The accuracy of smartphone-based positioning systems using WiFi usually suffers from ranging errors caused by non-line-of-sight (NLOS) conditions. Previous research usually exploits several distribution features from a long time series (hundreds of samples) of WiFi received signal strength (RSS) or WiFi round-trip time (RTT) to achieve a high identification accuracy. However, the long time series or large sample size attributes to high power and time consumption in data collection for both training and testing. This will also undoubtedly be detrimental to user experience as the waiting time for getting enough samples is quite long. Therefore, this paper proposes three new real-time NLOS/LOS identification methods for smartphone-based indoor positioning systems using WiFi RSS and RTT distance measurement (RDM). Based on our extensive analysis of RSS and RDM dispersion features, three machine learning algorithms were chosen and developed to separate the samples for NLOS/LOS conditions. Experiments show that our best method achieves a discrimination accuracy of over 96% with a sample size of 10. Considering the theoretically shortest WiFi ranging interval of 100ms of the RTT-enabled smartphones, our algorithm is able to provide the shortest latency of 1s to get the testing result among all of the state-of-art methods.

Alzheimer’s disease is a lifelong progressive neurological disorder. It is associated with high disease management and caregiver costs. Intelligent sensing systems have the capability to provide context-aware adaptive feedback. These can assist Alzheimer’s patients with, continuous monitoring, functional support and timely therapeutic interventions for whom these are of paramount importance. This review aims to present a summary of such systems reported in the extant literature for the management of Alzheimer’s disease. Four databases were searched, and 253 English language articles were identified published between the years 2015 to 2020. Through a series of filtering mechanisms, 20 articles were found suitable to be included in this review. This study gives an overview of the depth and breadth of the efficacy as well as the limitations of these intelligent systems proposed for Alzheimer’s. Results indicate two broad categories of intelligent technologies, distributed systems and self-contained devices. Distributed systems base their outcomes mostly on long-term monitoring activity patterns of individuals whereas handheld devices give quick assessments through touch, vision and voice. The review concludes by discussing the potential of these intelligent technologies for clinical practice while highlighting future considerations for improvements in the design of these solutions for Alzheimer’s disease.

Digital Twin (DT) in Healthcare 4.0 (H4.0) presents a digital model of the patient with all its biological properties and characteristics. One of the application areas is patient respiration monitoring for enhanced patient care and decision support to healthcare professionals. Obtrusive methods of patient monitoring create hindrances in the patient’s daily routine. This research presents a novel DT model (ResDT) based on Wi-Fi Carrier State Information (CSI), improved signal processing, and Machine Learning (ML) algorithms for monitoring and classification (binary and multi-class) of patient respiration. A Wi-Fi sensor ESP32 with Wi-Fi CSI was utilized for the collection of respiration data. This provides an added advantage of unobtrusive monitoring of patient vital signs. The Patient’s Breaths Per Minute (BPM) is estimated from raw sensor data through the integration of multiple signal processing methodologies for denoising (smoothing and filtering) and dimensionality reduction (PCA, SVM, EMD, EMD-PCA). Multiple filters and dimensionality reduction methodologies are compared for accurate BPM estimation. The elliptical filter provides a relatively better estimation of the BPM with 87.5% accurate estimation as compared to other bandpass filters such as Butterworth (BF), Chebyshev type 1 Filter (CH1), Chebyshev type 2 Filter (CH2), and wavelet Decomposition (62.5%, 75%, 68.75%, and 75% respectively). Principal Component Analysis (PCA) was performed to provide better dimensionality reduction with 87.5% accurate BPM values compared to EMD, SVD, and EMD-PCA (57%, 44%, and 44% respectively). Additionally, the fine tree algorithm, from the implemented 21 ML supervised classification algorithms with K-fold crossvalidation, was observed to be the optimal choice for multi-class and binary-class classification problems in the presented ResDT model with 96.9% and 95.8% accuracy respectively.

Alzheimer's disease is the most common form of neurodegenerative disease and a leading cause of dementia today. A pathological effect of Alzheimer's disease is brain atrophy, which is the progressive shrinkage of the brain volume and weight. Another effect of Alzheimer's disease is the enlargement of lateral ventricles in the brain. Currently, MRI and CT scanners can detect and show images of the brain during different stages of Alzheimer's disease. However, its limited accessibility, high costs, and static structure make it inconvenient for some to use. This paper presents the design and novel application of a wearable device comprising of flexible microwave antennas, with an operating frequency range of 800 MHz to 2.5 GHz, that detects the progression of brain atrophy and lateral ventricle enlargement in patients with Alzheimer's at the earliest stage possible. The operating principle of the antennas are simulated in near field using CST and the device is experimentally validated using lamb brain samples and samples representing cerebral spinal fluid (CSF). The measured reflection coefficients and transmission coefficients were found to correlate with changes in brain volume and changes in CSF volume successfully, thus giving an indication of the progression of Alzheimer's disease in a patient.

Arguably, 5G and next-generation technology with its key features (specifically, supporting high data rates and high mobility platforms) make it valuable for coping with the emerging needs of medical healthcare. A 5G-enabled portable device receives the sensitive detection signals from the head imaging system and transmits them over the 5G network for real-time monitoring, analysis, and storage purposes. In terms of material, graphene-based flexible electronics have become very popular for wearable and healthcare devices due to their exceptional mechanical strength, thermal stability, high electrical conductivity, and biocompatibility. A graphene-based flexible antenna for data communication from wearable head imaging devices over a 5G network was designed and modelled. The antenna operated at the 34.5 GHz range and was designed using an 18 µm thin graphene film for the conductive radiative patch and ground with electric conductivity of 3.5 × 10⁵ S/m. The radiative patch was designed in a fractal fashion to provide sufficient antenna flexibility for wearable uses. The patch was designed over a 1.5 mm thick flexible polyamide substrate that made the design suitable for wearable applications. This paper presented the 3D modelling and analysis of the 5G flexible antenna for communication in a digital care-home model. The analyses were carried out based on the antenna’s reflection coefficient, gain, radiation pattern, and power balance. The time-domain signal analysis was carried out between the two antennas to mimic real-time communication in wearable devices.