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Neurological diseases including stroke and neurodegenerative disorders cause a hefty burden on the healthcare system. Survivors experience significant impairment in mobility and daily activities, which requires extensive rehabilitative interventions to assist them to regain lost skills and restore independence. The advent of remote rehabilitation architecture and enabling technology mandates the elaboration of sensing mechanisms tailored to individual clinical needs. This study aims to review current trends in the application of sensing mechanisms in remote monitoring and rehabilitation in neurological diseases, and to provide clinical insights to develop bespoke sensing mechanisms. A systematic search was performed using the PubMED database to identify 16 papers published for the period between 2018 to 2022. Teleceptive sensors (56%) were utilized more often than wearable proximate sensors (50%). The most commonly used modality was infrared (38%) and acceleration force (38%), followed by RGB color, EMG, light and temperature, and radio signal. The strategy adopted to improve the sensing mechanism included a multimodal sensor, the application of multiple sensors, sensor fusion, and machine learning. Most of the stroke studies utilized biofeedback control systems (78%) while the majority of studies for neurodegenerative disorders used sensors for remote monitoring (57%). Functional assessment tools that the sensing mechanism may emulate to produce clinically valid information were proposed and factors affecting user adoption were described. Lastly, the limitations and directions for further development were discussed. [ABSTRACT FROM AUTHOR]

Whereas modern digital cameras use a pixelated detector array to capture images, single-pixel imaging reconstructs images by sampling a scene with a series of masks and associating the knowledge of these masks with the corresponding intensity measured with a single-pixel detector. Though not performing as well as digital cameras in conventional visible imaging, single-pixel imaging has been demonstrated to be advantageous in unconventional applications, such as multi-wavelength imaging, terahertz imaging, X-ray imaging, and three-dimensional imaging. The developments and working principles of single-pixel imaging are reviewed, a mathematical interpretation is given, and the key elements are analyzed. The research works of three-dimensional single-pixel imaging and their potential applications are further reviewed and discussed. [ABSTRACT FROM AUTHOR]

Location-based services have permeated Smart academic institutions, enhancing the quality of higher education. Position information of people and objects can predict different potential requirements and provide relevant services to meet those needs. Indoor positioning system (IPS) research has attained robust location-based services in complex indoor structures. Unforeseeable propagation loss in complex indoor environments results in poor localization accuracy of the system. Various IPSs have been developed based on fingerprinting to precisely locate an object even in the presence of indoor artifacts such as multipath and unpredictable radio propagation losses. However, such methods are deleteriously affected by the vulnerability of fingerprint matching frameworks. In this paper, we propose a novel machine learning framework consisting of Bag-of-Features and followed by a k-nearest neighbor classifier to categorize the final features into their respective geographical coordinate data. BoF calculates the vocabulary set using k-mean clustering, where the frequency of the vocabulary in the raw fingerprint data represents the robust final features that improve localization accuracy. Experimental results from simulation-based indoor scenarios and real-time experiments demonstrate that the proposed framework outperforms previously developed models. [ABSTRACT FROM AUTHOR]

The sensitive detection of Pb2+ is of significant importance for food safety, environmental monitoring, and human health care. To this end, a novel fluorescent biosensor, DNAzyme-functionalized R-phycoerythrin (DNAzyme-R-PE), was presented for Pb2+ analysis. The biosensor was prepared via the immobilization of Iowa Black® FQ-modified DNAzyme–substrate complex onto the surface of SPDP-functionalized R-PE. The biosensor produced a minimal fluorescence signal in the absence of Pb2+. However, Pb2+ recognition can induce the cleavage of substrate, resulting in a fluorescence restoration of R-PE. The fluorescence changes were used to measure sensitively Pb2+ and the limit of detection was 0.16 nM with a linear range from 0.5–75 nM. Furthermore, the proposed biosensor showed excellent selectivity towards Pb2+ even in the presence of other metal ions interferences and was demonstrated to successfully determine Pb2+ in spiked lake water samples. [ABSTRACT FROM AUTHOR]