scholarly journals A musculoskeletal model driven by dual Microsoft Kinect Sensor data

2017 ◽  
Vol 41 (4) ◽  
pp. 297-316 ◽  
Author(s):  
Sebastian Skals ◽  
Kasper P. Rasmussen ◽  
Kaare M. Bendtsen ◽  
Jian Yang ◽  
Michael S. Andersen
2017 ◽  
Vol 26 (12) ◽  
pp. e382-e389 ◽  
Author(s):  
James D. Wilson ◽  
Jennifer Khan-Perez ◽  
Dominic Marley ◽  
Susan Buttress ◽  
Michael Walton ◽  
...  

2012 ◽  
Vol 19 (2) ◽  
pp. 4-10 ◽  
Author(s):  
Zhengyou Zhang

2020 ◽  
pp. 114179
Author(s):  
Lourdes Ramirez Cerna ◽  
Edwin Escobedo Cardenas ◽  
Dayse Garcia Miranda ◽  
David Menotti ◽  
Guillermo Camara-Chavez

Sensors ◽  
2017 ◽  
Vol 17 (2) ◽  
pp. 286 ◽  
Author(s):  
Ali Al-Naji ◽  
Kim Gibson ◽  
Sang-Heon Lee ◽  
Javaan Chahl

2013 ◽  
Vol 2 (4) ◽  
pp. 28-37 ◽  
Author(s):  
Maliheh Fakhar ◽  
Saeed Behzadipour ◽  
Amir Mobini

In this study, motion performance indices based on the kinematics of upper body have been presented and compared to be used in a home-based rehabilitation device. Microsoft Kinect sensor is used to extract and calculate such indices. A set of experiments has been designed and carried out in which, kinematic data of three patients has been recorded. Finally, the selected indices have been calculated, and the results were compared with those of a healthy subject.


2019 ◽  
Vol 277 ◽  
pp. 03005
Author(s):  
Abrar Alharbi ◽  
Fahad Alharbi ◽  
Eiji Kamioka

Human gait is a significant biometric feature used for the identification of people by their style of walking. Gait offers recognition from a distance at low resolution while requiring no user interaction. On the other hand, other biometrics are likely to require a certain level of interaction. In this paper, a human gait recognition method is presented to identify people who are wearing long baggy clothes like Thobe and Abaya. Microsoft Kinect sensor is used as a tool to establish a skeleton based gait database. The skeleton joint positions are obtained and used to create five different datasets. Each dataset contained different combination of joints to explore their effectiveness. An evaluation experiment was carried out with 20 walking subjects, each having 25 walking sequences in total. The results achieved good recognition rates up to 97%.


2020 ◽  
Vol 25 (2) ◽  
pp. 79-94
Author(s):  
Ditte Rudå ◽  
Gudmundur Einarsson ◽  
Jannik Boll Matthiassen ◽  
Christoph U. Correll ◽  
Karsten Gjessing Jensen ◽  
...  

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