2024
Assessing Free-Living Postural Sway in Persons With Multiple Sclerosis
Meyer B, Cohen J, DePetrillo P, Ceruolo M, Jangraw D, Cheney N, Solomon A, McGinnis R. Assessing Free-Living Postural Sway in Persons With Multiple Sclerosis. IEEE Transactions On Neural Systems And Rehabilitation Engineering 2024, 32: 967-973. PMID: 38373134, PMCID: PMC10966905, DOI: 10.1109/tnsre.2024.3366903.Peer-Reviewed Original ResearchConceptsMeasures of postural swayPostural swayFall riskPostural instabilityAssessment of postural instabilityRemote patient monitoring technologyPostural sway assessmentMeasures of balanceMeasures of swayPredicting fall riskPatient-reported measuresPatient monitoring technologyForce platformBalance impairmentDisease statusMobility impairmentsWearable accelerometersLab-based measuresAssociated with disease statusSwayMultiple sclerosisAnalyzed dataRiskArea under curveDaily life
2023
Digital Phenotypes of Instability and Fatigue Derived From Daily Standing Transitions in Persons With Multiple Sclerosis
VanDyk T, Meyer B, DePetrillo P, Donahue N, O’Leary A, Fox S, Cheney N, Ceruolo M, Solomon A, McGinnis R. Digital Phenotypes of Instability and Fatigue Derived From Daily Standing Transitions in Persons With Multiple Sclerosis. IEEE Transactions On Neural Systems And Rehabilitation Engineering 2023, 31: 2279-2286. PMID: 37115839, PMCID: PMC10408384, DOI: 10.1109/tnsre.2023.3271601.Peer-Reviewed Original ResearchConceptsPrediction of fall riskStanding transitionsDigital phenotypingFall riskNon-fallersSymptom monitoringPhysician assessmentMultiple sclerosisBiweekly assessmentsClinical metricsCharacterize symptomsHome monitoringSymptomsFatigueMotor instabilityAccelerometryFallersAssessmentPwMSPersonsApplications of wearablePhysiciansIsolation periodActivity classifierIntervention
2022
Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis
Meyer B, Tulipani L, Gurchiek R, Allen D, Solomon A, Cheney N, McGinnis R. Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis. PLOS Digital Health 2022, 1: e0000120. PMID: 36812538, PMCID: PMC9931255, DOI: 10.1371/journal.pdig.0000120.Peer-Reviewed Original ResearchWalking boutsFall riskGait parametersOpen-source datasetsNon-fallersFeature-based modelWalking bout durationClassification performanceWearable sensorsFall risk classificationDaily activity performancePatient-reported surveysBiannual clinical visitsFall risk estimationBout durationDeep learning modelsFall historyShort boutsClinic visitsInvestigate fall riskWalking dataRisk estimatesAssociated with morbiditySensor dataDeep learningEvaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis
Tulipani L, Meyer B, Allen D, Solomon A, McGinnis R. Evaluation of unsupervised 30-second chair stand test performance assessed by wearable sensors to predict fall status in multiple sclerosis. Gait & Posture 2022, 94: 19-25. PMID: 35220031, PMCID: PMC9086135, DOI: 10.1016/j.gaitpost.2022.02.016.Peer-Reviewed Original ResearchConceptsWearable sensorsFall statusFall riskUnsupervised conditionsChair stand test performanceClassification AUCUnsupervised monitoringChair stand testAccelerometer-derived metricsPredicting fall riskStandard Functional AssessmentSupervised performanceBalance confidenceFunctional mobilityWearableNon-fallersStand testBalance deficitsRoutine clinical assessmentSupervision visitsSensorThree-month periodPerformanceFunctional assessmentMultiple sclerosis
2021
Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis
Meyer B, Tulipani L, Gurchiek R, Allen D, Adamowicz L, Larie D, Solomon A, Cheney N, McGinnis R. Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis. IEEE Journal Of Biomedical And Health Informatics 2021, 25: 1824-1831. PMID: 32946403, PMCID: PMC8221405, DOI: 10.1109/jbhi.2020.3025049.Peer-Reviewed Original ResearchConceptsWearable sensorsPreventive interventionsBidirectional long short-termInexpensive wearable sensorsDeep neural networksWearable sensor dataFall prevention interventionsSpatiotemporal gait parametersFall risk assessmentLong-Short-TermMachine learning modelsGait biomechanicsGait parametersSensor dataFall riskNeural networkHealthcare providersStatistical featuresLearning modelsPatient reportsAccelerometer dataIdentified measuresMultiple sclerosisWearableGood performance
2020
Metrics extracted from a single wearable sensor during sit-stand transitions relate to mobility impairment and fall risk in people with multiple sclerosis
Tulipani L, Meyer B, Larie D, Solomon A, McGinnis R. Metrics extracted from a single wearable sensor during sit-stand transitions relate to mobility impairment and fall risk in people with multiple sclerosis. Gait & Posture 2020, 80: 361-366. PMID: 32615409, PMCID: PMC7413823, DOI: 10.1016/j.gaitpost.2020.06.014.Peer-Reviewed Original ResearchConceptsInertial sensorsAccelerometer-based approachFall riskBalance confidenceWearable inertial sensorsStand-to-sit transitionsTriaxial acceleration dataFall statusWearable sensorsAccelerometer-based metricsMeasures of disease severityAccelerometer featuresSelf-report outcome measuresChair stand testWearable accelerometersAccelerometer-derived metricsSit-to-standSit-stand transitionsAccuracy of functional assessmentsChallenging taskMetricsSensorClinical metricsAcceleration dataLogistic regression models