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Kinematic and Stress Analysis of Metacarpophalangeal Joint Implants
Published in J. Middleton, M. L. Jones, G. N. Pande, Computer Methods in Biomechanics & Biomedical Engineering – 2, 2020
J.M.T. Penrose, N.W. Williams, D.R. Hose, E.A. Trowbridge
Rheumatoid arthritis and post-traumatic osteo-arthritis are very common conditions affecting the metacarpophalangeal (MCP) joints in the human hand. These conditions are often very painful and are also associated with deformity and a reduction in function. Whilst permanent fixation of the joint (arthrodesis) is often performed in the distal and proximal interphalangeal joints, it is contraindicated at the metacarpophalangeal joints due to the importance of an adequate range of motion for many manual tasks. Various prostheses and implants have been developed for the reconstruction of the metacarpophalangeal joint, all designed to restore a functional range of motion as well as correct existing, and prevent further, deformity. They also help reduce pain in the joint and give a cosmetic improvement.
Upper extremity injuries
Published in Youlian Hong, Roger Bartlett, Routledge Handbook of Biomechanics and Human Movement Science, 2008
Ronald F. Zernicke, William C. Whiting, Sarah L. Manske
Thumb injuries: The most common hand sprain is the ulnar collateral ligament of the first metacarpophalangeal joint. The sprain is colloquially termed gamekeeper’s thumb or skier’s thumb as it commonly occurs when a skier falls onto an outstretched hand with the thumb in an abducted position (Figure 28.5). The handle of the ski pole holds the thumb in abduction as the load of the fall is absorbed by the hand, placing excessive tensile forces on the ulnar collateral ligament. The other common mechanism for ulnar collateral ligament sprain is hyperextension of the first metacarpophalangeal joint. This commonly occurs in a collision between two athletes, such as the hand of a softball player tagging an opponent sliding into a base.
The influence of body segment estimation methods on body segment inertia parameters and joint moments in javelin throwing
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Hans-Peter Köhler, Axel Schüler, Felix Quaas, Hannes Fiedler, Maren Witt, Karen Roemer
Data processing covered the time frame between the right leg touchdown and javelin release. Kinematic data were filtered using a fourth-order, zero-lag Butterworth filter, in which the cut-off frequency for each marker was determined individually via residual-analysis (Winter 2009). The cut-off frequencies were between 10 and 13 Hz. A generic model was built consisting of six rigid segments (javelin, right hand, forearm, upper arm, thorax, abdomen) using Visual 3D Software (Ver. 6.03.06, C-Motion, Germantown, USA). The orientation of each segment was estimated using the six degrees of freedom algorithm. The shoulder joint center was determined using functional methods implemented into Visual 3D (Schwartz and Rozumalski 2005). The joint centers of the elbow and the wrist were determined as the midpoints between the medial and lateral humeral epicondyles and the midpoints between the ulnar and radial styloid. For the segments upper arm, forearm, and hand the segment coordinate systems were built as follows: The longitudinal axis was defined as the line connecting the joint center of the proximal and distal joint of the respective segment (Figure 1). For the hand, the proximal end was defined as the midpoint between the metacarpophalangeal joint of the 2nd and 5th finger. The anterior/posterior axis was calculated as the cross product between the longitudinal axis and the line connecting the two distal markers of each segment. The medial/lateral axis was computed as the cross product of the longitudinal and anterior/posterior axis.
Comparative study between fully tethered and free swimming at different paces of swimming in front crawl
Published in Sports Biomechanics, 2019
Mathias Samson, Tony Monnet, Anthony Bernard, Patrick Lacouture, Laurent David
All the aquatic measurements were recorded from an optoelectronic system composed of eight Vicon® T-40 cameras (Motion Capture System, UK) configured with 12.5 mm lenses. A CCD sensor with a resolution of 2,352 × 1,728 (4,064,256) pixels and a circular array of 320 red (623 nm) LEDs were mounted around each camera. The sampling frequency was 200 Hz, and the dimension of the calibrated volume was about 2 m long, 1 m wide and 1 m high. Seven markers (diameter 14 mm) were fixed on the right wrist and hand: tip of the third finger (FT), fifth metacarpophalangeal joint (M5), second metacarpophalangeal joint (M2), palmar side of the fifth metacarpophalangeal joint (M5i), palmar side of the second metacarpophalangeal joint (M2i), radial styloid (RS) and ulnar styloid (US) (Monnet, Samson, Bernard, David, & Lacouture, 2014) (Figure 2(a)).
Development of an upper-limb neuroprosthesis to voluntarily control elbow and hand
Published in Advanced Robotics, 2018
Yosuke Ogiri, Yusuke Yamanoi, Wataru Nishino, Ryu Kato, Takehiko Takagi, Hiroshi Yokoi
The robot hand has two degrees of freedom of motion in the carpometacarpal joint (CM joint) of the thumb and metacarpophalangeal joint (MP joint) of the other four fingers [11]. Two compact servomotors (Hyperion Atlas HP-DH16-FTD, max torque 12.0 kgf cm) are used for these drive joints. Using this, it is possible to obtain three different hand griping postures: power grip, precision grip, and lateral grip, as shown in Figure 2. In addition, the ring finger and little finger fixed with springs are passive and support three kinds of griping postures. It is reported that these three gripping postures cover 85% of the activities of daily living (ADL) [12]. The size of the hand chosen is the average value of female adults. The hand parts are molded using 3D layered modeling (ABS resin). Moreover, a stable grip is enabled by attaching an elastomer glove simulating the finger pad and palm [13].