CAT & MAUS: A novel system for true dynamic motion measurement of underlying bony structures with compensation for soft tissue movement

Skin marker-based motion analysis has been widely used in biomechanical studies and clinical applications. Unfortunately, the accuracy of knee joint secondary motions is largely limited by the nonrigidity nature of human body segments. Numerous studies have investigated the characteristics of soft tissue movement. Utilizing these characteristics, we may improve the accuracy of knee joint motion measurement. An optimizer was developed by incorporating the soft tissue movement patterns at special bony landmarks into constraint functions. Bony landmark constraints were assigned to the skin markers at femur epicondyles, tibial plateau edges, and tibial tuberosity in a motion analysis algorithm by limiting their allowed position space relative to the underlying bone. The rotation matrix was represented by quaternion, and the constrained optimization problem was solved by Fletcher’s version of the Levenberg–Marquardt optimization technique. The algorithm was validated by using motion data from both skin-based markers and bone-mounted markers attached to fresh cadavers. By comparing the results with the ground truth bone motion generated from the bone-mounted markers, the new algorithm had a significantly higher accuracy (root-mean-square (RMS) error: 0.7±0.1 deg in axial rotation and 0.4±0.1 deg in varus-valgus) in estimating the knee joint secondary rotations than algorithms without bony landmark constraints (RMS error: 1.7±0.4 deg in axial rotation and 0.7±0.1 deg in varus-valgus). Also, it predicts a more accurate medial-lateral translation (RMS error: 0.4±0.1 mm) than the conventional techniques (RMS error: 1.2±0.2 mm). The new algorithm, using bony landmark constrains, estimates more accurate secondary rotations and medial-lateral translation of the underlying bone.

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Soft-Tissue Movement at the Foot During the Stance Phase of Walking

Journal of the American Podiatric Medical Association ◽

10.7547/1010025 ◽

2011 ◽

Vol 101 (1) ◽

pp. 25-34 ◽

Cited By ~ 4

Author(s):

Shing-Jye Chen ◽

Mukherjee Mukul ◽

Li-Shan Chou

Keyword(s):

Young Adults ◽

Soft Tissue ◽

Lower Limb ◽

Stance Phase ◽

Lower Limb Injury ◽

Limb Injury ◽

Motion System ◽

Bony Landmarks ◽

Tissue Movement ◽

Intermarker Distance

Background: Soft-tissue movement has challenged the use of noninvasive skin-based markers that are assumed to be rigidly attached to the underlying bony landmarks. We assessed soft-tissue movement in multiple foot segments by calculating the relative changes in the intermarker distances of the hindfoot, midfoot, and forefoot segments during the early, middle, and late stances of walking compared with the intermarker distances measured while participants remained still during standing. Methods: Seven healthy young adults with no previous lower-limb injury were tested while walking barefoot at a comfortable pace. Skin-based markers were placed on three foot regions (hindfoot-calcaneus, midfoot-navicular, and forefoot–first to fifth metatarsals). A motion system sampled at 120 Hz was used to capture the foot markers during the stance phase of walking. Results: Soft-tissue movement was found in the forefoot region characterized by shortened distances, specifically during early (breaking) stance and late (propulsion) stance. In the hindfoot region, soft-tissue movement was characterized by shortened and elongated distances during the early and late stance periods, respectively. All of the foot regions showed the least intermarker distance changes during midstance. Conclusions: The dynamics of soft-tissue movement in multiple foot segments were characterized by the greatest changes in the intermarker distances in the forefoot and hindfoot during the early and late stance phases and the least changes in the foot segments during midstance. The results provide a feasible and accessible measurement for assessing soft-tissue movement in the foot when skin-based motion markers are used. (J Am Podiatr Med Assoc 101(1): 25–34, 2011)

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