Human movement analysis using stereophotogrammetry. Part 3. Soft tissue artifact assessment and compensation.

By Alberto Leardini, L. Chiari, U. Della Croce, A. Cappozzo. Gait & Posture, 2005

1,153 citations recorded by the source corpus.

Subfields: Endocrinology, Diabetes and Metabolism, Physical Therapy, Sports Therapy and Rehabilitation, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/44cb9938bb56ccb6f82a14e0f27159c4a364da48

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Endocrinology, Diabetes and Metabolism

1. Gerard Pons-Moll
2. Alberto Leardini
3. Michael J. Black
4. Todd C. Pataky

In Physical Therapy, Sports Therapy and Rehabilitation

1. Scott L. Delp
2. Thomas K. Uchida
3. Ajay Seth
4. Daniel Roetenberg
5. Alberto Leardini
6. Henk Luinge

In Radiology, Nuclear Medicine and Imaging

1. Gerard Pons-Moll
2. Michael J. Black

Citations in the corpus

• A new anatomically based protocol for gait analysis in children. (2007, background): “The sources of variability associated with measurement errors are discussed above and elsewhere [28,46].”
• AMASS: Archive of Motion Capture As Surface Shapes (2019, background): “Most of the work in the mocap community focuses on minimizing the effect of skin deformations on the marker motions [12, 24].”
• Validation of inertial measurement units with an optoelectronic system for whole-body motion analysis (2016, background): “Since anthropometry can play a large role in the accuracy of motion analysis [18], it seems important to test a sufficient number of subjects of various body dimensions.”
• Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors (2009, background): “contracting muscles (active) and skin and fat (passive) [17].”
• A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System (2018, background): “As soft tissue movement introduces both systematic and random errors of similar frequency to the actual bone movement, this is difficult to attenuate through data smoothing [56].”
• Rear-foot, mid-foot and fore-foot motion during the stance phase of gait. (2007, background): “Although a very cautious analysis of this motion data is necessary [11,46], the clinical and biomechanical information gained is essential [11].”
• Is my model good enough? Best practices for verification and validation of musculoskeletal models and simulations of movement. (2015, methodology): “Traditional marker-based motion capture systems generally have system errors of 1–5 mm [35], which is combined with soft tissue artifacts of up to 10 mm for human movement [36].”
• MoSh (2014, background): “Interestingly, much of the attention paid to soft-tissue motion in the mocap community (particularly within biomechanics) actually focuses on minimizing the effects of soft tissue dynamics [Leardini et al. 2005].”