Ultra-Wideband Radar-Based Indoor Activity Monitoring for Elderly Care

Multicohort cross-sectional study of cognitive and behavioural digital biomarkers in neurodegeneration: the Living Lab Study protocol

INTRODUCTION AND AIMS

Digital biomarkers can provide a cost-effective, objective and robust measure for neurological disease progression, changes in care needs and the effect of interventions. Motor function, physiology and behaviour can provide informative measures of neurological conditions and neurodegenerative decline. New digital technologies present an opportunity to provide remote, high-frequency monitoring of patients from within their homes. The purpose of the living lab study is to develop novel digital biomarkers of functional impairment in those living with neurodegenerative disease (NDD) and neurological conditions.

METHODS AND ANALYSIS

The Living Lab study is a cross-sectional observational study of cognition and behaviour in people living with NDDs and other, non-degenerative neurological conditions. Patients (n≥25 for each patient group) with dementia, Parkinson's disease, amyotrophic lateral sclerosis, mild cognitive impairment, traumatic brain injury and stroke along with controls (n≥60) will be pragmatically recruited. Patients will carry out activities of daily living and functional assessments within the Living Lab. The Living Lab is an apartment-laboratory containing a functional kitchen, bathroom, bed and living area to provide a controlled environment to develop novel digital biomarkers. The Living Lab provides an important intermediary stage between the conventional laboratory and the home. Multiple passive environmental sensors, internet-enabled medical devices, wearables and electroencephalography (EEG) will be used to characterise functional impairments of NDDs and non-NDD conditions. We will also relate these digital technology measures to clinical and cognitive outcomes.

ETHICS AND DISSEMINATION

Ethical approvals have been granted by the Imperial College Research Ethics Committee (reference number: 21IC6992). Results from the study will be disseminated at conferences and within peer-reviewed journals.

Ultra-Wideband (UWB) Systems in Biomedical Sensing

The extremely low power transmission levels of ultra-wideband (UWB) technology, alongside its advantageously large bandwidth, make it a prime candidate for being used in numerous healthcare scenarios, which require short-range high-data-rate communications and safe radar-based applications [...].

Improved Extreme Learning Machine Based UWB Positioning for Mobile Robots with Signal Interference

For the purpose of tackling ultra-wideband (UWB) indoor positioning with signal interference, a binary classifier for signal interference discrimination and positioning errors compensation model combining genetic algorithm (GA) and extreme learning machine (ELM) are put forward. Based on the distances between four anchors and the target which are calculated with time of flight (TOF) ranging technique, GA-ELM-based binary classifier for judging the existence of signal interference, and GA-ELM-based positioning errors compensation model are built up to compensate for the result of the preliminary evaluated positioning model. Finally, the datasets collected in the actual scenario are used for verification and analysis. The experimental results indicate that the root-mean-square error (RMSE) of positioning without signal interference is 14.5068 cm, which is reduced by 71.32% and 59.72% compared with those results free of compensation and optimization, respectively. Moreover, the RMSE of positioning with signal interference is 28.0861 cm, which is decreased by 64.38% and 70.16%, in comparison to their counterparts without compensation and optimization, respectively. Consequently, these calculated results of numerical examples lead to the conclusion that the proposed method displays its wide application, high precision and rapid convergence in improving the positioning accuracy for mobile robots.

Sinuous Antenna for UWB Radar Applications

In this paper, the recent progress on sinuous antennas is detailed, focusing the attention on the antenna geometry, dielectric structure, and miniaturization techniques. In the first part, we introduce the basic principles of the frequency-independent antenna, in particular the self-complementary and log-periodic geometries, as well as the antenna geometries, all characterized in terms of angles. The operating principles, main advantages, system design considerations, limits, and challenges of conventional sinuous antennas are illustrated. Second, we describe some technical solutions aimed to ensure the optimal trade-off between antenna size and radiation behavior. To this aim, some special modification of the antenna geometry based on the meandering as well as on the loading with dielectric structures are presented. Moreover, the cavity backing technique is explained in detail as a method to achieve unidirectional radiation. Third, we present a new class of supershaped sinuous antenna based on a suitable merge of the 2D superformula and the sinuous curve. The effect of the free parameters change on the antenna arm geometry as well as the performance improvement in terms of directivity, beam stability, beam angle, gain, and radiating efficiency are highlighted.

Wise Information Technology of Med: Human Pose Recognition in Elderly Care

The growing problem of aging has led to a social concern on how to take care of the elderly living alone. Many traditional methods based on visual cameras have been used in elder monitoring. However, these methods are difficult to be applied in daily life, limited by high storage space with the camera, low-speed information processing, sensitivity to lighting, the blind area in vision, and the possibility of revealing privacy. Therefore, wise information technology of the Med System based on the micro-Doppler effect and Ultra Wide Band (UWB) radar for human pose recognition in the elderly living alone is proposed to effectively identify and classify the human poses in static and moving conditions. In recognition processing, an improved PCA-LSTM approach is proposed by combing with the Principal Component Analysis (PCA) and Long Short Term Memory (LSTM) to integrate the micro-Doppler features and time sequence of the human body to classify and recognize the human postures. Moreover, the classification accuracy with different kernel functions in the Support Vector Machine (SVM) is also studied. In the real experiment, there are two healthy men and one woman (22-26 years old) selected to imitate the movements of the elderly and slowly perform five postures (from sitting to standing, from standing to sitting, walking in place, falling and boxing). The experimental results show that the resolution of the entire system for the five actions reaches 99.1% in the case of using Gaussian kernel function, so the proposed method is effective and the Gaussian kernel function is suitable for human pose recognition.