Shoulder glenohumeral elevation estimation based on upper arm orientation

Name: Shoulder glenohumeral elevation estimation based on upper arm orientation
Author: Hamdan, Sara, Öztop, Erhan, Furukawa, J.-I., Morimoto, J., Uğurlu, Regaip Barkan

İsim	Shoulder glenohumeral elevation estimation based on upper arm orientation
Yazar	Hamdan, Sara, Öztop, Erhan, Furukawa, J.-I., Morimoto, J., Uğurlu, Regaip Barkan
Basım Tarihi:	2018-10-26
Basım Yeri	- IEEE
Konu	Biological system modeling, Predictive models, Shoulder, Ground penetrating radar, Biomechanics, Measurement uncertainty, Q measurement
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-153863646-6
Kayıt Numarası	d256e494-2a84-4996-8439-4146402d4f6b
Lokasyon	Computer Science, Mechanical Engineering
Tarih	2018-10-26
Notlar	New Energy and Industrial Technology Development Organization ; Japan Society for the Promotion of Science ; Cabinet Office, Government of Japan ; Council for Science, Technology and Innovation ; TÜBİTAK ; Japan Agency for Medical Research and Development.
Örnek Metin	In this paper, the shoulder glenohumeral displacement during the movement of the upper arm is studied. Four modeling approaches were examined and compared to estimate the humeral head elevation (vertical displacement) and translation (horizontal displacement). A biomechanics-inspired method was used firstly to model the glenohumeral displacement in which a least squares method was implemented for parameter identification. Then, three Gaussian process regression models were used in which the following variable sets were employed: i) shoulder adduction/abduction angle, ii) combination of shoulder adduction/abduction and flexion/extension angles, iii) overall upper arm orientation in the form of quaternions. In order to test the respective performances of these four models, we collected motion capture data and compared the models' representative capabilities. As a result, Gaussian process regression that considered the overall upper arm orientation outperformed the other modeling approaches; however, it should be noted that the other methods also provided accuracy levels that may be sufficient depending on task requirements.
DOI	10.1109/EMBC.2018.8512564
Cilt	2018

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Shoulder glenohumeral elevation estimation based on upper arm orientation

Yazar Hamdan, Sara, Öztop, Erhan, Furukawa, J.-I., Morimoto, J., Uğurlu, Regaip Barkan

Basım Tarihi 2018-10-26

Basım Yeri - IEEE

Konu Biological system modeling, Predictive models, Shoulder, Ground penetrating radar, Biomechanics, Measurement uncertainty, Q measurement

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-153863646-6

Kayıt Numarası d256e494-2a84-4996-8439-4146402d4f6b

Lokasyon Computer Science, Mechanical Engineering

Tarih 2018-10-26

Notlar New Energy and Industrial Technology Development Organization ; Japan Society for the Promotion of Science ; Cabinet Office, Government of Japan ; Council for Science, Technology and Innovation ; TÜBİTAK ; Japan Agency for Medical Research and Development.

Örnek Metin In this paper, the shoulder glenohumeral displacement during the movement of the upper arm is studied. Four modeling approaches were examined and compared to estimate the humeral head elevation (vertical displacement) and translation (horizontal displacement). A biomechanics-inspired method was used firstly to model the glenohumeral displacement in which a least squares method was implemented for parameter identification. Then, three Gaussian process regression models were used in which the following variable sets were employed: i) shoulder adduction/abduction angle, ii) combination of shoulder adduction/abduction and flexion/extension angles, iii) overall upper arm orientation in the form of quaternions. In order to test the respective performances of these four models, we collected motion capture data and compared the models' representative capabilities. As a result, Gaussian process regression that considered the overall upper arm orientation outperformed the other modeling approaches; however, it should be noted that the other methods also provided accuracy levels that may be sufficient depending on task requirements.

DOI 10.1109/EMBC.2018.8512564

Cilt 2018