Evaluation of RGB-D Multi-Camera Pose Estimation for 3D Reconstruction

On the reliability of single-camera markerless systems for overground gait monitoring

BACKGROUND AND OBJECTIVE

Motion analysis is crucial for effective and timely rehabilitative interventions on people with motor disorders. Conventional marker-based (MB) gait analysis is highly time-consuming and calls for expensive equipment, dedicated facilities and personnel. Markerless (ML) systems may pave the way to less demanding gait monitoring, also in unsupervised environments (i.e., in telemedicine). However,scepticism on clinical usability of relevant outcome measures has hampered its use. ML is normally used to analyse treadmill walking, which is significantly different from the more physiological overground walking. This study aims to provide end-users with instructions on using a single-camera markerless system to obtain reliable motion data from overground walking, while clinicians will be instructed on the reliability of obtained quantities.

METHODS

The study compares kinematics obtained from ML systems to those concurrently obtained from marker-based systems, considering different stride counts and subject positioning within the capture volume.

RESULTS

The findings suggest that five straight walking trials are sufficient for collecting reliable kinematics with ML systems. Precision on joint kinematics decreased at the boundary of the capture volume. Excellent correlation was found between ML and MB systems for hip and knee angles (0.92

CONCLUSION

Single-camera markerless motion capture systems have great potential in assessing human joint kinematics during overground walking. Clinicians can confidently rely on estimated joint kinematics while walking, enabling personalized interventions and improving accessibility to remote evaluation and rehabilitation services, as long as: (i) the camera is positioned to capture someone walking back and forth at least five times with good visibility of the entire body silhouette; (ii) the walking path is at least 2 m long; and (iii) images captured at the boundaries of the camera image plane should be discarded.

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Key Words

• Clinical evaluation
• Human joint kinematics
• Human pose estimation
• Motion analysis
• Reference data
• Telemedicine

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MESH Headings

• Humans
• Gait Analysis
• Reproducibility of Results
• Gait/physiology
• Walking/physiology
• Knee Joint/physiology
• Biomechanical Phenomena

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Affiliation(s)

Michele Boldo Department of Computer Science, University of Verona, Strada Le Grazie, 15, Verona, 37134, Italy.
Roberto Di Marco Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie, 15, Verona, 37134, Italy.
Enrico Martini Department of Computer Science, University of Verona, Strada Le Grazie, 15, Verona, 37134, Italy.
Mauro Nardon Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Via Casorati, 43, Verona, 37131, Italy.
Matteo Bertucco Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Via Casorati, 43, Verona, 37131, Italy.
Nicola Bombieri Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie, 15, Verona, 37134, Italy.

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