Efficient fall detection using Kalman filter-enhanced triaxial accelerometer signals and machine learning

This paper introduces the technical description of ICT R&D project Fall Detection -- HOLDS. The purpose of this project is to quickly assist elderly people or people who have a cognitive or motor disability when they suffer a fall at outdoor and indoor spaces. The project is aimed at enhancing the safety of these target groups and allows the elderly and impaired to carry out a more normal lifestyle. The HOLDS project is based on currently rising technologies, namely wireless sensors & actuators networks (WSAN) combined with advanced image processing. The HOLDS system comprises two subsystems, accelerometer-based fall detection and computer-vision-based (visible and infrared) fall detection, which are collected in a central system.

BackgroundRecently, machine learning (ML) has been transforming our daily lives by enabling intelligent voice assistants, personalized support for purchase decisions, and efficient credit card fraud detection. In addition to its everyday applications, ML holds the potential to improve medicine as well, especially with regard to diagnostics in clinics. In a world characterized by population growth, demographic change, and the global COVID-19 pandemic, ML systems offer the opportunity to make diagnostics more effective and efficient, leading to a high interest of clinics in such systems. However, despite the high potential of ML, only a few ML systems have been deployed in clinics yet, as their adoption process differs significantly from the integration of prior health information technologies given the specific characteristics of ML.ObjectiveThis study aims to explore the factors that influence the adoption process of ML systems for medical diagnostics in clinics to foster the adoption of these systems in clinics. Furthermore, this study provides insight into how these factors can be used to determine the ML maturity score of clinics, which can be applied by practitioners to measure the clinic status quo in the adoption process of ML systems.MethodsTo gain more insight into the adoption process of ML systems for medical diagnostics in clinics, we conducted a qualitative study by interviewing 22 selected medical experts from clinics and their suppliers with profound knowledge in the field of ML. We used a semistructured interview guideline, asked open-ended questions, and transcribed the interviews verbatim. To analyze the transcripts, we first used a content analysis approach based on the health care–specific framework of nonadoption, abandonment, scale-up, spread, and sustainability. Then, we drew on the results of the content analysis to create a maturity model for ML adoption in clinics according to an established development process.ResultsWith the help of the interviews, we were able to identify 13 ML-specific factors that influence the adoption process of ML systems in clinics. We categorized these factors according to 7 domains that form a holistic ML adoption framework for clinics. In addition, we created an applicable maturity model that could help practitioners assess their current state in the ML adoption process.ConclusionsMany clinics still face major problems in adopting ML systems for medical diagnostics; thus, they do not benefit from the potential of these systems. Therefore, both the ML adoption framework and the maturity model for ML systems in clinics can not only guide future research that seeks to explore the promises and challenges associated with ML systems in a medical setting but also be a practical reference point for clinicians.

PurposeThe first transmission of coronavirus to humans started in Wuhan city of China, took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19), and posed a principal threat to the entire world. The researchers are trying to inculcate artificial intelligence (Machine learning or deep learning models) for the efficient detection of COVID-19. This research explores all the existing machine learning (ML) or deep learning (DL) models, used for COVID-19 detection which may help the researcher to explore in different directions. The main purpose of this review article is to present a compact overview of the application of artificial intelligence to the research experts, helping them to explore the future scopes of improvement.MethodsThe researchers have used various machine learning, deep learning, and a combination of machine and deep learning models for extracting significant features and classifying various health conditions in COVID-19 patients. For this purpose, the researchers have utilized different image modalities such as CT-Scan, X-Ray, etc. This study has collected over 200 research papers from various repositories like Google Scholar, PubMed, Web of Science, etc. These research papers were passed through various levels of scrutiny and finally, 50 research articles were selected.ResultsIn those listed articles, the ML / DL models showed an accuracy of 99% and above while performing the classification of COVID-19. This study has also presented various clinical applications of various research. This study specifies the importance of various machine and deep learning models in the field of medical diagnosis and research.ConclusionIn conclusion, it is evident that ML/DL models have made significant progress in recent years, but there are still limitations that need to be addressed. Overfitting is one such limitation that can lead to incorrect predictions and overburdening of the models. The research community must continue to work towards finding ways to overcome these limitations and make machine and deep learning models even more effective and efficient. Through this ongoing research and development, we can expect even greater advances in the future.

ObjectiveThis study introduces LiteFallNet, a lightweight and interpretable deep learning model for real-time fall detection using only inertial sensor data. It aims to overcome key limitations in current systems, including high computational demands, latency, and privacy concerns, while delivering accurate and reliable performance.MethodsLiteFallNet integrates a Gated Recurrent Unit (GRU) layer, a Temporal Convolutional Network (TCN) block, depthwise separable convolutions, and a Squeeze-and-Excitation (SE) block to efficiently extract temporal features from tri-axial accelerometer, gyroscope, and magnetometer signals. The model was trained and evaluated on the FallAllD and the UMAFall datasets. To enhance transparency, one-dimensional gradient-weighted class activation mapping (1D Grad-CAM) and local interpretable model-agnostic explanations (LIME) were used to interpret how the model made its predictions.ResultsThe model on the FallAllD dataset achieved an accuracy of 97.81%, a recall of 98.55%, and an F1-score of 97.88%, with an area under the receiver operating characteristic curve of 99.33%. With a size of just 0.312 MB and an inference time of 7.07 ms, LiteFallNet combines strong performance with efficiency. These attributes make it highly suitable for deployment in real-time, resource-constrained environments.ConclusionLiteFallNet offers a privacy-preserving and real-time solution for fall detection. Its accuracy, transparency, and lightweight design make it suitable for smart homes, eldercare facilities, and wearable health technologies.

This literature review research paper examines the application of AI and machine learning in the financial industry and its effects on risk management and fraud detection. The study conducts a comprehensive search of academic and industry sources and identifies key findings and trends related to the use of these technologies in the financial industry. The literature review finds that AI-based systems have been shown to improve the efficiency and effectiveness of fraud detection by analysing vast amounts of data in real-time and identifying patterns and anomalies that may indicate potential fraud (KPMG, 2018). Additionally, machine learning algorithms can be trained to adapt and improve over time, making the detection process even more accurate. Furthermore, AI-based systems have also been shown to improve risk management in the financial industry (Deloitte, 2019). However, it is important to note that while AI and machine learning have the potential to significantly improve risk management and fraud detection in the financial industry, they are not without limitations and potential biases (Bolton and Hand, 2002; Bose, 2018). Therefore, financial institutions should carefully consider the implementation and use of these technologies and ensure proper governance and controls are in place. This literature review provides a comprehensive understanding of the current state of AI and machine learning in the financial industry, its benefits and challenges, and future possibilities. Keywords: AI, Machine Learning, Financial Industry, Risk Management, Fraud Detection, Governance, Ethical Implications

IntroductionUsing wrist-wearable sensors to ecological transient assessment may provide a more valid assessment of physical activity, sedentary time, sleep and circadian rhythm than self-reported questionnaires, but has not been used widely to study the association with mild cognitive impairment and their characteristics.Methods31 normal cognitive ability participants and 68 MCI participants were monitored with tri-axial accelerometer and nocturnal photo volumetric pulse wave signals for 14 days. Two machine learning algorithms: gradient boosting decision tree and eXtreme gradient boosting were constructed using data on daytime physical activity, sedentary time and nighttime physiological functions, including heart rate, heart rate variability, respiratory rate and oxygen saturation, combined with subjective scale features. The accuracy, precision, recall, F1 value, and AUC of the different models are compared, and the training and model effectiveness are validated by the subject-based leave-one-out method.ResultsThe low physical activity state was higher in the MCI group than in the cognitively normal group between 8:00 and 11:00 (P < 0.05), the daily rhythm trend of the high physical activity state was generally lower in the MCI group than in the cognitively normal group (P < 0.05). The peak rhythms in the sedentary state appeared at 12:00–15:00 and 20:00. The peak rhythms of rMSSD, HRV high frequency output power, and HRV low frequency output power in the 6h HRV parameters at night in the MCI group disappeared at 3:00 a.m., and the amplitude of fluctuations decreased; the amplitude of fluctuations of LHratio nocturnal rhythm increased and the phase was disturbed; the oxygen saturation was between 90 and 95% and less than 90% were increased in all time periods (P < 0.05). The F1 value of the two machine learning algorithms for MCI classification of multi-feature data combined with subjective scales were XGBoost (78.02) and GBDT (84.04).ConclusionBy collecting PSQI Scale data combined with circadian rhythm characteristics monitored by wrist-wearable sensors, we are able to construct XGBoost and GBDT machine learning models with good discrimination, thus providing an early warning solution for identifying family and community members with high risk of MCI.

Malicious software also known as "Malware" is software that uses legitimate instructions or code to perform malicious actions. Malware poses a major threat for computer security and information security in general. Over the years, malware has evolved to the point that a single malware specimen can have hundreds or maybe thousands of variants using polymorphic and metamorphic transformation to change the signature of the malware variant in propagation. The common signature-based malware detection methods are no longer robust to detect these variants due to the alteration of code. Static analysis is required to obtain these signatures and anti-virus companies are required to propagate these signature updates to their software. A faster detection method is needed to compensate the exponentially increasing number of malware variants. Machine learning is a trending approach for malware detection. This removes the need to use signature-based detection and is also faster. Software companies do not need to propagate signatures as often. Machine learning algorithms using opcode sequences can recognise patterns in the malicious code that are not present in common signatures and classify them more efficiently. Therefore, a machine learning approach for malware detection should be adopted for faster and more efficient detection. Most research in malware detection using machine learning used static attributes such as network connections, processes spawned, hashes, etc., that were not that robust to changes. In this paper we introduced our novel approach in using trigrams and PE file attributes as features for malware detection. We took a text mining approach to make our detection method more robust to polymorphism and metamorphism. The instruction sequence for critical code in malware on the assembly level is basically the same across malware families. We used opcode trigram sequences as the main feature for our machine learning algorithm. We used Support Vector Machine(SVM) as our classifying algorithm which is a discriminative classifier model that gives a definite decision whether the predicted outcome belongs to the learned class or not. The above shows our novel approach that enabled us to get higher detection rates with less features.

Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.

Soft tissue tumors (STT) represent a significant medical challenge, requiring accurate and efficient analysis for timely diagnosis and treatment. This survey explores the application of machine learning techniques in the analysis of soft tissue tumors, focusing on enhancing efficiency in detection and classification. The review encompasses various approaches, including traditional image processing methods and the more recent advancements in machine learning. One of the key contributions of this survey is the proposal of a method employing Convolutional Neural Networks (CNN) for the analysis of soft tissue tumors. CNNs have demonstrated remarkable success in image-related tasks, making them particularly suitable for medical image analysis. The research particularly aims in distinguishing Melanoma from normal skin tissue. To enhance the efficiency of research on the analysis of soft tissue tumors, the proposed study provides a comprehensive review of relevant literature, focusing on the application of machine learning in the identification of soft tissue tumors. This review includes an evaluation of the merits and demerits of each system. Furthermore, the study introduces a suggested model capable of providing accurate classification for soft tissue tumors.

Current approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, and most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis and intervention improves developmental course and outcome. The aim of the current study was to test the ability of machine learning (ML) models applied to electronic medical records (EMRs) to predict ASD early in life, in a general population sample. We used EMR data from a single Israeli Health Maintenance Organization, including EMR information for parents of 1,397 ASD children (ICD-9/10) and 94,741 non-ASD children born between January 1st, 1997 and December 31st, 2008. Routinely available parental sociodemographic information, parental medical histories, and prescribed medications data were used to generate features to train various ML algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross-validation by computing the area under the receiver operating characteristic curve (AUC; C-statistic), sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value [PPV]). All ML models tested had similar performance. The average performance across all models had C-statistic of 0.709, sensitivity of 29.93%, specificity of 98.18%, accuracy of 95.62%, false positive rate of 1.81%, and PPV of 43.35% for predicting ASD in this dataset. We conclude that ML algorithms combined with EMR capture early life ASD risk as well as reveal previously unknown features to be associated with ASD-risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.

The expansion of electronic payments in the retail sector has been accompanied by an increase in advanced payment frauds. Traditional rule based legacy static fraud detection methods, in most cases, proved inadequate to address dynamically changing and constantly evolving fraudster practices. This paper discusses an efficient real time fraud detection system through a combination of Machine Learning (ML) algorithms and real time data stream processing technology. The study is helpful in the sense that it has a constructed synthetic transaction data set which was specially designed and built to be utilized for the very specific purpose of simulating real retail payment scenarios. The data have 489 samples with columns of transaction amount, merchant category, time of day, and frequency of transactions. We test and use a subset of the ML algorithms, like Random Forest and Gradient Boosting, to identify fraud effectively with very low latency. The solution takes consumption transaction as an Apache Kafka stream simulated, real time feature creation, and scoring of transactions using trained ML models. It depicts how ensemble methods, of which Gradient Boosting is a fair example, are more precise in recall and accuracy and minimize false negatives and false positives to a significant degree. This research acknowledges the prospect of a streaming paradigm and sophisticated ML in developing adaptive and real time fraud detection systems of high value addition to the security framework of modern retail payment systems.

Breast cancer is one of the most prevalent and chronic disease that affect women. To overcome this disease, effective medical treatment is required. Early detection of the disease plays an important role for suitable medication and survival of patient. To identify the breast cancer in the patients, standard imaging technique mammography is used. Due to the subtle and varied nature of cancer tissues interpreting mammogram images can be a challenge to doctors. Machine learning (ML) and Deep Learning (DL) techniques offer promising solutions that provide efficient breast cancer detection from mammograms. In this review paper a comprehensive review of ML and DL algorithms and their applications in mammogram image analysis are presented. Various supervised and unsupervised learning techniques, such as convolutional neural networks (CNNs), support vector machines (SVMs), random forests, and other popular ML and DL models are discussed in paper. The integration of these DL methods that are efficiently used in image preprocessing techniques, feature extraction, and classification strategies. The overall survey focusses on various performance metrics, datasets, and benchmarks used in existing studies. Further the strengths and limitations of different approaches used by various researchers are identified. By understating current research trends this paper aims to contribute to the ongoing development of more accurate and reliable breast cancer detection systems using advanced ML techniques.

The object of this study is the process of detecting threats and vulnerabilities in hacker forums, which are a well-known source of potential dangers for Internet users. However, the problem of analyzing and classifying data from these forums is its complexity due to such features of the participants' language as specific slang, jargon, etc., which requires the use of modern tools of their processing. This paper explores the application of machine learning to devise an effective method for analyzing sentiment and trends in hacker forums to identify potential threats and vulnerabilities in cyberspace. All necessary stages of the process of detecting threats and vulnerabilities have been developed, ranging from data collection and preprocessing to the training of a model that is capable of processing “raw” unstructured data from hacker forums. The implementation of six popular machine learning algorithms, namely k Nearest Neighbors (kNN), Random Forest, Naive Bayes, Logistic Regression, Support Vector Machines (SVM), and Decision Tree algorithms have been studied with a view to determining their efficiency of threat and vulnerability detection and classification. The experiments have been conducted on real data (150,000 messengers). It has been determined that the Random Forest algorithm coped with the task the best (accuracy=0.89, recall=0.84, precision=0.91, F1-score=0.87 and ROC-AUC=0.89). The proposed tool based on machine learning not only collects data that poses a potential threat but also processes and classifies it according to the specified keywords. This allows detecting threats and vulnerabilities at a high speed. The results of the study make it possible to identify potential trends in threats and vulnerabilities. This will contribute to the improvement of cybersecurity systems and ensure more reliable protection of information resources

Three-dimensional (3D) medical images are prone to overlap, and there are some problems, such as low detection efficiency and inconsistent with the actual situation. Therefore, a 3D medical image surface reconstruction method based on data mining and machine learning is proposed. The 3D medical images were classified according to different ways, the information frame of 3D medical images was established and the surface overlapping information model of 3D images was given. Based on this information framework, the nonlinear function of overlapping area information of 3D medical images was constructed. The weight of the nonlinear function was used to calculate the input and output results of overlapping area information. Combined with the input mode of 3D medical image information, the error between the information output and the expected output was set. The nonlinear function weight of the overlapping area information of 3D medical images was modified by using the learning rate and the use time of the overlapping area information, and the influence factors of the overlapping information detection were obtained by increasing the situation terms, so as to complete the detection of the surface reconstruction information of 3D medical images. The experimental results show that the information detection results of the proposed method fit well with the actual situation, and the information detection efficiency is high.

Overall equipment effectiveness (OEE) is a necessary metric for monitoring and improving production processes in industry [Nakajima, 1988]. In order to make the OEE calculation properly, one needs to digitize the accurate data coming from the production line on the shopfloor, which is a challenge itself. The solution we present provides the accurate collection of production line status and OEE data required for monitoring and decision making. The problems found in existing solutions are overcome with advanced analytical methods such as video image processing and deep learning / machine learning. There are many solutions in the literature using traditional image processing approaches [Dalal, 2005] or machine learning methods [Felzenszwalb, 2010] to solve the object detection problem in the video. In recent years, deep learning methods have also yielded successful results in object detection [Girshick, 2014]. The innovative aspect of the solution we offer is that it is a system that learns patterns that may be different for each production line, and automatically predicts the production line status.