Outcome evaluation in shoulder surgery using 3D kinematics sensors

Coley, Brian; Jolles, Brigitte M.; Farron, Alain; Bourgeois, Aline; Nussbaumer, F.; Pichonnaz, Claude; Aminian, Kamiar

doi:10.1016/j.gaitpost.2006.06.016

Cited by 64 publications

(89 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case 150 • of abduction was achieved in 0.7 s. The last movement was also a simple abduction with (t) = 0 and (t) = 0, but Â(t) was obtained from in vivo recorded data [25].…”

Section: Kinematicsmentioning

confidence: 99%

A musculoskeletal shoulder model based on pseudo-inverse and null-space optimization

Terrier

Aeberhard

Michellod

et al. 2010

Medical Engineering & Physics

Self Cite

View full text Add to dashboard Cite

ARTICLE IN PRESSThe goal of the present work was assess the feasibility of using a pseudo-inverse and null-space optimization approach in the modeling of the shoulder biomechanics. The method was applied to a simplified musculoskeletal shoulder model. The mechanical system consisted in the arm, and the external forces were the arm weight, 6 scapulo-humeral muscles and the reaction at the glenohumeral joint, which was considered as a spherical joint. The muscle wrapping was considered around the humeral head assumed spherical. The dynamical equations were solved in a Lagrangian approach. The mathematical redundancy of the mechanical system was solved in two steps: a pseudo-inverse optimization to minimize the square of the muscle stress and a null-space optimization to restrict the muscle force to physiological limits. Several movements were simulated. The mathematical and numerical aspects of the constrained redundancy problem were efficiently solved by the proposed method. The prediction of muscle moment arms was consistent with cadaveric measurements and the joint reaction force was consistent with in vivo measurements.This preliminary work demonstrated that the developed algorithm has a great potential for more complex musculoskeletal modeling of the shoulder joint. In particular it could be further applied to a non-spherical joint model, allowing for the natural translation of the humeral head in the glenoid fossa.

show abstract

“…In this case 150 • of abduction was achieved in 0.7 s. The last movement was also a simple abduction with (t) = 0 and (t) = 0, but Â(t) was obtained from in vivo recorded data [25].…”

Section: Kinematicsmentioning

confidence: 99%

A musculoskeletal shoulder model based on pseudo-inverse and null-space optimization

Terrier

Aeberhard

Michellod

et al. 2010

Medical Engineering & Physics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Each motion sensor consists of a tri-axial accelerometer and gyrometer, collecting three-dimensional linear acceleration (A x , A y , A z ) and three dimensional angular velocity (G x , G y , G z ,) at a sampling frequency of 500Hz. This sampling frequency is much higher when compared with the sensors being used in some other previous studies, ranging from 20 to 200Hz (Bernmark & Wiktorin, 2002;Coley et al, 2007), as to collect adequate data during vigorous sprain motions which happen within 50ms (Fong et al, 2009). …”

Section: Data Collectionmentioning

confidence: 99%

Identification of ankle sprain motion from common sporting activities by dorsal foot kinematics data

Chan¹,

Fong²,

Chung³

et al. 2010

Journal of Biomechanics

View full text Add to dashboard Cite

“…In order to show a clinical application of the proposed methods, one patient suffering from a rotator cuff disease implying a right shoulder supraspinatus rupture of 1 cm 2 (48 years old, right-handed) was studied. In this study two inertial modules with three miniature capacitive gyroscopes (Analog device, ADXRS 250, ±400 deg/s) were fixed by a patch on each dorsal side of the distal humerus and one module with 3D gyroscopes and three miniature accelerometers (Analog device, ADXL 210, ±5 g) on the thorax [2] (Fig. 1).…”

Section: Subjects and Materialsmentioning

confidence: 99%

Detection of the movement of the humerus during daily activity

Coley

Jolles

Farron

et al. 2009

Med Biol Eng Comput

Self Cite

View full text Add to dashboard Cite

A new ambulatory technique for qualitative and quantitative movement analysis of the humerus is presented. 3D gyroscopes attached on the humerus were used to recognize the movement of the arm and to classify it as flexion, abduction and internal/external rotations. The method was first validated in a laboratory setting and then tested on 31 healthy volunteer subjects while carrying the ambulatory system during 8 h of their daily life. For each recording, the periods of sitting, standing and walking during daily activity were detected using an inertial sensor attached on the chest. During each period of daily activity the type of arm movement (flexion, abduction, internal/external rotation) its velocity and frequency (number of movement/hour) were estimated. The results showed that during the whole daily activity and for each activity (i.e. walking, sitting and walking) the frequency of internal/external rotation was significantly higher while the frequency of abduction was the lowest (P < 0.009). In spite of higher number of flexion, abduction and internal/external rotation in the dominant arm, we have not observed in our population a significant difference with the non-dominant arm, implying that in healthy subjects the arm dominance does not lie considerably on the number of movements. As expected, the frequency of the movement increased from sitting to standing and from standing to walking, while we provide a quantitative value of this change during daily activity. This study provides preliminary evidence that this system is a useful tool for objectively assessing upper-limb activity during daily activity. The results obtained with the healthy population could be used as control data to evaluate arm movement of patients with shoulder diseases during daily activity.

show abstract

Outcome evaluation in shoulder surgery using 3D kinematics sensors

Cited by 64 publications

References 16 publications

A musculoskeletal shoulder model based on pseudo-inverse and null-space optimization

A musculoskeletal shoulder model based on pseudo-inverse and null-space optimization

Identification of ankle sprain motion from common sporting activities by dorsal foot kinematics data

Detection of the movement of the humerus during daily activity

Contact Info

Product

Resources

About