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.

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.

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.

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.

WiSFall:一种非接触式的浴室跌倒监测模型

封闭浴室场景中的跌倒行为危害性大,救助及时性差,对其高效便捷的监测研究有重要意义.针对现有基于Wi-Fi信号感知的跌倒算法中存在的特征提取不足、识别精度有限问题,本文提出了一种基于深度学习的非接触式浴室跌倒监测模型WiSFall.首先,WiSFall将一维时序CSI数据流重构为二维的频率能量图形式,使得感知数据的特征容纳能力得以增强;其次,对重构后的感知数据进行巴特沃斯滤波,以去除环境噪声;最后,将滤波后的感知数据以频率能量图形式输入到构建好的深度学习模型中,通过对感知特征的有效提取和分类实现Wi-Fi环境下高精度的浴室跌倒监测.实验结果表明,WiSFall在居家浴室环境下对跌倒行为监测的准确率达到99.63%,相比同类模型表现更好,且具有较强泛化能力.

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.

Assessment of Falls among the Elderly in the Emergency Department of the Idrissa Pouye General Hospital in Dakar, Senegal: A Cohort of 100 Cases

Background: Falls in the elderly are a global public health problem with serious medical and socio-economic consequences, especially in low and middle-income countries. The aim of this study was to describe the characteristics of falls among the elderly in trauma units in Senegal. Materials and Methods: This was a descriptive, prospective study from April 20, 2022 to October 30, 2022 among people aged at least 60 and admitted to the surgical emergency department of Idrissa Pouye Hospital in Dakar following a fall. Socio-demographic, clinical, therapeutic and evolutionary characteristics were collected and analyzed using Sphinx Plus 2 and Excel 2019 for Windows software. Results: Out of 730 elderly people seen during this period, 100 met the criteria, representing a prevalence of 13.69%. The average consultation time was 3.25 +/? 4 days. The average age was 73 +/? 8.43 years, with women predominating (74%). Medical expenses were mainly covered by the family (73%). Most falls occurred during the day (68%), at home (82%), especially in the bedroom (30%), with stumbling (32%) as the main mechanism. The majority of patients (86%) spent less than 30 minutes on the floor. Predisposing factors were dominated by visual disorders (56%) and precipitating factors were mainly environmental (62%). Geriatric syndromes were dominated by frailty (22%). Complications were dominated by fractures (86%), and almost half (47%) had lost their autonomy for post-fall Activities Daily Living (ADL). Prescription medication was almost systematic (98%), dominated by analgesics (98%). Surgery was indicated in 58% of patients. The average waiting time for surgery was 25.36 +/? 19 days. A death rate of 1% was recorded in the emergency department. Conclusion: Falls in the elderly are a frequent occurrence in traumatological emergencies, with etiological factors that are often multiple and interrelated, leading to significant morbidity. Raising awareness among people at risk and setting up an orthogeriatric service would help prevent falls and optimize care in the short and long term.

Discrepancy between Patients’ and Nurses’ Estimates of Patients’ Activities of Daily Living for Fall Risk Assessment: A Quantitative Observational Study

Background: Patient falls are a serious problem in a rehabilitation unit. Although patient falls have been described in the healthcare literature for more 60 years, and many risk assessment tools have been developed, the rate of falls in hospitals in Japan has remained unchanged for the last 8 years. A previous study reported that about 50% of patients in rehabilitation estimated their fall risk lower than that estimated by their nurses. We believe that patients in rehabilitation tend to overestimate their ability to perform ADLs. Aim: To identify discrepancies between patients’ and nurses’ estimates of patients’ ability to perform activities of daily living (ADL) and clarify any relationship between the discrepancies and patient falls. Methods: Participants comprised 82 patients (42 men) admitted to a rehabilitation unit in Osaka, Japan from July to December of 2017. Patients and their nurses answered the same questionnaire about patients’ ability to perform ADL. The questionnaire was developed based on the Functional Independence Measure (FIM) and administered at admission, at 1 month after admission, and at discharge. Participants were classified into the overestimating group and the accurately estimating/underestimating group, and groups were compared using Wilcoxon rank-sum tests. Results: The mean age of participants was 76.4 years. At admission, approximately 72% of participants estimated their own ability to perform ADL higher than did the nurses. The percentage of overestimating participants dropped to 30% at discharge. Fifteen of the participants experienced a fall;all were in the overestimating group. The ADL Discrepancy and fall-assessment scores for these 15 participants were significantly higher than those of other participants. Conclusions: There are discrepancies between patients’ and nurses’ estimates of patients’ ability to perform ADL and had important significance for assessing their risk of fall. And minimizing the discrepancy may support the prevention of falls.

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%.

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.

Relationship of Microvascular Obstruction with Global and Regional Myocardial Function Determined by Cardiac Magnetic Resonance after ST-Segment Elevation Myocardial Infarction

Objective To investigate the impact of microvascular obstruction(MVO)on the global and regional myocardial function by cardiac magnetic resonance feature-tracking(CMR-FT)in ST-segment-elevation myocardial infarction(STEMI)patients after percutaneous coronary intervention.Methods Consecutive acute STEMI patients who underwent cardiac magnetic resonance imaging 1-7 days after successful reperfusion by percutaneous coronary intervention treatment were included in this retrospective study.Based on the presence or absence of MVO on late gadolinium enhancement images,patients were divided into groups with MVO and without MVO.The infarct zone,adjacent zone,and remote zone were determined based on a myocardial 16-segment model.The radial strain(RS),circumferential strain(CS),and longitudinal strain(LS)of the global left ventricle(LV)and the infarct,adjacent,and remote zones were measured by CMRFT from cine images and compared between patients with and without MVO using independent-samples t-test.Logistic regression analysis was used to assess the association of MVO with the impaired LV function.Results A total of 157 STEMI patients(mean age 56.66±11.38 years)were enrolled.MVO was detected in 37.58%(59/157)of STEMI patients,and the mean size of MVO was 3.00±3.76 mL.Compared with patients without MVO(n=98),the MVO group had significantly reduced LV global RS(t=-4.30,P<0.001),global CS(t=4.99,P<0.001),and global LS(t=3.51,P=0.001).The RS and CS of the infarct zone in patients with MVO were significantly reduced(t=-3.38,P=0.001;t=2.64,P=0.01;respectively)and the infarct size was significantly larger(t=8.37,P<0.001)than that of patients without MVO.The presence of LV MVO[OR=4.10,95%CI:2.05-8.19,P<0.001]and its size[OR=1.38,95%CI:1.10-1.72,P=0.01],along with the heart rate and LV infarct size were significantly associated with impaired LV global CS in univariable Logistic regression analysis,while only heart rate(OR=1.08,95%CI:1.03-1.13,P=0.001)and LV infarct size(OR=1.10,95%CI:1.03-1.16,P=0.003)were independent influencing factors for the impaired LV global CS in multivariable Logistic regression analysis.Conclusion The infarct size was larger in STEMI patients with MVO,and MVO deteriorates the global and regional LV myocardial function.

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.

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.

Pre-Impact and Impact Fall Detection Based on a Multimodal Sensor Using a Deep Residual Network

Falls are the contributing factor to both fatal and nonfatal injuries in the elderly.Therefore,pre-impact fall detection,which identifies a fall before the body collides with the floor,would be essential.Recently,researchers have turned their attention from post-impact fall detection to pre-impact fall detection.Pre-impact fall detection solutions typically use either a threshold-based or machine learning-based approach,although the threshold value would be difficult to accu-rately determine in threshold-based methods.Moreover,while additional features could sometimes assist in categorizing falls and non-falls more precisely,the esti-mated determination of the significant features would be too time-intensive,thus using a significant portion of the algorithm’s operating time.In this work,we developed a deep residual network with aggregation transformation called FDSNeXt for a pre-impact fall detection approach employing wearable inertial sensors.The proposed network was introduced to address the limitations of fea-ture extraction,threshold definition,and algorithm complexity.After training on a large-scale motion dataset,the KFall dataset,and straightforward evaluation with standard metrics,the proposed approach identified pre-impact and impact falls with high accuracy of 91.87 and 92.52%,respectively.In addition,we have inves-tigated fall detection’s performances of three state-of-the-art deep learning models such as a convolutional neural network(CNN),a long short-term memory neural network(LSTM),and a hybrid model(CNN-LSTM).The experimental results showed that the proposed FDSNeXt model outperformed these deep learning models(CNN,LSTM,and CNN-LSTM)with significant improvements.

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.

Fish Swimming amid Falling Flowers中的中国绘画元素

中国传统文化传播深刻影响了欧美诗人的诗歌创作,如霍华德·奈莫洛夫受中国传统绘画《落花游鱼图》的影响创作了美国现代诗歌Fish Swimming amid Falling Flowers。霍华德·奈莫洛夫受中国传统绘画的影响主要体现在诗歌创作中对物象移用、意境营造、重返自然、诗意栖居、万物相融、和谐共存的哲思。

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%.

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.

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.

Research on Fall Detection System Based on Commercial Wi-Fi Devices

Falls are a major cause of disability and even death in the elderly,and fall detection can effectively reduce the damage.Compared with cameras and wearable sensors,Wi-Fi devices can protect user privacy and are inexpensive and easy to deploy.Wi-Fi devices sense user activity by analyzing the channel state information(CSI)of the received signal,which makes fall detection possible.We propose a fall detection system based on commercial Wi-Fi devices which achieves good performance.In the feature extraction stage,we select the discrete wavelet transform(DWT)spectrum as the feature for activity classification,which can balance the temporal and spatial resolution.In the feature classification stage,we design a deep learning model based on convolutional neural networks,which has better performance compared with other traditional machine learning models.Experimental results show our work achieves a false alarm rate of 4.8%and a missed alarm rate of 1.9%.