Fall classification by machine learning using mobile phones
about
Analysis of Android Device-Based Solutions for Fall DetectionChallenges, issues and trends in fall detection systemsNovel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model.Fall detection devices and their use with older adults: a systematic review.Comparison and characterization of Android-based fall detection systems.Automatic fall monitoring: a reviewSmartphone-based solutions for fall detection and prevention: challenges and open issuesA ZigBee-based location-aware fall detection system for improving elderly telecare.Detecting falls as novelties in acceleration patterns acquired with smartphonesHand, belt, pocket or bag: Practical activity tracking with mobile phones.Lennie: a smartphone application with novel implications for the management of animal colonies.Validity of a Smartphone-Based Fall Detection Application on Different Phones Worn on a Belt or in a Trouser Pocket.The Effect of Personalization on Smartphone-Based Fall Detectors.Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection.The need to approximate the use-case in clinical machine learningAnalysis of Public Datasets for Wearable Fall Detection Systems.Monitoring functional capability of individuals with lower limb amputations using mobile phonesOne Small Step for a Man: Estimation of Gender, Age and Height from Recordings of One Step by a Single Inertial Sensor.In-lab versus at-home activity recognition in ambulatory subjects with incomplete spinal cord injuryDynamic Bayesian networks for context-aware fall risk assessment.Workplace slip, trip and fall injuries and obesity.Highly Portable, Sensor-Based System for Human Fall Monitoring.Home Camera-Based Fall Detection System for the Elderly.Fall Detection in Individuals With Lower Limb Amputations Using Mobile Phones: Machine Learning Enhances Robustness for Real-World Applications.A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials.Combining novelty detectors to improve accelerometer-based fall detection.Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning.Irregular Gait Detection using Wearable Sensors"You can tell by the way I use my walk." Predicting the presence of cognitive load with gait measurements
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Fall classification by machine learning using mobile phones
description
2012 nî lūn-bûn
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2012 թուականի Մայիսին հրատարակուած գիտական յօդուած
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2012 թվականի մայիսին հրատարակված գիտական հոդված
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2012年の論文
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2012年論文
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2012年論文
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2012年論文
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2012年論文
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2012年論文
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2012年论文
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Fall classification by machine learning using mobile phones
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Fall classification by machine learning using mobile phones
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Fall classification by machine learning using mobile phones
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Fall classification by machine learning using mobile phones
@ast
Fall classification by machine learning using mobile phones
@en
Fall classification by machine learning using mobile phones
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Fall classification by machine learning using mobile phones
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Fall classification by machine learning using mobile phones
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Fall classification by machine learning using mobile phones
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P2860
P921
P1433
P1476
Fall classification by machine learning using mobile phones
@en
P2093
Mark V Albert
Megan Herrmann
P2860
P304
P356
10.1371/JOURNAL.PONE.0036556
P407
P577
2012-05-07T00:00:00Z