Sensors (Basel, Switzerland)
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Internet of Things (IoT) technology has recently been integrated with various healthcare devices to monitor patients' health status and share it with their healthcare practitioners. Since healthcare data often contain personal and sensitive information, healthcare systems must provide a secure user authentication scheme. ⋯ We propose a novel three-factor lightweight user authentication scheme that addresses these weaknesses and verifies the security of the proposed scheme using a formal verification tool called ProVerif. In addition, our proposed scheme outperforms other proposed symmetric encryption-based schemes or elliptic curve-based schemes.
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The coronavirus disease 2019 (COVID-19) pandemic is considered a public health emergency of international concern. The 2019 novel coronavirus (2019-nCoV) or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused this pandemic has spread rapidly to over 200 countries, and has drastically affected public health and the economies of states at unprecedented levels. ⋯ In this paper, an overview of the progress made in the development of nanobiosensors for the detection of human coronaviruses (SARS-CoV, SARS-CoV-2, and Middle East respiratory syndrome coronavirus (MERS-CoV) is presented, along with specific techniques for modifying the surface of nanobiosensors. The newest detection methods of the influenza virus responsible for acute respiratory syndrome were compared with conventional methods, highlighting the newest trends in diagnostics, applications, and challenges of SARS-CoV-2 (COVID-19 causative virus) nanobiosensors.
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In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients' vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. ⋯ The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration.
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Persistent gait alterations can occur after concussion and may underlie future musculoskeletal injury risk. We compared dual-task gait stability measures among adolescents who did/did not sustain a subsequent injury post-concussion, and uninjured controls. Forty-seven athletes completed a dual-task gait evaluation. ⋯ The concussion/subsequent injury group walked slower (0.76 ± 0.14 vs. 0.65 ± 0.13 m/s; p = 0.008) and demonstrated higher diagonal line length (0.67 ± 0.08 vs. 0.58 ± 0.05; p = 0.02) and trapping time (5.3 ± 1.5 vs. 3.8 ± 0.6; p = 0.006) than uninjured controls. Dual-task diagonal line length (hazard ratio =1.95, 95% CI = 1.05-3.60), trapping time (hazard ratio = 1.66, 95% CI = 1.09-2.52), and walking speed (hazard ratio = 0.01, 95% CI = 0.00-0.51) were associated with subsequent injury. Dual-task gait stability measures can identify altered movement that persists despite clinical concussion recovery and is associated with future injury risk.
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Kinetics data such as ground reaction forces (GRFs) are commonly used as indicators for rehabilitation and sports performance; however, they are difficult to measure with convenient wearable devices. Therefore, researchers have attempted to estimate accurately unmeasured kinetics data with artificial neural networks (ANNs). Because the inputs to an ANN affect its performance, they must be carefully selected. ⋯ The walking data from 17 participants walking at various speeds were used to train and validate the ANN. The estimated 3D GRF, CoP trajectory, and joint torques of the lower limbs were reasonably accurate, with normalized root-mean-square errors (NRMSEs) of 6.7% to 15.6%, 8.2% to 20.0%, and 11.4% to 24.1%, respectively. This result implies that the biomechanical characteristics can be used to estimate the complete three-dimensional gait data with an ANN model and a single IMU.