Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

[1]  H. Hel-Or,et al.  Automatic and Efficient Fall Risk Assessment Based on Machine Learning , 2022, Sensors.

[2]  Nuno Ferrete Ribeiro,et al.  Fall Risk Assessment Using Wearable Sensors: A Narrative Review , 2022, Sensors.

[3]  Rohit Tanwar,et al.  Pathway of Trends and Technologies in Fall Detection: A Systematic Review , 2022, Healthcare.

[4]  N. Vayatis,et al.  A review of center of pressure (COP) variables to quantify standing balance in elderly people: Algorithms and open‐access code , 2021, Physiological reports.

[5]  Huansheng Ning,et al.  Sensor-Based Fall Risk Assessment: A Survey , 2021, Healthcare.

[6]  Abdul Saboor,et al.  Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review , 2021, Sensors.

[7]  Sydney A Santin,et al.  Functional Reach Test, Single-Leg Stance Test, and Tinetti Performance-Oriented Mobility Assessment for the Prediction of Falls in Older Adults: A Systematic Review. , 2021, Physical therapy.

[8]  C. Youm,et al.  XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes , 2021, Scientific Reports.

[9]  L. Thompson,et al.  Deep Neural Networks for Human’s Fall-risk Prediction using Force-Plate Time Series Signal , 2021, medRxiv.

[10]  T. Brandt,et al.  Fall prediction in neurological gait disorders: differential contributions from clinical assessment, gait analysis, and daily-life mobility monitoring , 2021, Journal of Neurology.

[11]  N. Vayatis,et al.  Revealing posturographic profile of patients with Parkinsonian syndromes through a novel hypothesis testing framework based on machine learning , 2021, PLoS ONE.

[12]  A. Trouvé,et al.  A Langevin-Based Model With Moving Posturographic Target to Quantify Postural Control , 2021, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[13]  T. Nef,et al.  Wearables in the home-based assessment of abnormal movements in Parkinson’s disease: a systematic review of the literature , 2021, Journal of Neurology.

[14]  Matjaž Gams,et al.  Detection of Gait Abnormalities for Fall Risk Assessment Using Wrist-Worn Inertial Sensors and Deep Learning , 2020, Sensors.

[15]  Argyris Kalogeratos,et al.  Multivariate two-sample hypothesis testing through AUC maximization for biomedical applications , 2020, SETN.

[16]  Claudine J. C. Lamoth,et al.  Classification of Neurological Patients to Identify Fallers Based on Spatial-Temporal Gait Characteristics Measured by a Wearable Device , 2020, Sensors.

[17]  P. Vidal,et al.  Center of pressure displacement characteristics differentiate fall risk in older people: A systematic review with meta-analysis , 2020, Ageing Research Reviews.

[18]  Michael E. Miller,et al.  Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults , 2020, The journals of gerontology. Series A, Biological sciences and medical sciences.

[19]  A. Ramnemark,et al.  Neither Timed Up and Go test nor Short Physical Performance Battery predict future falls among independent adults aged ≥75 years living in the community , 2020, Journal of frailty, sarcopenia and falls.

[20]  E. Lemaire,et al.  Fall risk assessment in the wild: A critical examination of wearable sensor use in free-living conditions. , 2020, Gait & posture.

[21]  P. Vidal,et al.  Motor style at rest and during locomotion in human. , 2020, Journal of neurophysiology.

[22]  Flavien Quijoux,et al.  Personalized Template-Based Step Detection From Inertial Measurement Units Signals in Multiple Sclerosis , 2020, Frontiers in Neurology.

[23]  Laurent Oudre,et al.  Non-Linear Template-Based Approach for the Study of Locomotion , 2020, Sensors.

[24]  Hua Jin,et al.  Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm , 2020, Int. J. Medical Informatics.

[25]  Cem Ersoy,et al.  Deep Learning for Fall Risk Assessment With Inertial Sensors: Utilizing Domain Knowledge in Spatio-Temporal Gait Parameters , 2019, IEEE Journal of Biomedical and Health Informatics.

[26]  Sehoon Ha,et al.  Learning a Control Policy for Fall Prevention on an Assistive Walking Device , 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[27]  M. Testa,et al.  Ecological Gait as a Fall Indicator in Older Adults: A Systematic Review. , 2020, The Gerontologist.

[28]  P. Vidal,et al.  Wearable inertial sensors provide reliable biomarkers of disease severity in multiple sclerosis: a systematic review and meta-analysis. , 2020, Annals of physical and rehabilitation medicine.

[29]  Nicolas Vayatis,et al.  A Data Set for the Study of Human Locomotion with Inertial Measurements Units , 2019, Image Process. Line.

[30]  Laurent Oudre,et al.  Comparing Gait Trials with Greedy Template Matching , 2019, Sensors.

[31]  Maurizio Rebaudengo,et al.  A Review on Fall Prediction and Prevention System for Personal Devices: Evaluation and Experimental Results , 2019, Adv. Hum. Comput. Interact..

[32]  Julien Audiffren,et al.  Local Assessment of Statokinesigram Dynamics in Time: An in-Depth Look at the Scoring Algorithm , 2019, Image Process. Line.

[33]  T. Brandt,et al.  Perception of Verticality and Vestibular Disorders of Balance and Falls , 2019, Front. Neurol..

[34]  Nicolas Vayatis,et al.  Balance Impairment in Radiation Induced Leukoencephalopathy Patients Is Coupled With Altered Visual Attention in Natural Tasks , 2019, Front. Neurol..

[35]  Cynthia Rudin,et al.  This Looks Like That: Deep Learning for Interpretable Image Recognition , 2018 .

[36]  Nicolas Vayatis,et al.  Template-Based Step Detection with Inertial Measurement Units , 2018, Sensors.

[37]  Jacob J. Sosnoff,et al.  Fall Risk Prediction in Multiple Sclerosis Using Postural Sway Measures: A Machine Learning Approach , 2018, Scientific Reports.

[38]  Graeme Hamilton,et al.  FITsense: Employing Multi-modal Sensors in Smart Homes to Predict Falls , 2018, ICCBR.

[39]  Julien Audiffren,et al.  Model-Space Regularization and Fully Interpretable Algorithms for Postural Control Quantification , 2018, 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC).

[40]  Ahmed Nait Aicha,et al.  Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry , 2018, Sensors.

[41]  Kai-Chun Liu,et al.  Machine learning-based fall characteristics monitoring system for strategic plan of falls prevention , 2018, 2018 IEEE International Conference on Applied System Invention (ICASI).

[42]  Julien Audiffren,et al.  On the importance of local dynamics in statokinesigram: A multivariate approach for postural control evaluation in elderly , 2018, PloS one.

[43]  Rossana Castaldo,et al.  Wearable Inertial Sensors for Fall Risk Assessment and Prediction in Older Adults: A Systematic Review and Meta-Analysis , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[44]  J. Sosnoff,et al.  Novel sensing technology in fall risk assessment in older adults: a systematic review , 2018, BMC Geriatrics.

[45]  P. Vidal,et al.  Gaze constraint while walking in progressive multiple sclerosis: A feasibility study , 2017, Neurophysiologie Clinique.

[46]  Tzyy-Ping Jung,et al.  Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions , 2017, Sensors.

[47]  Stéphane Baudry,et al.  Functional Synergy Between Postural and Visual Behaviors When Performing a Difficult Precise Visual Task in Upright Stance , 2017, Cogn. Sci..

[48]  Laurent Oudre,et al.  Template-DTW based on inertial signals: Preliminary results for step characterization , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[49]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[50]  P. Vidal,et al.  Inertial Sensors to Assess Gait Quality in Patients with Neurological Disorders: A Systematic Review of Technical and Analytical Challenges , 2017, Front. Psychol..

[51]  Pierre-Paul Vidal,et al.  Observational Study of 180° Turning Strategies Using Inertial Measurement Units and Fall Risk in Poststroke Hemiparetic Patients , 2017, Frontiers in Neurology.

[52]  Attila Kertész-Farkas,et al.  HuGaDB: Human Gait Database for Activity Recognition from Wearable Inertial Sensor Networks , 2017, AIST.

[53]  Kazuhiro Kosuge,et al.  Autoregressive-moving-average hidden Markov model for vision-based fall prediction—An application for walker robot , 2017, Assistive technology : the official journal of RESNA.

[54]  S. Fritz,et al.  Determining Risk of Falls in Community Dwelling Older Adults: A Systematic Review and Meta-analysis Using Posttest Probability , 2016, Journal of geriatric physical therapy.

[55]  S. Muir-Hunter,et al.  Risk Factors Associated with Falls in Older Adults with Dementia: A Systematic Review. , 2017, Physiotherapy Canada. Physiotherapie Canada.

[56]  Julien Audiffren,et al.  A Non Linear Scoring Approach for Evaluating Balance: Classification of Elderly as Fallers and Non-Fallers , 2016, PloS one.

[57]  N. Vayatis,et al.  An Automated Recording Method in Clinical Consultation to Rate the Limp in Lower Limb Osteoarthritis , 2016, PloS one.

[58]  G. Bergen,et al.  Falls and Fall Injuries Among Adults Aged ≥65 Years - United States, 2014. , 2016, MMWR. Morbidity and mortality weekly report.

[59]  J M Bauer,et al.  Technology-based measurements for screening, monitoring and preventing frailty , 2016, Zeitschrift für Gerontologie und Geriatrie.

[60]  Navrag B. Singh,et al.  Neuroscience and Biobehavioral Reviews Revealing the Quality of Movement: a Meta-analysis Review to Quantify the Thresholds to Pathological Variability during Standing and Walking , 2022 .

[61]  Renato Moraes,et al.  Synchrony of gaze and stepping patterns in people with Parkinson’s disease , 2016, Behavioural Brain Research.

[62]  T. Hortobágyi,et al.  Walking ability to predict future cognitive decline in old adults: A scoping review , 2016, Ageing Research Reviews.

[63]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[64]  Isabel S. Moore,et al.  Is There an Economical Running Technique? A Review of Modifiable Biomechanical Factors Affecting Running Economy , 2016, Sports Medicine.

[65]  Matjaz B. Juric,et al.  Inertial Sensor-Based Gait Recognition: A Review , 2015, Sensors.

[66]  P. Vidal,et al.  Early post-stroke period: A privileged time for sensory re-weighting? , 2015, Journal of rehabilitation medicine.

[67]  Jonathan B. Dingwell,et al.  Identifying Stride-To-Stride Control Strategies in Human Treadmill Walking , 2015, PloS one.

[68]  Chitralakshmi K Balasubramanian,et al.  The Community Balance and Mobility Scale Alleviates the Ceiling Effects Observed in the Currently Used Gait and Balance Assessments for the Community-Dwelling Older Adults , 2015, Journal of geriatric physical therapy.

[69]  Björn Eskofier,et al.  Inertial Sensor-Based Stride Parameter Calculation From Gait Sequences in Geriatric Patients , 2015, IEEE Transactions on Biomedical Engineering.

[70]  J. Barela,et al.  Effects of saccadic eye movements on postural control in older adults , 2015 .

[71]  Marie-Aline Charles,et al.  The effect of fall prevention exercise programmes on fall induced injuries in community dwelling older adults: systematic review and meta-analysis of randomised controlled trials , 2013, British Journal of Sports Medicine.

[72]  A. S. Ferreira,et al.  Test-retest reliability for assessment of postural stability using center of pressure spatial patterns of three-dimensional statokinesigrams in young health participants. , 2014, Journal of biomechanics.

[73]  N. A. Abu Osman,et al.  Are the spatio-temporal parameters of gait capable of distinguishing a faller from a non-faller elderly? , 2014, European journal of physical and rehabilitation medicine.

[74]  Stephen R. Lord,et al.  Single and dual task tests of gait speed are equivalent in the prediction of falls in older people: A systematic review and meta-analysis , 2014, Ageing Research Reviews.

[75]  Michael King,et al.  Trap of trends to statistical significance: likelihood of near significant P value becoming more significant with extra data , 2014, BMJ : British Medical Journal.

[76]  Tom Fahey,et al.  Is the Timed Up and Go test a useful predictor of risk of falls in community dwelling older adults: a systematic review and meta- analysis , 2014, BMC Geriatrics.

[77]  Kamiar Aminian,et al.  Gait and Foot Clearance Parameters Obtained Using Shoe-Worn Inertial Sensors in a Large-Population Sample of Older Adults , 2013, Sensors.

[78]  M. Bucci,et al.  Saccades Improve Postural Control: A Developmental Study in Normal Children , 2013, PloS one.

[79]  Agata Brajdic,et al.  Walk detection and step counting on unconstrained smartphones , 2013, UbiComp.

[80]  R. Kenny,et al.  Falls classification using tri-axial accelerometers during the five-times-sit-to-stand test. , 2013, Gait & posture.

[81]  Michael Schwenk,et al.  Frailty and Technology: A Systematic Review of Gait Analysis in Those with Frailty , 2013, Gerontology.

[82]  Natalie E. Allen,et al.  Recurrent Falls in Parkinson's Disease: A Systematic Review , 2013, Parkinson's disease.

[83]  J. Gladman,et al.  The Relationship between Executive Function and Falls and Gait Abnormalities in Older Adults: A Systematic Review , 2012, Dementia and Geriatric Cognitive Disorders.

[84]  Stéphan Clémençon,et al.  Ranking forests , 2013, J. Mach. Learn. Res..

[85]  Edgar Ramos Vieira,et al.  Can Falls Risk Prediction Tools Correctly Identify Fall-Prone Elderly Rehabilitation Inpatients? A Systematic Review and Meta-Analysis , 2012, PloS one.

[86]  G. Selbæk,et al.  Do behavioral disturbances predict falls among nursing home residents? , 2012, Aging Clinical and Experimental Research.

[87]  Thomas Brandt,et al.  Aging of human supraspinal locomotor and postural control in fMRI , 2012, Neurobiology of Aging.

[88]  Olivier Beauchet,et al.  Timed up and go test and risk of falls in older adults: A systematic review , 2011, The journal of nutrition, health & aging.

[89]  S. Studenski,et al.  Gait speed and survival in older adults. , 2011, JAMA.

[90]  A K Bourke,et al.  Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities. , 2010, Journal of biomechanics.

[91]  Bruce Walker,et al.  The test-retest reliability of centre of pressure measures in bipedal static task conditions--a systematic review of the literature. , 2010, Gait & posture.

[92]  Ping-Min Lin,et al.  A fall detection system using k-nearest neighbor classifier , 2010, Expert Syst. Appl..

[93]  F. Horak,et al.  The relevance of clinical balance assessment tools to differentiate balance deficits. , 2010, European journal of physical and rehabilitation medicine.

[94]  Andreas Zwergal,et al.  Gait disturbances in old age: classification, diagnosis, and treatment from a neurological perspective. , 2010, Deutsches Arzteblatt international.

[95]  Jeffrey M. Hausdorff,et al.  Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom , 2010, IEEE Transactions on Information Technology in Biomedicine.

[96]  Shirley Rietdyk,et al.  Multiple timescales in postural dynamics associated with vision and a secondary task are revealed by wavelet analysis , 2009, Experimental Brain Research.

[97]  J. Keating,et al.  A systematic review of mobility instruments and their measurement properties for older acute medical patients , 2008, Health and quality of life outcomes.

[98]  Lisa C. Blum,et al.  Usefulness of the Berg Balance Scale in Stroke Rehabilitation: A Systematic Review , 2008, Physical Therapy.

[99]  Michael Vassallo,et al.  Fall risk-assessment tools compared with clinical judgment: an evaluation in a rehabilitation ward. , 2008, Age and ageing.

[100]  S. D. de Rooij,et al.  Fear of falling: measurement strategy, prevalence, risk factors and consequences among older persons. , 2008, Age and ageing.

[101]  Lena H Ting,et al.  Neuromechanics of muscle synergies for posture and movement , 2007, Current Opinion in Neurobiology.

[102]  Greta C Bernatz,et al.  Video task analysis of turning during activities of daily living. , 2007, Gait & posture.

[103]  P. Shekelle,et al.  Will my patient fall? , 2007, JAMA.

[104]  Patrick Guinan,et al.  Who is My Patient? , 2006, The Linacre quarterly.

[105]  M. Hollands,et al.  Evidence for a link between changes to gaze behaviour and risk of falling in older adults during adaptive locomotion. , 2005, Gait & posture.

[106]  E. Olsson,et al.  Motor Function in Subjects with Mild Cognitive Impairment and Early Alzheimer’s Disease , 2005, Dementia and Geriatric Cognitive Disorders.

[107]  I. Melzer,et al.  Postural stability in the elderly: a comparison between fallers and non-fallers. , 2004, Age and ageing.

[108]  B. MacWilliams,et al.  Sensory cueing effects on maximal speed gait initiation in persons with Parkinson's disease and healthy elders. , 2004, Gait & posture.

[109]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[110]  Brice Isableu,et al.  Differential approach to strategies of segmental stabilisation in postural control , 2003, Experimental Brain Research.

[111]  J. Lynch,et al.  The parieto‐collicular pathway: anatomical location and contribution to saccade generation , 2003, The European journal of neuroscience.

[112]  M. Tinetti Clinical practice. Preventing falls in elderly persons. , 2003, The New England journal of medicine.

[113]  R. Feise Do multiple outcome measures require p-value adjustment? , 2002, BMC medical research methodology.

[114]  S L Luther,et al.  Fall risk assessment measures: an analytic review. , 2001, The journals of gerontology. Series A, Biological sciences and medical sciences.

[115]  B E Maki,et al.  Cognitive demands of executing postural reactions: does aging impede attention switching? , 2001, Neuroreport.

[116]  I S Curthoys,et al.  Variability in the control of head movements in seated humans: a link with whiplash injuries? , 2001, The Journal of physiology.

[117]  N. Shimizu [Neurology of eye movements]. , 2000, Rinsho shinkeigaku = Clinical neurology.

[118]  M. Woollacott,et al.  Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test. , 2000, Physical therapy.

[119]  D M Merfeld,et al.  Humans use internal models to estimate gravity and linear acceleration , 1999, Nature.

[120]  H Okada,et al.  Brain activation during maintenance of standing postures in humans. , 1999, Brain : a journal of neurology.

[121]  M. Ouaknine,et al.  Sensory strategies in human postural control before and after unilateral vestibular neurotomy , 1997, Experimental Brain Research.

[122]  H. Deubel,et al.  Saccade target selection and object recognition: Evidence for a common attentional mechanism , 1996, Vision Research.

[123]  Diane Podsiadlo,et al.  The Timed “Up & Go”: A Test of Basic Functional Mobility for Frail Elderly Persons , 1991, Journal of the American Geriatrics Society.

[124]  G. Rizzolatti,et al.  Reorienting attention across the horizontal and vertical meridians: Evidence in favor of a premotor theory of attention , 1987, Neuropsychologia.

[125]  M. Tinetti Performance‐Oriented Assessment of Mobility Problems in Elderly Patients , 1986, Journal of the American Geriatrics Society.

[126]  V. Gurfinkel,et al.  [Control elements of voluntary movements]. , 1967, Biofizika.