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Fall Detection Systems (FDS) are automated systems designed to detect falls experienced by older adults or individuals. Early or real-time detection of falls may reduce the risk of major problems. This literature review explores the current state of research on FDS and its applications. The review shows various types and strategies of fall detection methods. Each type of fall detection is discussed with its pros and cons. Datasets of fall detection systems are also discussed. Security and privacy issues related to fall detection systems are also considered in the discussion. The review also examines the challenges of fall detection methods. Sensors, algorithms, and validation methods related to fall detection are also talked over. This work found that fall detection research has gradually increased and become popular in the last four decades. The effectiveness and popularity of all strategies are also discussed. The literature review underscores the promising potential of FDS and highlights areas for further research and development.

Background: Due to educational, social and economic reasons, more and more women are delaying childbirth. However, advanced maternal age is associated with several adverse pregnancy outcomes, and in particular a high risk of Down’s syndrome (DS). Hence, it is increasingly important to be able to detect fetal Down’s syndrome (FDS). Methods: We developed an effective, highly sensitive, surface plasmon resonance (SPR) biosensor with biochemically amplified responses using carboxyl-molybdenum disulfide (MoS2) film. The use of carboxylic acid as a surface modifier of MoS2 promoted dispersion and formed specific three-dimensional coordination sites. The carboxylic acid immobilized unmodified antibodies in a way that enhanced the bioaffinity of MoS2 and preserved biorecognition properties of the SPR sensor surface. Complete antigen pregnancy-associated plasma protein-A2 (PAPP-A2) conjugated with the carboxyl-MoS2-modified gold chip to amplify the signal and improve detection sensitivity. This heterostructure interface had a high work function, and thus improved the efficiency of the electric field energy of the surface plasmon. These results provide evidence that the interface electric field improved performance of the SPR biosensor. Results: The carboxyl-MoS2-based SPR biosensor was used successfully to evaluate PAPP-A2 level for fetal Down’s syndrome screening in maternal serum samples. The detection limit was 0.05 pg/mL, and the linear working range was 0.1 to 1100 pg/mL. The women with an SPR angle > 46.57 m° were more closely associated with fetal Down’s syndrome. Once optimized for serum Down’s syndrome screening, an average recovery of 95.2% and relative standard deviation of 8.5% were obtained. Our findings suggest that carboxyl-MoS2-based SPR technology may have advantages over conventional ELISA in certain situations. Conclusion: Carboxyl-MoS2-based SPR biosensors can be used as a new diagnostic technology to respond to the increasing need for fetal Down’s syndrome screening in maternal serum samples. Our results demonstrated that the carboxyl-MoS2-based SPR biosensor was capable of determining PAPP-A2 levels with acceptable accuracy and recovery. We hope that this technology will be investigated in diverse clinical trials and in real case applications for screening and early diagnosis in the future.

It is challenging to control smoke in the case of a train fire occurred in the subway depot with a complex internal structure. In this paper, the effect of door opening state on smoke behavior characteristics induced by subway depot fire scenario was investigated. A series of numerical simulation were conducted by Fire Dynamics Simulator (FDS) software. Three key parameters were analyzed corresponding to the temperature, visibility and CO concentration for evaluating smoke propagation respectively. Results show that temperature distribution and CO concentration at 2 m height inside the train with train door opening state are lower than those with the train door closed. However, visibility at 2 m height inside the train shows the opposite trend. The results can provide reference in the ventilation system design and emergency evacuation scheme for the subway depot.

Objective: This study aims to investigate the potential of the Fire Dynamics Simulator (FDS) software, developed by the National Institute of Standards and Technology (NIST), as a tool to assess and improve Fire Safety (FS) in educational environments, specifically in the classrooms of CEFET in Araxá (MG), Brazil.   Theoretical Framework: The research is based on the importance of fire safety in educational environments, highlighting the vulnerability of children and young people. Concepts of fire behavior, smoke propagation, and thermal conditions in buildings are addressed.   Method: The methodology adopted for this research involves detailed computer simulations using FDS to analyze different fire scenarios. The simulation results were validated with the PyroSim software. Data collection was conducted through these simulations, generating robust empirical data that support the review and development of specific regulations.   Results and Discussion: The results indicate that FDS is an important ally in promoting fire safety in educational buildings. The simulations showed the effectiveness of FDS in predicting fire behavior, smoke propagation, and thermal conditions, helping to prevent and minimize potential damages. The discussion contextualizes these results, highlighting the need for accurate empirical data to strengthen Brazilian fire safety legislation.   Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied to positively influence legislation, optimize architectural designs, and promote safety guidelines in educational institutions in Brazil.   Originality/Value: This study significantly contributes to the advancement of knowledge and practices in FS. The originality of the research lies in the application of FDS for simulations in educational environments, offering a robust tool for evaluating and improving fire safety.

Given the high prevalence and detrimental effects of unintentional falls in the elderly, fall detection has become a pertinent public concern. A Fall Detection System (FDS) gathers information from sensors to distinguish falls from routine activities in order to provide immediate medical assistance. Hence, the integrity of collected data becomes imperative. Presence of missing values in data, caused by unreliable data delivery, lossy sensors, local interference and synchronization disturbances and so forth, greatly hamper the credibility and usefulness of data making it unfit for reliable fall detection. This paper presents a noise tolerant FDS performing in presence of missing values in data. The work focuses on Deep Learning (DL) particularly Recurrent Neural Networks (RNNs) with an underlying Bidirectional Long Short-Term Memory (BiLSTM) stack to implement FDS based on wearable sensors. The proposed technique is evaluated on two publicly available datasets—SisFall and UP-Fall Detection. Our system produces an accuracy of 97.21% and 97.41%, sensitivity of 96.97% and 99.77% and specificity of 93.18% and 91.45% on SisFall and UP-Fall Detection respectively, thus outperforming the existing state of the art on these benchmark datasets. The resultant outcomes suggest that the ability of BiLSTM to retain long term dependencies from past and future make it an appropriate model choice to handle missing values for wearable fall detection systems.

Abstract Study objectives To identify a fast and reliable method for rapid eye movement (REM) sleep without atonia (RWA) quantification. Methods We analyzed 36 video-polysomnographies (v-PSGs) of isolated REM sleep behavior disorder (iRBD) patients and 35 controls’ v-PSGs. Patients diagnosed with RBD had: i) RWA, quantified with a reference method, i.e. automatic and artifact-corrected 3-s Sleep Innsbruck Barcelona (SINBAR) index in REM sleep periods (RSPs, i.e. manually selected portions of REM sleep); and ii) v-PSG-documented RBD behaviors. We quantified RWA with other (semi)-automated methods requiring less human intervention than the reference one: the indices proposed by the SINBAR group (the 3-s and 30-s phasic flexor digitorum superficialis (FDS), phasic/”any”/tonic mentalis), and the REM atonia, short and long muscle activity indices (in mentalis/submentalis/FDS muscles). They were calculated in whole REM sleep (i.e. REM sleep scored following international guidelines), in RSPs, with and without manual artifact correction. Area under curves (AUC) discriminating iRBD from controls were computed. Using published cut-offs, the indices’ sensitivity and specificity for iRBD identification were calculated. Apnea-hypopnea index in REM sleep (AHIREM) was considered in the analyses. Results RWA indices from FDS muscles alone had the highest AUCs and all of them had 100% sensitivity. Without manual RSP selection and artifact correction, the “30-s phasic FDS” and the “FDS long muscle activity” had the highest specificity (85%) with AHIREM < 15/h. RWA indices were less reliable when AHIREM≥15/h. Conclusions If AHIREM<15/h, FDS muscular activity in whole REM sleep and without artifact correction is fast and reliable to rule out RWA.

To protect ancient buildings from fire, prevent the occurrences of fire, and minimise the losses caused by fire to the maximum extent possible, this study combined experimental measurements and numerical simulations to analyse the flame spread behaviour over wood treated with flame retardants. First, some wood blocks were treated with a nitrogen and phosphorus (water-based) flame retardant, and then a smoke combustion experiment was performed to test the smoke density. Scanning electron microscopy was also employed to observe the flame retardant effect. Next, a fire dynamic simulation software was used to simulate and analyse the results of the flame spread behaviour over the yellow pine collected from the Long’en Hall of Fuling Mausoleum, that was treated with flame retardants. The results showed the variation trend of the fire site’s heat release rate (HRR) over time to be consistent with the movement of fire spread after its occurrence. Compared with pristine wood, the flame retardant-treated wood exhibited a HRR reduction of 53.1%. The addition of flame retardants also reduced the concentration of the released smoke and CO 2 gas, decreased the temperature of the fire site, and enhanced visibility.