Fangming Hu

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.

Adaptive Interference Cancellation of ECG Signals

As an important biological signal, electrocardiogram (ECG) signals provide a valuable basis for the clinical diagnosis and treatment of several diseases. However, its reference significance is based on the effective acquisition and correct recognition of ECG signals. In fact, this mV-level weak signal can be easily affected by various interferences caused by the power of magnetic field, patient respiratory motion or contraction, and so on from the sampling terminal to the receiving and display end. The overlapping interference affects the quality of ECG waveform, leading to the false detection and recognition of wave groups, and thus causing misdiagnosis or faulty treatment. Therefore, the elimination of the interference of the ECG signal and the subsequent wave group identification technology has been a hot research topic, and their study has important significance. Based on the above, this paper introduces two improved adaptive algorithms based on the classical least mean square (LMS) algorithm by introducing symbolic functions and block-processing concepts.

Patterns-of-Life Aided Authentication

Wireless Body Area Network (WBAN) applications have grown immensely in the past few years. However, security and privacy of the user are two major obstacles in their development. The complex and very sensitive nature of the body-mounted sensors means the traditional network layer security arrangements are not sufficient to employ their full potential, and novel solutions are necessary. In contrast, security methods based on physical layers tend to be more suitable and have simple requirements. The problem of initial trust needs to be addressed as a prelude to the physical layer security key arrangement. This paper proposes a patterns-of-life aided authentication model to solve this issue. The model employs the wireless channel fingerprint created by the user’s behavior characterization. The performance of the proposed model is established through experimental measurements at 2.45 GHz. Experimental results show that high correlation values of 0.852 to 0.959 with the habitual action of the user in different scenarios can be used for auxiliary identity authentication, which is a scalable result for future studies.

Biometric Behavior Authentication Exploiting Propagation Characteristics of Wireless Channel

Massive expansion of wireless body area networks (WBANs) in the field of health monitoring applications has given rise to the generation of huge amount of biomedical data. Ensuring privacy and security of this very personal data serves as a major hurdle in the development of these systems. An effective and energy friendly authentication algorithm is, therefore, a necessary requirement for current WBANs. Conventional authentication algorithms are often implemented on higher levels of the Open System Interconnection model and require advanced software or major hardware upgradation. This paper investigates the implementation of a physical layer security algorithm as an alternative. The algorithm is based on the behavior fingerprint developed using the wireless channel characteristics. The usability of the algorithm is established through experimental results, which show that this authentication method is not only effective, but also very suitable for the energy-, resource-, and interface-limited WBAN medical applications.

Double Threshold Authentication Using Body Area Radio Channel Characteristics

The demand of portable and body-worn devices for remote health monitoring is ever increasing. One of the major challenges caused by this influx of wireless body area network (WBAN) devices is security of users extremely vital and personal information. Conventional authentication techniques implemented at upper layers of the Open System Interconnection (OSI) model usually consumes huge amount of power. They also require significant changes at hardware and software levels. It makes them unsuitable for inherently low powered WBAN devices. This letter investigates the usability of a double threshold algorithm as a physical layer security measure in these scenarios. The algorithm is based on the users behavioral fingerprint extracted from the radio channel characteristics. Effectiveness of this technique is established through experimental measurements considering a variety of common usage scenarios. The results show that this method provides high level of security against false authentication attacks and has great potential in WBANs.

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.

A Novel Digital Phantom Using an Optical Noncontact Measurement System

Digital phantoms are vital for various biomedical researches. Traditional phantoms include theoretical models and voxel models reconstructed from medical images. It has been demonstrated that the homogeneous phantom filled with uniform tissue is accurate enough for wearable antenna design, body-centric channel modeling, etc. Therefore, it is interesting and necessary to investigate the novel approach of generating digital phantoms using an optical noncontact measurement system. In this letter, the point cloud data are first obtained; then, they are simplified via principal component analysis; finally, by applying surface reconstruction and mesh simplification techniques, a digital Chinese phantom is established. To verify the usability of the phantom, numerical calculation is performed to check E-fields at different positions on the body. Results sufficiently prove the feasibility of the train of thought presented in this letter.

Characterization of the on-body received signal strength indication considering different propagation environment

A method based on the received signal strength indicator (RSSI) for the characterization of wireless communication between on-body sensors in different propagation environments is presented in this paper [1]. The CC2431 sensors are employed to do the experimentation in indoor and outdoor environments in this study. These sensors are being used typically in ubiquitous sensor networks (USN) providing PHY layer functions for IEEE802.15.4 standard. They use a radio frequency (RF) transceiver for wireless communication at the 2.4GHz Industry Science Medical (ISM) band. Six on-body receivers and one transmitter replicating a typical body centric wireless network are used for the measurement campaign and impact of the varying environment on the RSSI value is analyzed.