Fall into beauty陷入美丽之中

雅诗兰黛公司2004秋季彩妆的色彩灵感来自表现主义的摇滚风格，其中两个新产品特别体现这一主题。这两款名为Pure Color Eyeliner眼线和Pure Color LipTint唇彩的新产品．拥有丰富绚烂的色彩。

Numerical and experimental study on the falling film flow characteristics with the effect of co-current gas flow in hydrogen liquefaction process

Liquid hydrogen storage and transportation is an effective method for large-scale transportation and utilization of hydrogen energy. Revealing the flow mechanism of cryogenic working fluid is the key to optimize heat exchanger structure and hydrogen liquefaction process(LH2). The methods of cryogenic visualization experiment, theoretical analysis and numerical simulation are conducted to study the falling film flow characteristics with the effect of co-current gas flow in LH2spiral wound heat exchanger.The results show that the flow rate of mixed refrigerant has a great influence on liquid film spreading process, falling film flow pattern and heat transfer performance. The liquid film of LH2mixed refrigerant with column flow pattern can not uniformly and completely cover the tube wall surface. As liquid flow rate increases, the falling film flow pattern evolves into sheet-column flow and sheet flow, and liquid film completely covers the surface of tube wall. With the increase of shear effect of gas-phase mixed refrigerant in the same direction, the liquid film gradually becomes unstable, and the flow pattern eventually evolves into a mist flow.

Fuzzy inference system using genetic algorithm and pattern search for predicting roof fall rate in underground coal mines

One of the most dangerous safety hazard in underground coal mines is roof falls during retreat mining.Roof falls may cause life-threatening and non-fatal injuries to miners and impede mining and transportation operations.As a result,a reliable roof fall prediction model is essential to tackle such challenges.Different parameters that substantially impact roof falls are ill-defined and intangible,making this an uncertain and challenging research issue.The National Institute for Occupational Safety and Health assembled a national database of roof performance from 37 coal mines to explore the factors contributing to roof falls.Data acquired for 37 mines is limited due to several restrictions,which increased the likelihood of incompleteness.Fuzzy logic is a technique for coping with ambiguity,incompleteness,and uncertainty.Therefore,In this paper,the fuzzy inference method is presented,which employs a genetic algorithm to create fuzzy rules based on 109 records of roof fall data and pattern search to refine the membership functions of parameters.The performance of the deployed model is evaluated using statistical measures such as the Root-Mean-Square Error,Mean-Absolute-Error,and coefficient of determination(R_(2)).Based on these criteria,the suggested model outperforms the existing models to precisely predict roof fall rates using fewer fuzzy rules.

A Novel Fall Detection Framework Using Skip-DSCGAN Based on Inertial Sensor Data

With the widespread use of Internet of Things(IoT)technology in daily life and the considerable safety risks of falls for elderly individuals,research on IoT-based fall detection systems has gainedmuch attention.This paper proposes an IoT-based spatiotemporal data processing framework based on a depthwise separable convolution generative adversarial network using skip-connection(Skip-DSCGAN)for fall detection.The method uses spatiotemporal data from accelerometers and gyroscopes in inertial sensors as input data.A semisupervised learning approach is adopted to train the model using only activities of daily living(ADL)data,which can avoid data imbalance problems.Furthermore,a quantile-based approach is employed to determine the fall threshold,which makes the fall detection frameworkmore robust.This proposed fall detection framework is evaluated against four other generative adversarial network(GAN)models with superior anomaly detection performance using two fall public datasets(SisFall&MobiAct).The test results show that the proposed method achieves better results,reaching 96.93% and 92.75% accuracy on the above two test datasets,respectively.At the same time,the proposed method also achieves satisfactory results in terms ofmodel size and inference delay time,making it suitable for deployment on wearable devices with limited resources.In addition,this paper also compares GAN-based semisupervised learning methods with supervised learning methods commonly used in fall detection.It clarifies the advantages of GAN-based semisupervised learning methods in fall detection.

Ambulance Records as an Information Source to Document and Prevent Fall Injuries in Sweden

In Sweden, there has been only limited documentation for injuries requiring ambulance responses. The main objective of this study is, through the use of historic data, to assess the suitability of ambulance records to describe circumstances with fall injuries. Methods: The injury events data around patients were sourced from the ambulance data register. Descriptive statistics were used to describe injured patients based on age group, type of injury, place of injury, injury mechanism and consequence of an injury event. Two-group comparison was performed with Pearson’s chi-squared. Predictors of transport to hospital were identified using logistic regression analyses. Result: Ambulance provides unique data on all injury events, with direct implications for translational research, public policy and clinical practice (safety promotion). In 2002 ambulance attended 3964 injured people which represents 14% of ambulance attended workload in Värmland county, Sweden. The most common trauma location was the traffic area followed by residential area and nursing home. These three injury sites account for 2320 cases (61.6%). The most common cause of injury was falls (63.9%) followed by contact with another person (26.5%). Contact with another person is the most common site of injury in the traffic area (79.5%), and men aged 25-66 years are overrepresented. Conclusion: Logistic regression found that, age-group and place code were significant predictor for being attended by ambulance. Traffic, home and nursing homes were over-represented injury environments (61.6%) that require special attention. Most injury cases occur in the home and nursing homes among people over 67 years of age. Surprisingly, most of the injury events in the traffic environment are about hitting another person. Paramedics can provide rich and valuable data on injury events. Registration of such data is entirely possible and desirable, and can be used in preventive work.

Factors associated with the incidence of patient falls in hospitals:A scoping review

When a patient falls within a hospital setting,there is a significant increase in the risk of severe injury or health complications.Recognizing factors associated with such falls is crucial to mitigate their impact on patient safety.This review seeks to analyze the factors contributing to patient falls in hospitals.The main goal is to enhance our understanding of the reasons behind these falls,enabling hospitals to devise more effective prevention strategies.This study reviewed literature published from 2013 to 2022,using the Arksey and O’Malley methodology for a scoping review.The research literature was searched from seven databases,namely,PubMed,ScienceDirect,Wiley Library,Garuda,Global Index Medicus,Emerald Insight,and Google Scholar.The inclusion criteria comprised both qualitative and quantitative primary and secondary data studies centered on hospitalized patients.Out of the 893 studies analyzed,23 met the criteria and were included in this review.Although there is not an abundance of relevant literature,this review identified several factors associated with falls in hospitals.These encompass environmental,patient,staff,and medical factors.This study offers valuable insights for hospitals and medical personnel aiming to enhance fall prevention practices.Effective prevention efforts should prioritize early identification of patient risk factors,enhancement of the care environment,thorough training for care staff,and vigilant supervision of high-risk patients.By comprehending the factors that contribute to patient falls,hospitals can bolster patient safety and mitigate the adverse effects of falls within the health-care setting.

A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.

Analysis of the Clinical Interventions for Falls in Elderly Patients in the Community From 2002 To 2022:A Bibliometric Analysis

Objective:To analyze and provide a comprehensive overview of the knowledge structure and research hotspots of clinical interventions for falls in elderly patients in the community.Methods:The search for publications related to clinical interventions for falls in elderly patients in the community from 2002 to 2022 was conducted on the Web of Science Core Collection(WoSCC)database.VOSviewers,CiteSpace,and the R package“bibliometrix”were used to conduct this bibliometric analysis.Results:2091 articles from 70 countries,primarily the United States and Australia,were included.The number of publications related to clinical interventions for falls in elderly patients is increasing yearly.The main research institutions in this field were the University of Sydney,Harvard University,and the University of California.BioMed Central(BMC)Geriatrics was the most popular journal in this field and Journals of the American Geriatrics Society was the most co-cited journal.These publications came from 8984 authors among which author Lord SR had published the most papers and author Tinetti Me had the most co-citations.The main keywords in this research field were“balance,”“exercise,”and“risk factor.”Conclusion:This was the first bibliometric study that comprehensively summarized the research hot spots and development of clinical interventions for falls in elderly patients in the community.This paper aims to provide a reference for scholars and researchers in this particular field.

Construction and verification of a model for predicting fall risk in patients with maintenance hemodialysis

Objective:To construct a risk prediction model for fall in patients with maintenance hemodialysis(MHD)and to verify the prediction effect of the model.Methods:From June 2020 to December 2020,307 patients who underwent MHD in a tertiary hospital in Chengdu were divided into a fall group(32 cases)and a non-fall group(275 cases).Logistic regression analysis model was used to establish the influencing factors of the subjects.Hosmer–Lemeshow and receiver operating characteristic(ROC)curve were used to test the goodness of fit and predictive effect of the model,and 104 patients were again included in the application research of the model.Results:The risk factors for fall were history of falls in the past year(OR=3.951),dialysis-related hypotension(OR=6.949),time up and go(TUG)test(OR=4.630),serum albumin(OR=0.661),frailty(OR=7.770),and fasting blood glucose(OR=1.141).Hosmer–Lemeshow test was P=0.475;the area under the ROC curve was 0.907;the Youden index was 0.642;the sensitivity was 0.843;and the specificity was 0.799.Conclusions:The risk prediction model constructed in this study has a good effect and can provide references for clinical screening of fall risks in patients with MHD.

Effectiveness of Extended Care in Preventing Falls in Disabled Elderly and Its Impact on Quality of Life

Objective:To analyze the value of extended care interventions for disabled elderly in preventing falls and optimizing quality of life.Methods:A sample of 60 cases of disabled elderly in a tertiary hospital from May 2022 to May 2023 was selected and grouped by the random number table method.The observation group received extended care,while the control group adopted routine care.The differences in complication rate,fall rate,36-Item Short Form Health Survey(SF-36)score,health knowledge awareness score,and nursing satisfaction were compared.Results:The complication rate and fall rate of the disabled elderly in the observation group were lower than those in the control group,P<0.05;the SF-36 score,health knowledge score,and nursing satisfaction of the observation group were higher than those of the control group,P<0.05.Conclusion:Extended care for the disabled elderly can reduce the risk of falls and complications related to disability,as well as optimize their cognition and improve their quality of life,which is efficient and feasible.

Numerical Investigations on Harbor Oscillations Induced by Falling Objects

In this paper,the open-sourced computational fluid dynamics software,OpenFOAM~?,is used to study the fluctuation phenomenon of the water body inside a horizontally one-dimensional enclosed harbor basin with constant water depth triggered by falling wedges with various horizontal falling positions,initial falling velocities and masses.Based on both Fourier transfo rm analysis and wavelet spectrum analysis for the time series of the free surface elevations inside the harbor basin,it is found for the first time that the wedge falling inside the harbor can directly trigger harbor resonance.The influences of the three factors(including the horizontal falling position,the initial falling velocity,and the mass)on the response amplitudes of the lowest three resonant modes are also investigated.The results show that when the wedge falls on one of the nodal points of a resonant mode,the mode would be remarkably suppressed.Conversely,when the wedge falls on one of the anti-nodal points of a resonant mode,the mode would be evidently triggered.The initial falling velocity of the wedge mainly has a remarkable effect on the response amplitude of the most significant mode,and the latter shows a gradual increase trend with the increase of the former.While for the other two less significant modes,their response amplitudes fluctuate around certain constant values as the initial falling velocity rises.In general,the response amplitudes of all the lowest three modes are shown to gradually increase with the mass of the wedge.

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes.These sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during emergencies.Falling is one of the most important problems confronted by older people and people with movement disabilities.Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people.But,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor environments.Currently,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements.Against this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)model.The presented IWODL-FDDP model aims to identify the fall events to assist disabled people.The presented IWODLFDDP model applies an image filtering approach to pre-process the image.Besides,the EfficientNet-B0 model is utilized to generate valuable feature vector sets.Next,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall events.Finally,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the work.The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest model.The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks.Moreover,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer.The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a smartwatch.It includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and fall.Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural networks.By considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

Deep Transfer Learning Driven Automated Fall Detection for Quality of Living of Disabled Persons

Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.

Influencing factors of fear of falling among glaucoma patients in west China:a cross-sectional study

AIM:To investigate the current situation and influencing factors of fear of falling in glaucoma patients in western China.METHODS:In this cross-sectional study,glaucoma patients treated in the Ophthalmology Department of West China Hospital of Sichuan University were conducted to investigate the demographic data,visual acuity,visual field,activities of daily living,risk of falling,fear of falling and psychological states.Generalized linear model was used for multivariate analysis with fear of falling as dependent variable and other factors as independent variables.RESULTS:The mean score of the Chinese version modified Fall Efficacy Scale(MFES)was 7.52±2.09 points.Univariate analysis and multivariate analysis showed that the history of falls within one year,visual acuity,visual field,risk of falling,activities of daily living and psychological states had statistically difference on fear of falling(P<0.05).CONCLUSION:Glaucoma patients in west China have relatively high risk of fear of falling.History of falling within 1y,severe visual function impairment,high risk of falling,incapable of independence of daily living,and abnormal psychological state are risk factors of fear of falling among glaucoma patients.

Teamwork Optimization with Deep Learning Based Fall Detection for IoT-Enabled Smart Healthcare System

The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart healthcare.Healthcare services could be enhanced by incorporating key techniques like AI and IoT.The convergence of AI and IoT provides distinct opportunities in the medical field.Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population.Therefore,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support.Lately,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home care.This article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare system.Initially,the presented TWODL-FDSHS technique exploits IoT devices for the data collection process.Next,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature extraction.At last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS technique.The experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.

Dense Spatial-Temporal Graph Convolutional Network Based on Lightweight OpenPose for Detecting Falls

Fall behavior is closely related to high mortality in the elderly,so fall detection becomes an important and urgent research area.However,the existing fall detection methods are difficult to be applied in daily life due to a large amount of calculation and poor detection accuracy.To solve the above problems,this paper proposes a dense spatial-temporal graph convolutional network based on lightweight OpenPose.Lightweight OpenPose uses MobileNet as a feature extraction network,and the prediction layer uses bottleneck-asymmetric structure,thus reducing the amount of the network.The bottleneck-asymmetrical structure compresses the number of input channels of feature maps by 1×1 convolution and replaces the 7×7 convolution structure with the asymmetric structure of 1×7 convolution,7×1 convolution,and 7×7 convolution in parallel.The spatial-temporal graph convolutional network divides the multi-layer convolution into dense blocks,and the convolutional layers in each dense block are connected,thus improving the feature transitivity,enhancing the network’s ability to extract features,thus improving the detection accuracy.Two representative datasets,Multiple Cameras Fall dataset(MCF),and Nanyang Technological University Red Green Blue+Depth Action Recognition dataset(NTU RGB+D),are selected for our experiments,among which NTU RGB+D has two evaluation benchmarks.The results show that the proposed model is superior to the current fall detection models.The accuracy of this network on the MCF dataset is 96.3%,and the accuracies on the two evaluation benchmarks of the NTU RGB+D dataset are 85.6%and 93.5%,respectively.

Developed Fall Detection of Elderly Patients in Internet of Healthcare Things

Falling is among the most harmful events older adults may encounter.With the continuous growth of the aging population in many societies,developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential.This paper presents a new healthcare Internet of Health Things(IoHT)architecture built around an ensemble machine learning-based fall detection system(FDS)for older people.Compared to deep neural networks,the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters.The number of cascaded random forest stages is automatically optimized.This study uses a public dataset of fall detection samples called SmartFall to validate the developed fall detection system.The SmartFall dataset is collected based on the acquired measurements of the three-axis accelerometer in a smartwatch.Each scenario in this dataset is classified and labeled as a fall or a non-fall.In comparison to the three machine learning models—K-nearest neighbors(KNN),decision tree(DT),and standard random forest(SRF),the proposed ensemble classifier outperformed the other models and achieved 98.4%accuracy.The developed healthcare IoHT framework can be realized for detecting fall accidents of older people by taking security and privacy concerns into account in future work.

The development of anti-fall functional clothing for elderly

Objective:The consequences of falls in the elderly are severe,ranging from skin abrasion to hip fracture,which is very easy to cause death.Using advanced technology to develop anti-fall clothing that meets the needs of the elderly can play a significant role in protecting the elderly.By reviewing and analyzing the existing literature on the importance of fall protection clothing in reducing falls and protecting the body of the elderly,it is hoped to explore further research that needs improvement.Methods:Guided by the preferred reporting items for systematic reviews and meta-analyses,eight related studies were identified through Web of Science,Scopus and Chinese National Knowledge Infrastructure.The research objects,approaches,material and equipment,protection principle,and survey results are extracted.Results:Two articles verified the fall detection algorithm adopted in the research through experiments,which significantly improved fall detection accuracy.Six papers found that selecting appropriate cushioning materials can effectively reduce the consequences of falls of the elderly through experimental comparative analysis.Finally,three attributes for significant design value are drawn:(1)size and fit;(2)cushioning materials;(3)wearable sensing elements.

Automated Disabled People Fall Detection Using Cuckoo Search with Mobile Networks

Falls are the most common concern among older adults or disabled peo-ple who use scooters and wheelchairs.The early detection of disabled persons’falls is required to increase the living rate of an individual or provide support to them whenever required.In recent times,the arrival of the Internet of Things(IoT),smartphones,Artificial Intelligence(AI),wearables and so on make it easy to design fall detection mechanisms for smart homecare.The current study devel-ops an Automated Disabled People Fall Detection using Cuckoo Search Optimi-zation with Mobile Networks(ADPFD-CSOMN)model.The proposed model’s major aim is to detect and distinguish fall events from non-fall events automati-cally.To attain this,the presented ADPFD-CSOMN technique incorporates the design of the MobileNet model for the feature extraction process.Next,the CSO-based hyperparameter tuning process is executed for the MobileNet model,which shows the paper’s novelty.Finally,the Radial Basis Function(RBF)clas-sification model recognises and classifies the instances as either fall or non-fall.In order to validate the betterment of the proposed ADPFD-CSOMN model,a com-prehensive experimental analysis was conducted.The results confirmed the enhanced fall classification outcomes of the ADPFD-CSOMN model over other approaches with an accuracy of 99.17%.