Adaptive window based fall detection using anomaly identification in fog computing scenario

2021 ◽  
Vol 17 (1) ◽  
pp. 15-37
Author(s):  
Rashmi Shrivastava ◽  
Manju Pandey
Keyword(s):  
Feature Extraction ◽  
Assisted Living ◽  
Detection System ◽  
Fog Computing ◽  
Ambient Assisted Living ◽  
Window Size ◽  
Fall Detection ◽  
Detection Methods ◽  
Support Vector ◽  
Adaptive Window

Human fall detection is a subcategory of ambient assisted living. Falls are dangerous for old aged people especially those who are unaccompanied. Detection of falls as early as possible along with high accuracy is indispensable to save the person otherwise it may lead to physical disability even death also. The proposed fall detection system is implemented in the edge computing scenario. An adaptive window-based approach is proposed here for feature extraction because window size affects the performance of the classifier. For training and testing purposes two public datasets and our collected dataset have been used. Anomaly identification based on a support vector machine with an enhanced chi-square kernel is used here for the classification of Activities of Daily Living (ADL) and fall activities. Using the proposed approach 100% sensitivity and 98.08% specificity have been achieved which are better when compared with three recent research based on unsupervised learning. One of the important aspects of this study is that it is also validated on actual real fall data and got 100% accuracy. This complete fall detection model is implemented in the fog computing scenario. The proposed approach of adaptive window based feature extraction is better than static window based approaches and three recent fall detection methods.

scholarly journals Intelligent fall detection system for eldercare

2014 ◽  
Author(s):  
◽  
Liang Liu
Keyword(s):  
Doppler Radar ◽  
Detection System ◽  
Fall Detection ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Training Dataset ◽  
Support Vector ◽  
Features Selection ◽  
Radar Sensor ◽  
Mel Frequency Cepstral Coefficients

Fall among elders is a main reason to cause accidental death among the population over the age 65 in United States. The fall detection methods have been brought into scene by implemented on different fall monitoring devices. For the advantages in privacy protection and non-invasive, independent of light, I design the fall detection system based on Doppler radar sensor. This dissertation explores different Doppler radar sensor configurations and positioning in both of the lab and real senior home environment, signal processing and machine learning algorithms. Firstly, I design the system based on the data collected with three configurations: two floor radars, one ceiling and one wall radars, one ceiling and one floor radars in lab. The performance of the sensor positioning and features are evaluated with classifiers: support vector machine, nearest neighbor, naïve Bayes, hidden Markov model. In the real senior home, I investigate the system by evaluating the detection variances caused by training dataset due to the variable subjects and environment settings. Moreover, I adjust the automatic fall detection system for the actual retired community apartment. I examine different features: Mel-frequency cepstral coefficients (MFCCs), local binary patterns (LBP) and the combined version of features with RELIEF algorithm. I also improve the detection performance with both pre-screener and features selection. I fuse the radar fall detection system with motion sensors. I develop a standalone fall detection system and generate a result to display on a designed webpage.

scholarly journals A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
R. Sekhar ◽  
K. Sasirekha ◽  
P. S. Raja ◽  
K. Thangavel
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Missing Values ◽  
Detection System ◽  
Radial Basis Function Network ◽  
Detection Methods ◽  
Support Vector ◽  
Parameter Method ◽  
List Type ◽  
Fuzzy C Means

Abstract Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks. In such situation, predicting attacks from normal packets is tedious, much challenging, time consuming and highly technical. As a result, different algorithms with varying learning and training capacity have been explored in the literature. However, the existing Intrusion Detection methods could not meet the desired performance requirements. Hence, this work proposes a new Intrusion Detection technique using Deep Autoencoder with Fruitfly Optimization. Initially, missing values in the dataset have been imputed with the Fuzzy C-Means Rough Parameter (FCMRP) algorithm which handles the imprecision in datasets with the exploit of fuzzy and rough sets while preserving crucial information. Then, robust features are extracted from Autoencoder with multiple hidden layers. Finally, the obtained features are fed to Back Propagation Neural Network (BPN) to classify the attacks. Furthermore, the neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments have been conducted on NSL_KDD and UNSW-NB15 dataset. The computational results of the proposed intrusion detection system using deep autoencoder with BPN are compared with Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN), BPN, and Autoencoder with Softmax. Article Highlights A hybridized model using Deep Autoencoder with Fruitfly Optimization is introduced to classify the attacks. Missing values have been imputed with the Fuzzy C-Means Rough Parameter method. The discriminate features are extracted using Deep Autoencoder with more hidden layers.

scholarly journals Contactless Interactive Fall Detection and Sleep Quality Estimation for Supporting Elderly with Incipient Dementia

2020 ◽  
Vol 6 (3) ◽  
pp. 388-391
Author(s):  
Roman Siedel ◽  
Tobias Scheck ◽  
Ana C. Perez Grassi ◽  
Julian B. Seuffert ◽  
André Apitzsch ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Fall Detection ◽  
Quality Estimation ◽  
Emergency Situations ◽  
People With Dementia ◽  
Affected Person ◽  
Development State ◽  
Current Behaviour

AbstractIn recent years, the demographic change in conjunction with a lack of professional caregivers led to retirement homes reaching capacity. The Alzheimer Disease International stated that over 50 million people suffered from dementia in 2019 worldwide and twice the amount will presumably be effected in 2030. The field of Ambient Assisted Living (AAL) tackles this problem by facilitating technical system-aided everyday life. AUXILIA is such an AAL system and does not only support elderly people with dementia in an early phase, but also monitors their activities to provide behaviour analysis results for care attendants, relatives and physicians. Moreover, the system is capable of recognizing emergency situations like human falls. Furthermore, sleep quality estimation is employed to be able to draw conclusions about the current behaviour of an affected person. This article presents the current development state of AUXILIA.

scholarly journals Machine Learning based Human Fall Detection System

2021 ◽  
Vol 9 (VI) ◽  
pp. 2677-2682
Author(s):  
Nishanth P
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Wearable Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
World Health ◽  
Support Vector ◽  
Data Output ◽  
Health Organization ◽  
Context Features

Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.

scholarly journals Recognition of Gait Activities Using Acceleration Data from A Smartphone and A Wearable Device

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 60 ◽  
Author(s):  
Irvin Hussein Lopez-Nava ◽  
Matias Garcia-Constantino ◽  
Jesus Favela
Keyword(s):  
Assisted Living ◽  
Inertial Sensor ◽  
Ambient Assisted Living ◽  
Human Gait ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Acceleration Data ◽  
Vector Machines ◽  
Young Subjects ◽  
Physical Spaces

Activity recognition is an important task in many fields, such as ambient intelligence, pervasive healthcare, and surveillance. In particular, the recognition of human gait can be useful to identify the characteristics of the places or physical spaces, such as whether the person is walking on level ground or walking down stairs in which people move. For example, ascending or descending stairs can be a risky activity for older adults because of a possible fall, which can have more severe consequences than if it occurred on a flat surface. While portable and wearable devices have been widely used to detect Activities of Daily Living (ADLs), few research works in the literature have focused on characterizing only actions of human gait. In the present study, a method for recognizing gait activities using acceleration data obtained from a smartphone and a wearable inertial sensor placed on the ankle of people is introduced. The acceleration signals were segmented based on the automatic detection of strides, also called gait cycles. Subsequently, a feature vector of the segmented signals was extracted, which was used to train four classifiers using the Naive Bayes, C4.5, Support Vector Machines, and K-Nearest Neighbors algorithms. Data was collected from seven young subjects who performed five gait activities: (i) going down an incline, (ii) going up an incline, (iii) walking on level ground, (iv) going down stairs, and (v) going up stairs. The results demonstrate the viability of using the proposed method and technologies in ambient assisted living contexts.

HYBRID FUSION OF FACE AND SPEECH INFORMATION FOR BIMODAL EMOTION ESTIMATION

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Krishna Mohan Kudiri
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Facial Expressions ◽  
Detection System ◽  
Support Vector ◽  
Emotion Detection ◽  
Fusion Technique ◽  
Speech Information ◽  
Hybrid Fusion ◽  
Emotion Estimation

Estimation of human emotions during a conversation is difficult using a computer. In this study, facial expressions and speech are used in order to estimate emotions (angry, sad, happy, boredom, disgust and surprise). A proposed hybrid system through facial expressions and speech is used to estimate emotions of a person when he is engaged in a conversational session. Relative Bin Frequency Coefficients and Relative Sub-Image-Based features are used for acoustic and visual modalities respectively. Support Vector Machine is used for classification. This study shows that the proposed feature extraction through acoustic and visual data is the most prominent aspect affecting the emotion detection system, along with the proposed fusion technique. Although some other aspects are considered to be affecting the system, the effect is relatively minor. It was observed that the performance of the bimodal system was lower than the unimodal system through deliberate facial expressions. In order to deal with the problem, a suitable database is used. The results indicate that the proposed system showed better performance, with respect to basic emotional classes than the rest.

scholarly journals Nighttime Fire/Smoke Detection System Based on a Support Vector Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Chao-Ching Ho
Keyword(s):  
Support Vector Machine ◽  
Detection System ◽  
Time Algorithm ◽  
Detection Methods ◽  
Support Vector ◽  
Smoke Detection ◽  
Fire Smoke ◽  
Large Fires ◽  
Monitoring Zone

Currently, video surveillance-based early fire smoke detection is crucial to the prevention of large fires and the protection of life and goods. To overcome the nighttime limitations of video smoke detection methods, a laser light can be projected into the monitored field of view, and the returning projected light section image can be analyzed to detect fire and/or smoke. If smoke appears within the monitoring zone created from the diffusion or scattering of light in the projected path, the camera sensor receives a corresponding signal. The successive processing steps of the proposed real-time algorithm use the spectral, diffusing, and scattering characteristics of the smoke-filled regions in the image sequences to register the position of possible smoke in a video. Characterization of smoke is carried out by a nonlinear classification method using a support vector machine, and this is applied to identify the potential fire/smoke location. Experimental results in a variety of nighttime conditions demonstrate that the proposed fire/smoke detection method can successfully and reliably detect fires by identifying the location of smoke.

A robust multimodal fall detection method for ambient assisted living applications

2010 ◽  
Author(s):  
Hande Ozgur Alemdar ◽  
Yunus Emre Kara ◽  
Mustafa Ozan Ozen ◽  
Gokhan Remzi Yavuz ◽  
Ozlem Durmaz Incel ◽  
...  
Keyword(s):  
Assisted Living ◽  
Detection Method ◽  
Ambient Assisted Living ◽  
Fall Detection
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