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 learningThe Sit-to-Stand Transition as a Biomarker for Impairment: Comparison of Instrumented 30-Second Chair Stand Test and Daily Life Transitions in Multiple Sclerosis
Tulipani L, Meyer B, Fox S, Solomon A, Mcginnis R. The Sit-to-Stand Transition as a Biomarker for Impairment: Comparison of Instrumented 30-Second Chair Stand Test and Daily Life Transitions in Multiple Sclerosis. IEEE Transactions On Neural Systems And Rehabilitation Engineering 2022, 30: 1213-1222. PMID: 35468063, PMCID: PMC9204833, DOI: 10.1109/tnsre.2022.3169962.Peer-Reviewed Original ResearchConceptsSensory impairmentInvestigate impairmentsDaily lifeLevel of clinical disabilityPerformance metricsStructured tasksImpairmentPyramidal impairmentSub scoresMultiple sclerosisDaily life monitoringDichotomize participantsSit-stand transitionsClinical disabilityClassification performanceTaskWearable sensorsSupervised settingChest featuresFaller classificationDeficitsLife monitoringLow fall riskArea under the curveFunctional assessmentEvaluation 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