AIMC Topic: Machine Learning

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A wearable computing platform for developing cloud-based machine learning models for health monitoring applications.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Wearable sensors have the potential to enable clinical-grade ambulatory health monitoring outside the clinic. Technological advances have enabled development of devices that can measure vital signs with great precision and significant progress has be...
Models, Theoretical Female Activities of Daily Living Adult Male Monitoring, Ambulatory Humans Cloud Computing Machine Learning
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A hybrid rule and machine learning based generic alerting platform for smart environments.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Existing smart environment based alert solutions have adopted a relatively complex and tailored approach to supporting individuals. These solutions have involved sensor based monitoring, activity recognition and assistance provisioning. Traditionally...
Monitoring, Physiologic Humans Pattern Recognition, Automated Human Activities Machine Learning Software
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Efficient compressive sensing of ECG segments based on machine learning for QRS-based arrhythmia detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
A novel method for efficient telemonitoring of arrhythmia based on using QRS complexes is proposed. Two features, namely, sum of absolute differences (SAD) and maximum of absolute differences (MAD) are efficiently computed for each ECG segment in the...
Signal Processing, Computer-Assisted Arrhythmias, Cardiac Data Compression Computer Simulation Electrocardiography Area Under Curve Reproducibility of Results Time Factors Humans Machine Learning Algorithms
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Automatic recognition of pleasant content of odours through ElectroEncephaloGraphic activity analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
This study presents a machine learning approach applied to ElectroEnchephaloGraphic (EEG) response in a group of subjects when exposed to a controlled olfactory stimulation experiment. In the literature, in fact, there are controversial results on EE...
Humans Machine Learning Female Young Adult Adult Electroencephalography Olfactory Perception Odorants Data Accuracy
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Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Automatic fall detection will promote independent living and reduce the consequences of falls in the elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable of...
Activities of Daily Living Monitoring, Ambulatory Independent Living Posture Lumbar Vertebrae Algorithms Databases, Factual Aged Sensitivity and Specificity Biomechanical Phenomena Accidental Falls Humans Machine Learning
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Improving quality and intelligibility of speech using single microphone for the broadband fMRI noise at low SNR.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Functional Magnetic Resonance Imaging (fMRI) is used in many diagnostic procedures for neurological related disorders. Strong broadband acoustic noise generated during fMRI scan interferes with the speech communication between the physician and the p...
Speech Intelligibility Physician-Patient Relations Signal-To-Noise Ratio Humans Machine Learning Algorithms Magnetic Resonance Imaging Noise
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Ensemble statistical and subspace clustering model for analysis of autism spectrum disorder phenotypes.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Heterogeneity in Autism Spectrum Disorder (ASD) is complex including variability in behavioral phenotype as well as clinical, physiologic, and pathologic parameters. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-...
Humans Machine Learning Autism Spectrum Disorder Prognosis Reproducibility of Results Algorithms Phenotype Databases, Factual Cluster Analysis Diagnostic and Statistical Manual of Mental Disorders
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Multi-view non-negative tensor factorization as relation learning in healthcare data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Discovering patterns in co-occurrences data between objects and groups of concepts is a useful task in many domains, such as healthcare data analysis, information retrieval, and recommender systems. These relational representations come from objects'...
Databases, Factual Models, Theoretical Information Storage and Retrieval Humans Machine Learning Algorithms
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An asynchronous multi-view learning approach for activity recognition using wearables.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
In this paper, we introduce an Asynchronous Multiview Learning (AML) approach to allow accurate transfer of activity classification models across asynchronous sensor views. Our study is motivated by the highly dynamic nature of health monitoring usin...
Machine Learning Monitoring, Ambulatory
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Deep neural network architectures for forecasting analgesic response.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Aug 1, 2016
Response to prescribed analgesic drugs varies between individuals, and choosing the right drug/dose often involves a lengthy, iterative process of trial and error. Furthermore, a significant portion of patients experience adverse events such as post-...
Analgesia Urinary Retention Oxycodone Analgesics Administration, Oral Neural Networks, Computer Machine Learning Postoperative Complications Support Vector Machine Pain Measurement Pain, Postoperative Humans
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