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.

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%.

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.

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.

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.

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.

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%.

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%.

A Portable Fall Detection and Alerting System Based on k-NN Algorithm and Remote Medicine

This paper presents information on a portable fall detection and alerting system mainly consisting of a custom vest and a mobile smart phone. A wearable motion detection sensor integrated with tri-axial accelerometer, gyroscope and Bluetooth is built into a custom vest worn by elderly. The vest can capture the reluctant acceleration and angular velocity about the activities of daily living(ADLs) of elderly in real time. The data via Bluetooth is then sent to a mobile smart phone running a fall detection program based on k-NN algorithm. When a fall occurs the phone can alert a family member or health care center through a call or emergent text message using a built in Global Positioning System. The experimental results show that the system discriminates falls from ADLs with a sensitivity of 95%, and a specificity of 96.67%. This system can provide remote monitoring and timely help for the elderly.

3D depth image analysis for indoor fall detection of elderly people

This paper presents a new fall detection method of etderly people in a room environment based on shape analysis of 3D depth images captured by a Kinect sensor. Depth images are pre- processed by a median filter both for background and target. The sithouette of moving individual in depth images is achieved by a subtraction method for background frames. The depth images are converted to disparity map, which is obtained by the horizontal and vertical projection histogram statistics. The initial floor plane information is obtained by V disparity map, and the floor ptane equation is estimated by the least square method. Shape information of human subject in depth images is analyzed by a set of moment functions. Coefficients of ellipses are calculated to determine the direction of individual The centroids of the human body are catculated and the angle between the human body and the floor plane is calculated. When both the distance from the centroids of the human body to the floor plane and the angle between the human body and the floor plane are tower than some threshotds, fall incident will be detected. Experiments with different failing direction are performed. Experimental results show that the proposed method can detect fall incidents effectively.

Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System

Human fall detection(FD)acts as an important part in creating sensor based alarm system,enabling physical therapists to minimize the effect of fall events and save human lives.Generally,elderly people suffer from several diseases,and fall action is a common situation which can occur at any time.In this view,this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection(IAOA-DLFD)model to identify the fall/non-fall events.The proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image quality.Besides,the IAOA with Capsule Network based feature extractor is derived to produce an optimal set of feature vectors.In addition,the IAOA uses to significantly boost the overall FD performance by optimal choice of CapsNet hyperparameters.Lastly,radial basis function(RBF)network is applied for determining the proper class labels of the test images.To showcase the enhanced performance of the IAOA-DLFD technique,a wide range of experiments are executed and the outcomes stated the enhanced detection outcome of the IAOA-DLFD approach over the recent methods with the accuracy of 0.997.

Improve the Accuracy of Fall Detection Based on Artificial Intelligence Algorithm

This work presents a fall detection system based on artificial intelligence.The system incorporates miniature wearable devices for fall detection.Fall detection is achieved by integrating a three-axis gyroscope and a threeaxis accelerometer.The system gathers the differential data collected by the gyroscope and accelerometer,applies artificial intelligence algorithms for model training and constructs an effective model for fall detection.To provide easywearing and effective position detection,it is designed as a small device attached to the user’swaist.Experiment results have shown that the accuracy of the proposed fall detection model is up to 98%,demonstrating the effectiveness of the model in real-life fall detection.

Research on Fall Detection Based on Improved Human Posture Estimation Algorithm

According to recent research statistics,approximately 30%of people who experienced falls are over the age of 65.Therefore,it is meaningful research to detect it in time and take appropriate measures when falling behavior occurs.In this paper,a fall detection model based on improved human posture estimation algorithm is proposed.The improved human posture estimation algorithm is implemented on the basis of Openpose.An im-proved strategy based on depthwise separable convolution combined with HDC structure is proposed.The depthwise separable convolution is used to replace the convolution neural network structure,which makes the network lightweight and reduces the redundant layer in the network.At the same time,in order to ensure that the image features are not lost and ensure the accuracy of detecting human joint points,HDC structure is introduced.Experiments show that the improved algorithm with HDC structure has higher accuracy in joint point detection.Then,human posture estimation is applied to fall detection research,and fall event modeling is carried out through fall feature extraction.The designed convolution neural network model is used to classify and distinguish falls.The experimental results show that our method achieves 98.53%,97.71%and 97.20%accuracy on three public fall detection data sets.Compared with the experimental results of other methods on the same data set,the model designed in this paper has a certain improvement in system accuracy.The sensitivity is also improved,which will reduce the error detection probability of the system.In addition,this paper also verifies the real-time performance of the model.Even if researchers are experimenting with low-level hardware,it can ensure a certain detection speed without too much delay.

Fall detection system in enclosed environments based on single Gaussian model

In this paper,we propose an efficient fall detection system in enclosed environments based on single Gaussian model using the maximum likelihood method.Online video clips are used to extract the features from two cameras.After the model is constructed,a threshold is set,and the probability for an incoming sample under the single Gaussian model is compared with that threshold to make a decision.Experimental results show that if a proper threshold is set,a good recognition rate for fall activities can be achieved.

Elderly Fall Detection Based on Improved SSD Algorithm

We propose an improved a single-shot detector(SSD)algorithm to detect falls of the elderly.The VGG16 network part of the SSD network is replaced with the MobilenetV2 network.At the same time,we change the infrastructure of MobilenetV2 network,the three layers that were not down-sampled at the end were removed,which can make the model structure simpler and faster to detect.The complete Intersection-over-Union(CIoU)loss function is introduced to get a good regression of the target borders that have different sizes and different proportions.We use Feature Pyramid Network(FPN)for up-sampling,it can fuse low-level feature maps with high resolution and high-level feature maps with rich semantic information.For sampling results,we use the Secure Shell(SSH)module to extract different receptive fields,which improves the detection accuracy.Our model ensures that the accuracy of the elderly fall detection remains unchanged,but it greatly improves the detection speed that only takes 10 milliseconds to detect a picture.

Elderly Fall Detection by Sensitive Features Based on Image Processing and Machine Learning

The world’s elderly population is growing every year.It is easy to say that the fall is one of the major dangers that threaten them.This paper offers a Trained Model for fall detection to help the older people live comfortably and alone at home.The purpose of this paper is to investigate appropriate methods for diagnosing falls by analyzing the motion and shape characteristics of the human body.Several machine learning technologies have been proposed for automatic fall detection.The proposed research reported in this paper detects a moving object by using a background subtraction algorithm with a single camera.The next step is to extract the features that are very important and generally describe the human shape and show the difference between the human falls from the daily activities.These features are based on motion,changes in human shape,and oval diameters around the human and temporal head position.The features extracted from the human mask are eventually fed in to various machine learning classifiers for fall detection.Experimental results showed the efficiency and reliability of the proposed method with a fall detection rate of 81%that have been tested with UR Fall Detection dataset.

Deep learning-based fall detection using commodity Wi-Fi

As the phenomenon of an aging population gradually becomes common worldwide,the pressure on the elderly has seen a notable increase.To address this challenge,fall detection systems are important in ensuring the safety of the elderly population,particularly those living alone.Wi-Fi sensing,as a privacy-preserving method of perception,can be deployed indoors for detecting human activities such as falls,based on the reflective properties of electromagnetic waves.Signals generated by transmitters experience reflections from various objects within indoor environments,leading to distinct propagation paths.These signals eventually aggregate at the receiver,incorporating details about the objects’orientation and their activity states.In this study,within practical experimental environments,we collect dataset and utilize deep learning method to classify the falling events.

Automatic Fall Detection Using Membership Based Histogram Descriptors

t We propose a framework for automatic fall detection based on video visual feature extraction. The proposed approach relies on a membership histogram descriptor that encodes the visual properties of the video frames. This descriptor is obtained by mapping the original low-level visual features to a more discriminative descriptor using possibilistic memberships. This mapping can be summarized in two main phases. The first one consists in categorizing the low-level visual features of the video frames arid generating homogeneous clusters in an unsupervised way. The second phase uses the obtained membership degrees generated by the clustering process to compute the membership based histogram descriptor （MHD）. For the testing stage, the proposed fall detection approach categorizes unlabeled videos as ＂Fall＂ or ＂Non-Fall＂ scene using a possibilistic K-nearest neighbors classifier. The proposed approach is assessed using standard videos dataset that simulates patient fall. Also, we compare its performance with that of state-of-the-art fall detection techniques.