Augmented emergency lighting as a soft early warning: measures on road users when moving over by Björn Lidestam, Philip Lindblad, Petr Ondomiši, Emilia Sörengård, Kajsa Weibull

“… A novel interface to provide robust, intuitive, and timely alerts to road users to move over for emergency vehicles (EVs) was tested in a seven-minute driving-simulator experiment. …”
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RTLS-Enabled Bidirectional Alert System for Proximity Risk Mitigation in Tunnel Environments by Fatima Afzal, Farhad Ullah Khan, Ayaz Ahmad Khan, Ruchini Jayasinghe, Numan Khan

“…The results showed that the system accurately tracked proximity and generated timely alerts when safety thresholds were crossed, although minor delays of 5–8 s and slight positional inaccuracies were noted. …”
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Ensemble of deep learning and IoT technologies for improved safety in smart indoor activity monitoring for visually impaired individuals by Mesfer Al Duhayyim

“…For individuals with visual impairments, it enhances independence and safety in daily tasks while supporting caregivers with timely alerts and monitoring. This paper develops an Ensemble of Deep Learning for Enhanced Safety in Smart Indoor Activity Monitoring (EDLES-SIAM) technique for visually impaired people. …”
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Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning by Zakwan AlArnaout, Chamseddine Zaki, Yehia Kotb, Mouhammad AlAkkoumi, Nour Mostafa

“…These findings suggest that HRV-based drowsiness detection systems can be effectively integrated into Advanced Driver Assistance Systems (ADAS) to enhance driver safety by providing timely alerts, thereby reducing the risk of accidents caused by drowsiness.…”
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Real-time prediction of HFNC treatment failure in acute hypoxemic respiratory failure using machine learning by Xiaojie Li, Chunliang Jiang, Qingyan Xie, Huiquan Wang, Jiameng Xu, Guanjun Liu, Panpan Chang, Guang Zhang

“…This study introduces a machine learning model integrated with a dynamic real-time alert system for predicting HFNC treatment failure in AHRF patients, demonstrating improved performance over traditional indices in internal validation and showing potential for decision support in select healthcare settings.…”
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Smart diabetes management: remote monitoring and predictive health insights by K.S. Smelyakov, I.A. Lurin, K.V. Misiura, A.S. Chupryna, T.V. Tyzhnenko, O.D. Dolhanenko, V.M. Repikhov

“…GluComp integrates CGM systems with personalized machine learning models to deliver real-time alerts, predictive insights, and adaptive care. …”
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