Jeonghoon Oh1, Moataz Eltoukhy2, Christopher Kuenze3, Michael S Andersen4, Joseph F Signorile5. 1. Department of Kinesiology and Sport Sciences, School of Education & Human Development, University of Miami, Coral Gables, FL, 33143, USA. 2. Department of Kinesiology and Sport Sciences, School of Education & Human Development, University of Miami, Coral Gables, FL, 33143, USA. Electronic address: meltoukhy@miami.edu. 3. Department of Kinesiology, School of Education, Michigan State University, East Lansing, MI, 48824, USA. 4. Department of Materials and Production, Aalborg University, Fibigerstraede 16, 9220, Aalborg East, Denmark. 5. Department of Kinesiology and Sport Sciences, School of Education & Human Development, University of Miami, Coral Gables, FL, 33143, USA; Center on Aging, Miller School of Medicine, 1695 N.W. 9th Avenue, Suite 3204, Miami, FL, 33136, USA.
Abstract
BACKGROUND: Abnormalities in gait kinetics in patients with Parkinson's disease (PD) who have suffer from gait impairment have been noted using a conventional inverse dynamic analysis derived by marker-based motion capture system and force plate, which are typically mounted in the laboratory floor. Despite the high accuracy of this approach in tracking markers' trajectories and acquiring ground reaction forces (GRFs), its dependence on laboratory-mounted equipment restricts its potential use in wider variety of clinical applications. RESEARCH QUESTION: Would a full-body musculoskeletal model driven by a single depth sensor data only produce comparable gait kinetic parameters, including GRFs and lower extremity joints moments, for elderly participants, both healthy and those diagnosed with PD? METHODS: Nine patients diagnosed with PD and 11 healthy age-matched control participants performed three over-ground gait trials. Full-body kinematic data were collected using a depth sensor and a musculoskeletal model have been constructed using AnyBody musculoskeletal modeling system to predict the three-dimensional GRFs and lower extremity joint moments. Predicted kinetic parameters for both PD and control groups were compared during the braking and propulsive phases of the gait cycle. In addition, ensemble curve analysis with 90% confidence intervals were constructed to compare between group differences across the stance phase of the gait cycle. RESULTS: The findings of this study showed that the PD exhibited a significantly lower braking peak vertical GRF and propulsion peak horizontal GRF while no significant between-group differences were found in peak lower extremity joint moments. However, the PD showed significant alterations in lower extremity joint moments during the early and late phases of stance, which indicate a difference in ambulation strategy. SIGNIFICANCE: The proposed method adopting full-body musculoskeletal model driven by a depth sensor data proves that it has the potential to be a portable and cost-effective gait analysis tool in the clinical setting.
BACKGROUND: Abnormalities in gait kinetics in patients with Parkinson's disease (PD) who have suffer from gait impairment have been noted using a conventional inverse dynamic analysis derived by marker-based motion capture system and force plate, which are typically mounted in the laboratory floor. Despite the high accuracy of this approach in tracking markers' trajectories and acquiring ground reaction forces (GRFs), its dependence on laboratory-mounted equipment restricts its potential use in wider variety of clinical applications. RESEARCH QUESTION: Would a full-body musculoskeletal model driven by a single depth sensor data only produce comparable gait kinetic parameters, including GRFs and lower extremity joints moments, for elderly participants, both healthy and those diagnosed with PD? METHODS: Nine patients diagnosed with PD and 11 healthy age-matched control participants performed three over-ground gait trials. Full-body kinematic data were collected using a depth sensor and a musculoskeletal model have been constructed using AnyBody musculoskeletal modeling system to predict the three-dimensional GRFs and lower extremity joint moments. Predicted kinetic parameters for both PD and control groups were compared during the braking and propulsive phases of the gait cycle. In addition, ensemble curve analysis with 90% confidence intervals were constructed to compare between group differences across the stance phase of the gait cycle. RESULTS: The findings of this study showed that the PD exhibited a significantly lower braking peak vertical GRF and propulsion peak horizontal GRF while no significant between-group differences were found in peak lower extremity joint moments. However, the PD showed significant alterations in lower extremity joint moments during the early and late phases of stance, which indicate a difference in ambulation strategy. SIGNIFICANCE: The proposed method adopting full-body musculoskeletal model driven by a depth sensor data proves that it has the potential to be a portable and cost-effective gait analysis tool in the clinical setting.