enhances the capacity and performance of wireless communication by increasing the data rate from 20 Gb/s to 1 Tb/s. The system captures and monitors the wireless channel information of different body motions and efficiently identifies the tremors experienced since each body motion induces a unique imprint that is used for a particular purpose. Different machine learning algorithms such as support vector machine, k‐nearest neighbors, and random forest are used to classify the wireless channel information data obtained for various human activities. The values obtained using different machine learning algorithms for various performance metrics such as accuracy, precision, recall, specificity, Kappa, and F‐measure indicate that the proposed method can efficiently identify the particular conditions experienced by multiple sclerosis patients.