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

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Open Journal of Applied Biosensor

ISSN Print: 2168-5401
ISSN Online: 2168-5398
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"An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network (ANN) and Support Vector Machine (SVM) Algorithms"
written by Bhargava Teja Nukala, Naohiro Shibuya, Amanda Rodriguez, Jerry Tsay, Jerry Lopez, Tam Nguyen, Steven Zupancic, Donald Yu-Chun Lie,
published by Open Journal of Applied Biosensor, Vol.3 No.4, 2014

has been cited by the following article(s):

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[25]	Optimized Clustering Techniques for Gait Profiling in Children with Cerebral Palsy for Rehabilitation
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[27]	Detecting Irregular Patterns in IoT Streaming Data for Fall Detection
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[31]	Bag-of-steps: Predicting Lower-limb Fracture Rehabilitation Length
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[35]	An overview of smart shoes in the internet of health things: gait and mobility assessment in health promotion and disease monitoring
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[38]	Accelerometer-based fall detection using feature extraction and support vector machine algorithms
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[39]	Enhanced Characterization of an Accelerometer-Based Fall Detection Algorithm Using a Repository
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[40]	Real-time classification of patients with balance disorders vs. normal subjects using a low-cost small wireless wearable gait sensor
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[41]	Gaits classification of normal vs. patients by wireless gait sensor and Support Vector Machine (SVM) classifier
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[42]	Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges
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[43]	Classification Algorithm: Application to Orthotic Bracing
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[46]	Development and Performance Analysis of Gait Classification Algorithm: Application to Orthotic Bracing
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[1]	Secure Your Steps: A Class-Based Ensemble Framework for Real-Time Fall Detection Using Deep Neural Networks IEEE Access, 2023 DOI:10.1109/ACCESS.2023.3289402

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[4]	A Survey of IoT-Based Fall Detection for Aiding Elderly Care: Sensors, Methods, Challenges and Future Trends Applied Sciences, 2022 DOI:10.3390/app12073276

[5]	Machine Learning Based Fall Detector With a Sensorized Tip IEEE Access, 2021 DOI:10.1109/ACCESS.2021.3132656

[6]	Systemic financial risk early warning of financial market in China using Attention-LSTM model The North American Journal of Economics and Finance, 2021 DOI:10.1016/j.najef.2021.101383

[7]	Systemic financial risk early warning of financial market in China using Attention-LSTM model The North American Journal of Economics and Finance, 2021 DOI:10.1016/j.najef.2021.101383

[8]	The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors, Classifiers and Processing Conditions Sensors, 2021 DOI:10.3390/s21144638

[9]	Post-fall Detection Using ANN Based on Ranking Algorithms International Journal of Precision Engineering and Manufacturing, 2020 DOI:10.1007/s12541-020-00398-6

[10]	Computational Intelligence in Sensor Networks Studies in Computational Intelligence, 2019 DOI:10.1007/978-3-662-57277-1_15

[11]	Facile Preparation of a ZnO/SnO2-Based Gas Sensor Array by Inkjet Printing for Gas Analysis with BPNN Journal of Electronic Materials, 2019 DOI:10.1007/s11664-019-06938-9

[12]	Differentiation of Patients with Balance Insufficiency (Vestibular Hypofunction) versus Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor Biosensors, 2019 DOI:10.3390/bios9010029

[13]	XXVI Brazilian Congress on Biomedical Engineering IFMBE Proceedings, 2019 DOI:10.1007/978-981-13-2119-1_98

[14]	Practical fall detection based on IoT technologies: A survey Internet of Things, 2019 DOI:10.1016/j.iot.2019.100124

[15]	Design of artificial neural network using particle swarm optimisation for automotive spring durability Journal of Mechanical Science and Technology, 2019 DOI:10.1007/s12206-019-1003-9

[16]	SVM Model for Feature Selection to Increase Accuracy and Reduce False Positive Rate in Falls Detection 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), 2019 DOI:10.1109/IC4ME247184.2019.9036529

[17]	Current technological trends in biosensors, nanoparticle devices and biolabels: Hi-tech network sensing applications Medical Devices & Sensors, 2018 DOI:10.1002/mds3.10011

[18]	A Heterogeneous Ensemble Learning Voting Method for Fatigue Detection in Daily Activities Journal of Advanced Computational Intelligence and Intelligent Informatics, 2018 DOI:10.20965/jaciii.2018.p0088

[19]	Optimized Clustering Techniques for Gait Profiling in Children with Cerebral Palsy for Rehabilitation The Computer Journal, 2018 DOI:10.1093/comjnl/bxy022

[20]	Hips Do Lie! A Position-Aware Mobile Fall Detection System 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2018 DOI:10.1109/PERCOM.2018.8444583

[21]	Detecting Irregular Patterns in IoT Streaming Data for Fall Detection 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2018 DOI:10.1109/IEMCON.2018.8614822

[22]	A Benchmark Database and Baseline Evaluation for Fall Detection Based on Wearable Sensors for the Internet of Medical Things Platform IEEE Access, 2018 DOI:10.1109/ACCESS.2018.2869833

[23]	Optimizing embedded neural networks based on saturated states 2018 Real-Time and Embedded Systems and Technologies (RTEST), 2018 DOI:10.1109/RTEST.2018.8397075

[24]	Current technological trends in biosensors, nanoparticle devices and biolabels: Hi‐tech network sensing applications MEDICAL DEVICES & SENSORS, 2018 DOI:10.1002/mds3.10011

[25]	Bag-of-steps: Predicting lower-limb fracture rehabilitation length by weight loading analysis Neurocomputing, 2017 DOI:10.1016/j.neucom.2016.11.084

[26]	An Overview of Smart Shoes in the Internet of Health Things: Gait and Mobility Assessment in Health Promotion and Disease Monitoring Applied Sciences, 2017 DOI:10.3390/app7100986

[27]	Reasoning algorithm of outdoors tumble based on Ë(t) set for the elderly 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), 2017 DOI:10.1109/HealthCom.2017.8210828

[28]	Enhanced characterization of an accelerometer-based fall detection algorithm using a repository Instrumentation Science & Technology, 2017 DOI:10.1080/10739149.2016.1268155

[29]	Real-Time Classification of Patients with Balance Disorders vs. Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor Biosensors, 2016 DOI:10.3390/bios6040058

[30]	Accelerometer-based fall detection using feature extraction and support vector machine algorithms Instrumentation Science & Technology, 2016 DOI:10.1080/10739149.2015.1123161

[31]	Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges Artificial Intelligence Review, 2016 DOI:10.1007/s10462-016-9514-6

[32]	Gaits classification of normal vs. patients by wireless gait sensor and Support Vector Machine (SVM) classifier 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), 2016 DOI:10.1109/ICIS.2016.7550922

[33]	ANN for gait estimations: A review on current trends and future applications 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2016 DOI:10.1109/IECBES.2016.7843464

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