scholarly journals Human Fall Detection Using Machine Learning Methods: A Survey

2019 ◽  
Vol 5 (1) ◽  
pp. 161-180
Author(s):  
Komal Singh ◽  
Akshay Rajput ◽  
Sachin Sharma
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Fall Detection ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Healthcare Sector ◽  
Daily Routine ◽  
Context Aware ◽  
Machine Learning Methods ◽  
Advanced Technologies

Human fall due to an accident can cause heavy injuries which may lead to a major medical issue for elderly people. With the introduction of new advanced technologies in the healthcare sector, an alarm system can be developed to detect a human fall. This paper summarizes various human fall detection methods and techniques, through observing people’s daily routine activities. A human fall detection system can be designed using one of these technologies: wearable based device, context-aware based and vision based methods. In this paper, we discuss different machine learning models designed to detect human fall using these techniques. These models have already been designed to discriminate fall from activities of daily living (ADL) like walking, moving, sitting, standing, lying and bending. This paper is aimed at analyzing the effectiveness of these machine learning algorithms for the detection of human fall.

scholarly journals MALGRA: Machine Learning and N-Gram Malware Feature Extraction and Detection System

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1777
Author(s):  
Muhammad Ali ◽  
Stavros Shiaeles ◽  
Gueltoum Bendiab ◽  
Bogdan Ghita
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Detection System ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Detection Methods ◽  
Normal Operation ◽  
Analysis Technique ◽  
N Gram

Detection and mitigation of modern malware are critical for the normal operation of an organisation. Traditional defence mechanisms are becoming increasingly ineffective due to the techniques used by attackers such as code obfuscation, metamorphism, and polymorphism, which strengthen the resilience of malware. In this context, the development of adaptive, more effective malware detection methods has been identified as an urgent requirement for protecting the IT infrastructure against such threats, and for ensuring security. In this paper, we investigate an alternative method for malware detection that is based on N-grams and machine learning. We use a dynamic analysis technique to extract an Indicator of Compromise (IOC) for malicious files, which are represented using N-grams. The paper also proposes TF-IDF as a novel alternative used to identify the most significant N-grams features for training a machine learning algorithm. Finally, the paper evaluates the proposed technique using various supervised machine-learning algorithms. The results show that Logistic Regression, with a score of 98.4%, provides the best classification accuracy when compared to the other classifiers used.

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 An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Francy Shu ◽  
Jeff Shu
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Low Cost ◽  
Fall Detection ◽  
Detection Methods ◽  
Surveillance Systems ◽  
Nursing Facilities ◽  
Risk Zones ◽  
Set Up ◽  
Current Detection

AbstractFalls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replacements, wearer discomfort, high costs, complicated setup, furniture occlusion, and intensive computation. In fact, all non-wearable methods fail to detect falls beyond ten meters. Here, we design a house-wide fall detection system capable of detecting stumbling, slipping, fainting, and various other types of falls at 60 m and beyond, including through transparent glasses, screens, and rain. By analyzing the fall pattern using machine learning and crafted rules via a local, low-cost single-board computer, true falls can be differentiated from daily activities and monitored through conventionally available surveillance systems. Either a multi-camera setup in one room or single cameras installed at high altitudes can avoid occlusion. This system’s flexibility enables a wide-coverage set-up, ensuring safety in senior homes, rehab centers, and nursing facilities. It can also be configured into high-precision and high-recall application to capture every single fall in high-risk zones.

scholarly journals Malicious Mining Behavior Detection System of Encrypted Digital Currency Based on Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Mu Bie ◽  
Haoyu Ma
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Network Flow ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Traffic Data ◽  
Sensitive Data ◽  
Power Resources ◽  
Normal Network

With the gradual increase of malicious mining, a large amount of computing resources are wasted, and precious power resources are consumed maliciously. Many detection methods to detect malicious mining behavior have been proposed by scholars, but most of which have pure defects and need to collect sensitive data (such as memory and register data) from the detected host. In order to solve these problems, a malicious mining detection system based on network timing signals is proposed. When capturing network traffic, the system does not need to know the contents of data packets but only collects network flow timing signals, which greatly protects the privacy of users. Besides, we use the campus network to carry out experiments, collect a large amount of network traffic data generated by mining behavior, and carry out feature extraction and data cleaning. We also collect traffic data of normal network behavior and combine them after labeling. Then, we use four machine learning algorithms for classification. The final results show that our detection system can effectively distinguish the normal network traffic and the network traffic generated by mining behavior.

scholarly journals Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey

2019 ◽  
Vol 9 (20) ◽  
pp. 4396 ◽  
Author(s):  
Hongyu Liu ◽  
Bo Lang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Detection System ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Learning Methods ◽  
Machine Learning Methods

Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in the network. Despite decades of development, existing IDSs still face challenges in improving the detection accuracy, reducing the false alarm rate and detecting unknown attacks. To solve the above problems, many researchers have focused on developing IDSs that capitalize on machine learning methods. Machine learning methods can automatically discover the essential differences between normal data and abnormal data with high accuracy. In addition, machine learning methods have strong generalizability, so they are also able to detect unknown attacks. Deep learning is a branch of machine learning, whose performance is remarkable and has become a research hotspot. This survey proposes a taxonomy of IDS that takes data objects as the main dimension to classify and summarize machine learning-based and deep learning-based IDS literature. We believe that this type of taxonomy framework is fit for cyber security researchers. The survey first clarifies the concept and taxonomy of IDSs. Then, the machine learning algorithms frequently used in IDSs, metrics, and benchmark datasets are introduced. Next, combined with the representative literature, we take the proposed taxonomic system as a baseline and explain how to solve key IDS issues with machine learning and deep learning techniques. Finally, challenges and future developments are discussed by reviewing recent representative studies.

scholarly journals A High Accuracy Pedestrian Detection System Combining a Cascade AdaBoost Detector and Random Vector Functional-Link Net

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhihui Wang ◽  
Sook Yoon ◽  
Shan Juan Xie ◽  
Yu Lu ◽  
Dong Sun Park
Keyword(s):  
Machine Learning ◽  
Random Vector ◽  
Detection System ◽  
Pedestrian Detection ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Functional Link ◽  
Detection Rates ◽  
Standard Scale

In pedestrian detection methods, their high accuracy detection rates are always obtained at the cost of a large amount of false pedestrians. In order to overcome this problem, the authors propose an accurate pedestrian detection system based on two machine learning methods: cascade AdaBoost detector and random vector functional-link net. During the offline training phase, the parameters of a cascade AdaBoost detector and random vector functional-link net are trained by standard dataset. These candidates, extracted by the strategy of a multiscale sliding window, are normalized to be standard scale and verified by the cascade AdaBoost detector and random vector functional-link net on the online phase. Only those candidates with high confidence can pass the validation. The proposed system is more accurate than other single machine learning algorithms with fewer false pedestrians, which has been confirmed in simulation experiment on four datasets.

scholarly journals A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 938
Author(s):  
Nicolas Zurbuchen ◽  
Adriana Wilde ◽  
Pascal Bruegger
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Learning Algorithms ◽  
Sampling Rate ◽  
Fall Detection ◽  
Machine Learning Algorithms ◽  
Measurement Unit ◽  
Gradient Boosting ◽  
Classification Approach ◽  
Multi Class Classification

Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors’ sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

scholarly journals Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alan Brnabic ◽  
Lisa M. Hess
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Real World ◽  
Learning Algorithms ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

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