2023
Chest-Based Wearables and Individualized Distributions for Assessing Postural Sway in Persons With Multiple Sclerosis
Meyer B, Cohen J, Donahue N, Fox S, O’Leary A, Brown A, Leahy C, VanDyk T, DePetrillo P, Ceruolo M, Cheney N, Solomon A, McGinnis R. Chest-Based Wearables and Individualized Distributions for Assessing Postural Sway in Persons With Multiple Sclerosis. IEEE Transactions On Neural Systems And Rehabilitation Engineering 2023, 31: 2132-2139. PMID: 37067975, PMCID: PMC10408383, DOI: 10.1109/tnsre.2023.3267807.Peer-Reviewed Original ResearchMeSH KeywordsBiomechanical PhenomenaHumansMultiple SclerosisPostural BalancePostureWearable Electronic DevicesConceptsPatient-reported measuresPostural sway featuresPostural swaySway featuresForce platformBalance assessmentMeasures of postural swayPostural sway measuresAssess postural swayStanding tasksSway measuresFall statusBalance impairmentLower backMobility impairmentsConcurrent validityChest accelerometerEyes-openMultiple sclerosisSensor patchSwayStatus groupsEyes-closedAccelerometerTraditional computers
2022
How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway
Meyer B, Depetrillo P, Franco J, Donahue N, Fox S, O’Leary A, Loftness B, Gurchiek R, Buckley M, Solomon A, Ng S, Cheney N, Ceruolo M, McGinnis R. How Much Data Is Enough? A Reliable Methodology to Examine Long-Term Wearable Data Acquisition in Gait and Postural Sway. Sensors 2022, 22: 6982. PMID: 36146348, PMCID: PMC9503816, DOI: 10.3390/s22186982.Peer-Reviewed Original ResearchConceptsFree-living environmentBalance impairmentIntra-class correlationPostural sway measuresMeasures of gaitEvaluation of gaitWearable accelerometer dataWear durationSway measuresPostural swayCapturing gaitEstablished patientMovement variabilityImpairment measuresGaitPatient burdenAccelerometer dataClinical measuresDays of monitoringWearable sensor-based methodsSensor wearSwayRegression analysisComprehensive assessmentStudy costsThe 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 ResearchMeSH KeywordsArea Under CurveBiomarkersHumansMultiple SclerosisPostural BalanceWearable Electronic DevicesConceptsSensory 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 ResearchMeSH KeywordsAccidental FallsDeep LearningGaitHumansMultiple SclerosisProspective StudiesRetrospective StudiesWalkingWearable Electronic DevicesConceptsWearable 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