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Artificial Intelligence Based Body Sensor Network Framework—Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare

Ashwin A. Phatak, Franz-Georg Wieland, Kartik Vempala, Frederik Volkmar, Daniel Memmert
2021 Sports Medicine - Open  
The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location  ...  This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis.  ...  Acknowledgements The authors of the paper would like to acknowledge Ms Maithili Phatak for her contribution for the artwork in the current paper.  ... 
doi:10.1186/s40798-021-00372-0 pmid:34716868 fatcat:htbk2pu7fvcbjcpslg3lcvgfhi

Detection of Abnormal Gait from Skeleton Data

Meng Meng, Hassen Drira, Mohamed Daoudi, Jacques Boonaert
2016 Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications  
Human gait analysis has becomes of special interest to computer vision community in recent years.  ...  We achieve 98% success to classify normal and abnormal gaits and show some relevant features that are able to distinguish them.  ...  At the same time, the recent improvement of of low cost depth cameras with real-time capabilities such as Microsoft Kinect have been employed and in a wide range of applications, including human-computer  ... 
doi:10.5220/0005722901310137 dblp:conf/visapp/MengDDB16 fatcat:uusedyouencvbfh3s3kc3wllpy

A Novel Martingale Based Model Using a Smartphone to Detect Gait Bout in Human Activity Recognition

Jonathan Etumusei, Jorge Carracedo Martinez, Sally McClean, Hailing Fu
2022 Journal of Sensors  
MTMS(PSO) utilizes the martingale framework to capture gait bout in human activity recognition data.  ...  Secondly, the activity recognition model involves computing a threshold for identifying gait bout.  ...  Acknowledgments This work was supported by a University of Ulster Vice-Chancellor's Research Studentship. The authors would like to thank anonymous reviewers for their constructive suggestions.  ... 
doi:10.1155/2022/4753732 fatcat:6ndp3xymzjagbnzd22fe3r6lcy

A Survey on Visual Surveillance of Object Motion and Behaviors

W. Hu, T. Tan, L. Wang, S. Maybank
2004 IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)  
It has a wide spectrum of promising applications, including access control in special areas, human identification at a distance, crowd flux statistics and congestion analysis, detection of anomalous behaviors  ...  ., occlusion handling, a combination of twoand three-dimensional tracking, a combination of motion analysis and biometrics, anomaly detection and behavior prediction, content-based retrieval of surveillance  ...  Xie, and G. Xu from the NLPR for their valuable suggestions and assistance in preparing this paper.  ... 
doi:10.1109/tsmcc.2004.829274 fatcat:cozxn2ogtrew3pybyuxcrj2rhi

Tensor-based anomaly detection: An interdisciplinary survey

Hadi Fanaee-T, João Gama
2016 Knowledge-Based Systems  
Traditional spectral-based methods such as PCA are popular for anomaly detection in a variety of problems and domains.  ...  This survey aims to highlight the potential of tensor-based techniques as a novel approach for detection and identification of abnormalities and failures.  ...  For instance, the difference of predicted and actual values can be used along with a threshold to detect anomalies.  ... 
doi:10.1016/j.knosys.2016.01.027 fatcat:lejxxae63jcutfx2ncahownt7e

Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

Hadi Banaee, Mobyen Ahmed, Amy Loutfi
2013 Sensors  
In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series  ...  This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services.  ...  Acknowledgements The authors of this work are partially supported by SAAPHO (Secure Active Aging: Participation and Health for the Old).  ... 
doi:10.3390/s131217472 pmid:24351646 pmcid:PMC3892855 fatcat:fy4hhounsrgffno2fclhanqt7m

Detection of Motor Impairment in Parkinson's Disease Via Mobile Touchscreen Typing

Teresa Arroyo-Gallego, Maria Jesus Ledesma-Carbayo, Alvaro Sanchez-Ferro, Ian Butterworth, Carlos S. Mendoza, Michele Matarazzo, Paloma Montero, Roberto Lopez-Blanco, Veronica Puertas-Martin, Rocio Trincado, Luca Giancardo
2017 IEEE Transactions on Biomedical Engineering  
This paper presents an algorithm to detect PD by analyzing the typing activity on smartphones independently of the content of the typed text. We propose a set of touchscreen typing  ...  At present, evaluation of PD signs requires trained experts to quantify motor impairment in the clinic, limiting the frequency and quality of the information available for understanding the status and  ...  ACKNOWLEDGEMENTS The authors would like to thank the Federación Española Parkinson and the M+Vision and MIT linQ faculty for their guidance in developing this project.  ... 
doi:10.1109/tbme.2017.2664802 pmid:28237917 fatcat:jyfmuyijynbenkpudrjm4ebwce

Symbolic Modelling of Dynamic Human Motions [chapter]

David Stirling, Amir Hesami, Christian Ritz, Kevin Adistambha, Fazel Naghdy
2010 Biosensors  
In order to be able to use the described method in a real application, an image processing and computer vision section for data acquisition should be added to the system.  ...  It is conjectured that the study of these dynamic (spatiotemporal) multidimensional manifestations will facilitate a new approach to anomaly pattern detection for human motions.  ... 
doi:10.5772/7215 fatcat:mgfvhfsrhrgodazqxktbhuo2zq

A survey of multilinear subspace learning for tensor data

Haiping Lu, Konstantinos N. Plataniotis, Anastasios N. Venetsanopoulos
2011 Pattern Recognition  
Lastly, the paper summarizes a wide range of MSL applications and concludes with perspectives on future research directions.  ...  This leads to a strong demand for learning algorithms to extract useful information from these massive data.  ...  Acknowledgment The authors would like to thank the anonymous reviewers for their insightful comments, which have helped to improve the quality of this paper.  ... 
doi:10.1016/j.patcog.2011.01.004 fatcat:6puqzxrohfawraeiyid6633wdm

Distributed Machine Learning in Materials that Couple Sensing, Actuation, Computation and Communication [article]

Dana Hughes, Nikolaus Correll
2016 arXiv   pre-print
This paper reviews machine learning applications and approaches to detection, classification and control of intelligent materials and structures with embedded distributed computation elements.  ...  As the ultimate goal of this research is to incorporate the approaches described in this survey into a robotic material paradigm, the potential for adapting the computational models used in these applications  ...  Detection of gait anomaly and be used to predict and possibly prevent falls.  ... 
arXiv:1606.03508v1 fatcat:zb5jcvda5jg6zpsv5w47wwr6nq

Big data analytics for preventive medicine

Muhammad Imran Razzak, Muhammad Imran, Guandong Xu
2019 Neural computing & applications (Print)  
We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and  ...  The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention.  ...  [210] integrate PCA and multivariate cumulative sum(MCUSUM) for anomaly detection with high sensitivity to small anomaly.  ... 
doi:10.1007/s00521-019-04095-y pmid:32205918 pmcid:PMC7088441 fatcat:x52upnuwbjdchkyb7hog5pvawm

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelos Angelopoulos, Emmanouel T. Michailidis, Nikolaos Nomikos, Panagiotis Trakadas, Antonis Hatziefremidis, Stamatis Voliotis, Theodore Zahariadis
2019 Sensors  
At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the  ...  Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors  ...  Thus, an intelligent data analysis and real-time supervision (IDARTS) framework was presented for data collection and creation of context-aware data analysis and evaluation.  ... 
doi:10.3390/s20010109 pmid:31878065 pmcid:PMC6983262 fatcat:n4muoguq5jalrfwqkq4264vswe

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

Shilan S. Hameed, Wan Haslina Hassan, Liza Abdul Latiff, Fahad Ghabban
2021 PeerJ Computer Science  
A total of 153 papers were reviewed and a critical analysis was conducted on the selected papers.  ...  Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data.  ...  include a computing analysis facility, which is coupled with a local archiving database in order to store the initial records of the patient.  ... 
doi:10.7717/peerj-cs.414 pmid:33834100 pmcid:PMC8022640 fatcat:tlpu7eud4zgifbhgzkqucsxk6e

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled.  ...  We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis.  ...  A structural time series is represented as (X, A) where X ∈ R T XpXc is a multivariate time series where T is the number of time steps, p is the number of sequences, c is the number of channels, and A  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

Application of AI and IoT in Clinical Medicine: Summary and Challenges

Zhao-xia Lu, Peng Qian, Dan Bi, Zhe-wei Ye, Xuan He, Yu-hong Zhao, Lei Su, Si-liang Li, Zheng-long Zhu
2021 Current Medical Science  
In this work, we examine the technical basis of IoT, cloud computing, big data analysis and machine learning involved in clinical medicine, combined with concepts of specific algorithms such as activity  ...  recognition, behavior recognition, anomaly detection, assistant decision-making system, to describe the scenario-based applications of remote diagnosis and treatment collaboration, neonatal intensive  ...  Domain-specific functions require functions tailored to specific applications, such as Time-Domain Gait Detection, which utilizes a timedomain algorithm for gait detection in addition to the Fast Fourier  ... 
doi:10.1007/s11596-021-2486-z pmid:34939144 pmcid:PMC8693843 fatcat:3g3qpksktjhv5koqs3i7ylco7y
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