Clinical Whole-Body Gait Characterization Using a Single RGB-D Sensor
Instrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer v...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/333 |
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| author | Lukas Boborzi Johannes Bertram Roman Schniepp Julian Decker Max Wuehr |
| author_facet | Lukas Boborzi Johannes Bertram Roman Schniepp Julian Decker Max Wuehr |
| author_sort | Lukas Boborzi |
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| description | Instrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now enable markerless whole-body tracking with high accuracy. Here, we present vGait, a comprehensive 3D gait assessment method using a single RGB-D sensor and state-of-the-art pose-tracking algorithms. vGait was validated in healthy participants during frontal- and sagittal-perspective walking. Performance was comparable across perspectives, with vGait achieving high accuracy in detecting initial and final foot contacts (F1 scores > 95%) and reliably quantifying spatiotemporal gait parameters (e.g., stride time, stride length) and whole-body coordination metrics (e.g., arm swing and knee angle ROM) at different levels of granularity (mean, step-to-step variability, side asymmetry). The flexibility, accuracy, and minimal resource requirements of vGait make it a valuable tool for clinical and non-clinical applications, including outpatient clinics, medical practices, nursing homes, and community settings. By enabling efficient and scalable gait assessment, vGait has the potential to enhance diagnostic and therapeutic workflows and improve access to clinical mobility monitoring. |
| format | Article |
| id | doaj-art-99ec2d69fc73448a95d60726d5283e81 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-99ec2d69fc73448a95d60726d5283e812025-01-24T13:48:33ZengMDPI AGSensors1424-82202025-01-0125233310.3390/s25020333Clinical Whole-Body Gait Characterization Using a Single RGB-D SensorLukas Boborzi0Johannes Bertram1Roman Schniepp2Julian Decker3Max Wuehr4German Center for Vertigo and Balance Disorders (DSGZ), LMU University Hospital, LMU Munich, 81377 Munich, GermanyGerman Center for Vertigo and Balance Disorders (DSGZ), LMU University Hospital, LMU Munich, 81377 Munich, GermanyInstitut für Notfallmedizin und Medizinmanagement (INM), LMU University Hospital, LMU Munich, 80336 Munich, GermanyGerman Center for Vertigo and Balance Disorders (DSGZ), LMU University Hospital, LMU Munich, 81377 Munich, GermanyGerman Center for Vertigo and Balance Disorders (DSGZ), LMU University Hospital, LMU Munich, 81377 Munich, GermanyInstrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now enable markerless whole-body tracking with high accuracy. Here, we present vGait, a comprehensive 3D gait assessment method using a single RGB-D sensor and state-of-the-art pose-tracking algorithms. vGait was validated in healthy participants during frontal- and sagittal-perspective walking. Performance was comparable across perspectives, with vGait achieving high accuracy in detecting initial and final foot contacts (F1 scores > 95%) and reliably quantifying spatiotemporal gait parameters (e.g., stride time, stride length) and whole-body coordination metrics (e.g., arm swing and knee angle ROM) at different levels of granularity (mean, step-to-step variability, side asymmetry). The flexibility, accuracy, and minimal resource requirements of vGait make it a valuable tool for clinical and non-clinical applications, including outpatient clinics, medical practices, nursing homes, and community settings. By enabling efficient and scalable gait assessment, vGait has the potential to enhance diagnostic and therapeutic workflows and improve access to clinical mobility monitoring.https://www.mdpi.com/1424-8220/25/2/333gait analysisgait disordersmotion trackingpose trackingRGB-D sensor |
| spellingShingle | Lukas Boborzi Johannes Bertram Roman Schniepp Julian Decker Max Wuehr Clinical Whole-Body Gait Characterization Using a Single RGB-D Sensor Sensors gait analysis gait disorders motion tracking pose tracking RGB-D sensor |
| title | Clinical Whole-Body Gait Characterization Using a Single RGB-D Sensor |
| title_full | Clinical Whole-Body Gait Characterization Using a Single RGB-D Sensor |
| title_fullStr | Clinical Whole-Body Gait Characterization Using a Single RGB-D Sensor |
| title_full_unstemmed | Clinical Whole-Body Gait Characterization Using a Single RGB-D Sensor |
| title_short | Clinical Whole-Body Gait Characterization Using a Single RGB-D Sensor |
| title_sort | clinical whole body gait characterization using a single rgb d sensor |
| topic | gait analysis gait disorders motion tracking pose tracking RGB-D sensor |
| url | https://www.mdpi.com/1424-8220/25/2/333 |
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