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.

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.

LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes

This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall detection.It balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks.The proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly recognition.The model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and falls.This study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.

Vision Based Real Time Monitoring System for Elderly Fall Event Detection Using Deep Learning

Human fall detection plays a vital part in the design of sensor based alarming system,aid physical therapists not only to lessen after fall effect and also to save human life.Accurate and timely identification can offer quick medical ser-vices to the injured people and prevent from serious consequences.Several vision-based approaches have been developed by the placement of cameras in diverse everyday environments.At present times,deep learning(DL)models par-ticularly convolutional neural networks(CNNs)have gained much importance in the fall detection tasks.With this motivation,this paper presents a new vision based elderly fall event detection using deep learning(VEFED-DL)model.The proposed VEFED-DL model involves different stages of operations namely pre-processing,feature extraction,classification,and parameter optimization.Primar-ily,the digital video camera is used to capture the RGB color images and the video is extracted into a set of frames.For improving the image quality and elim-inate noise,the frames are processed in three levels namely resizing,augmenta-tion,and min–max based normalization.Besides,MobileNet model is applied as a feature extractor to derive the spatial features that exist in the preprocessed frames.In addition,the extracted spatial features are then fed into the gated recur-rent unit(GRU)to extract the temporal dependencies of the human movements.Finally,a group teaching optimization algorithm(GTOA)with stacked autoenco-der(SAE)is used as a binary classification model to determine the existence of fall or non-fall events.The GTOA is employed for the parameter optimization of the SAE model in such a way that the detection performance can be enhanced.In order to assess the fall detection performance of the presented VEFED-DL model,a set of simulations take place on the UR fall detection dataset and multi-ple cameras fall dataset.The experimental outcomes highlighted the superior per-formance of the presented method over the recent methods.

An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network (ANN) and Support Vector Machine (SVM) Algorithms

In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Gait Analysis Sensor (WGAS). In order to perform automatic fall detection, we used Back Propagation Artificial Neural Network (BP-ANN) and Support Vector Machine (SVM) based on the 6 features extracted from the raw data. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller, is worn by the subjects at either T4 (at back) or as a belt-clip in front of the waist during the various tests. The raw data is wirelessly transmitted from the WGAS to a near-by PC for real-time fall classification. The BP ANN is optimized by varying the training, testing and validation data sets and training the network with different learning schemes. SVM is optimized by using three different kernels and selecting the kernel for best classification rate. The overall accuracy of BP ANN is obtained as 98.20% with LM and RPROP training from the T4 data, while from the data taken at the belt, we achieved 98.70% with LM and SCG learning. The overall accuracy using SVM was 98.80% and 98.71% with RBF kernel from the T4 and belt position data, respectively.

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.

Learning Based Falling Detection Using Multiple Doppler Sensors

Automated falling detection is one of the important tasks in this ageing society. Such systems are supposed to have little interference on daily life. Doppler sensors have come to the front as useful?devices to detect human activity without using any wearable sensors. The conventional Doppler sensor based falling detection mechanism uses the features of only one sensor. This paper presents falling detection using multiple Doppler sensors. The resulting data from sensors are combined or selected to find out the falling event. The combination method, using three sensors, shows 95.5% accuracy of falling detection. Moreover, this method compensates the drawbacks of mono Doppler sensor which encounters problems when detecting movement orthogonal to irradiation directions.

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.

社区老年人跌倒风险感知潜在剖面分析及影响因素研究

目的 分析社区老年人跌倒风险感知的潜在剖面类别及影响因素,为制订老年人安全活动方案从而降低跌倒风险提供参考。方法 2023年2-8月,采用便利抽样法选取合肥市3个社区的常住老年人423例为研究对象,采用一般资料问卷、跌倒风险感知量表、老化态度问卷、智力状态量表、修订版跌倒效能量表、体能状况量表和焦虑抑郁量表对其进行调查。运用潜在剖面分析和多元Logistic回归分析确定不同剖面及其影响因素。结果 社区老年人跌倒风险感知分为低跌倒风险感知型、中度跌倒风险感知型和高跌倒风险感知型3个潜在剖面类别,其影响因素包括锻炼爱好、健康自评、身体活动功能、老化态度、焦虑抑郁、跌倒效能等(均P<0.05)。结论 社区老年人跌倒风险感知存在异质性,医护人员应根据社区老年人跌倒风险感知不同剖面的分类特征,制订针对性的干预措施,以降低跌倒风险。

基于深度学习的老人摔倒检测设计

老年人摔倒而未被及时发现已经成为危害老人生命安全的一个重大因素。随着我国老年人的生命安全保障问题越来越被重视,为了及时发现老人在家摔倒从而能尽早得到救治,提出了一种基于改进的YOLOv5目标检测算法的老人摔倒识别检测设计。通过实验证明,该设计提升了算法识别精度,降低了漏检频率,使得其具有更好的识别检查功能。

基于深度学习的毫米波雷达人体摔倒检测系统研究

针对现有摔倒检测系统难以完成全天时检测、存在侵犯被检测人隐私的问题,该文设计了一种基于深度学习的毫米波雷达人体摔倒检测系统,包括信号采集、训练数据生成、智能检测和显示与告警四个部分。该系统利用1642毫米波雷达采集数据,对数据进行短时傅里叶变换,经数据增强后构建时频图数据集,通过ResNet101网络进行动作检测。检测为摔倒动作后,向远程接收端发送报警信息。该系统能够检测摔倒、弯腰、下蹲三种动作。实测结果表明,检测准确率为94.3%。

基于毫米波雷达技术的智能居家养老研究

随着国内人口老龄化的加剧以及国人居家养老的文化传承,智能居家养老研究领域备受关注。文章基于毫米波雷达技术,研究了智能居家养老的相关问题。首先,分析了国内人口老龄化及居家养老的需求;其次,介绍了毫米波雷达技术的基本原理、技术特点和应用优势;再次,探讨了毫米波雷达在智能居家养老中的应用,包括跌倒检测、睡眠监测、行为分析等方面;最后,分析了智能居家养老系统的架构和实现方法,包括系统的硬件设计、软件设计、数据处理等方面。

水泥混凝土路面板底脱空无损检测技术研究

为了进一步明确落锤式弯沉仪(FWD)和探地雷达(GPR)进行水泥混凝土路面板底脱空的无损检测方法和判断标准,基于2种方法的测试原理分析,分别提出了弯沉比值法和雷达信号畸变特征分析法进行路面脱空判定,并对两种脱空检测技术进行了工程应用。结果表明,两种检测技术判定结果具有较好的一致性,由于GPR检测速度快且为连续性检测,更适合运营道路水泥混凝土路面板底脱空检测。

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.

Design of a Smart Sole with Advanced Fall Detection Algorithm

Fall has become the second leading cause of unintentional injury, death, after road traffic injuries, for the elderly in Europe. This proportion will increase in the next decades and become more than ever a real public health issue. France was selected by the World Health Organization to be the first country to implement a program that reduces the coverage of the dependence. Commercial automatic fall detection devices can help seniors get back on their feet faster by reducing the time of emergency procedure. Many seniors do not take advantage of this potentially life-saving technology mainly because of intrusiveness constraints. After having reminded the context and the challenges of fall detection systems, this paper presents an original device which is unobtrusive, comfortable and very effective. The hardware architecture embedded into the sole and a new fall detection algorithm based on acceleration and time thresholds are presented. The algorithm introduces a new concept of differential acceleration to eliminate some drawbacks of current systems. Tests were carried out under real life conditions by 6 young participants for different ADLs. The data were analyzed blindly. We compared the detected falls and found a 100% sensibility and more than 93% sensitivity for all participants and scenarios.

一种可穿戴式跌倒检测装置设计

跌倒造成的人身意外事故,尤其是老年人跌倒造成的意外伤害,引起人们的极大关注。通过对跌倒行为特性的研究,设计了一种基于压力传感器的便携装置,进行跌倒检测。装置采用薄膜式压力传感器,将传感器安置于鞋垫,用于采集人体运动中的脚底压力信息,采用阈值分析与支持向量机算法相结合的方法对脚底压力值进行数据处理,判断人体是否跌倒。本装置通过实验测试验证,判断跌倒具有较高的可靠性和准确性。

基于多摄像头监控的人体跌倒检测算法

人口老龄化使得空巢老人数量越来越多。跌倒作为威胁独居老人生命安全的主要因素受到了社会的广泛关注。为保护老人的生命健康不受威胁,本文提出一种基于计算机视觉的两级人体跌倒检测算法。从监控摄像机中采集视频数据,对其做前景提取,通过形态学操作为前景块绘制矩形边界,根据矩形宽高比从中筛选出所有可能是跌倒的行为,这是粗粒度级检测。之后再用统计学方法对第一级检测出的前景块绘制椭圆边界,分析其形态变化,最终检测出跌倒行为,这是细粒度级检测。本文在开源多摄像头跌倒数据集上进行了评估。仿真实验和与当前的先进方法的对比表明本文算法取得了非常好的效果。

穿戴式跌倒检测中特征向量的提取和降维研究

穿戴式跌倒检测中老年人特征属性过多会造成维数灾难,影响后续跌倒检测精度。针对此问题,首先采用时域分析法提取初始特征向量集,用提出的改进核主成分分析算法(IKPCA)对特征向量进行降维,从而获得优质的特征向量集,使得后续的分类具有更好的效果。IKPCA算法首先利用I-RELIEF算法对初始特征向量集进行特征选择,然后计算跌倒特征向量的信息度量和相似度度量;最后根据跌倒特征向量的相似度度量剔除无效的跌倒特征向量。IKPCA算法不但保持核主成分分析算法(KPCA)较好的降维能力,而且扩充了较好的分类能力。利用真实的数据集进行实验,对比分析表明,相比其他算法,IKPCA算法能够得到更优质的特征向量数据集。

基于排列组合熵和加权核Fisher的肌电跌倒检测

为实现老年人的跌倒与日常行为动作的模式识别,提出了一种基于排列组合熵和加权核Fisher线性判别的表面肌电信号跌倒识别方法.以腓肠肌和股外侧肌2路肌电信号对应的排列组合熵为特征向量输入加权核Fisher线性分类器进行模式识别,对跌倒与坐下、蹲下和行走进行识别.实验结果表明,该方法的跌倒识别率为93.33%,特异度100%,优于其他分类方法.

基于SVM和阈值分析法的摔倒检测系统

随着我国人口老龄化的快速发展,老年人口呈现出高龄化、空巢化的趋势。当老年人在家中发生意外跌倒而未能及时获得救助时,会给老年人造成严重的身心伤害。针对这个问题,设计并实现老年人摔倒检测系统。该系统以嵌入式微处理器K60核心开发板作为处理内核,加速度传感器MMA7660FC采集人体三轴加速度信息,ENC-03陀螺仪采集两轴角速度信息。通过基于支持向量机(SVM)和阈值分析法的摔倒检测算法判断是否摔倒,在摔倒时能自动地发送摔倒报警信息。实验结果表明,系统能有效地区分摔倒和其他日常生活行为,算法准确度高、实时性高。