Using Microsoft Kinect V2 for Custom Upper-Limb Rehabilitation Exercises

Keywords

• upper limb rehabilitation
• Kinect v2
• stroke rehabilitation
• 3D motion capture
• exercise prescription
• telerehabilitation
• home-based exercise
• visual feedback

CCDS Authors

Prof. Amin Ahsan Ali, PhD

Prof. Amin Ahsan Ali, PhD

• Director, AI/ML Wing
• Supervisor, ComBio Wing

• 

  Professor

  Department of Computer Science and Engineering

  Independent University, Bangladesh (IUB)

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Prof. M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

• Founder Director, CCDS
• Director, AI/ML, HCI & DS Wings

• 

  Professor

  Department of Information Sciences and Technology

  George Mason University, USA

• 

  Honorary Professor

  Department of Computer Science and Engineering

  Independent University, Bangladesh (IUB)

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, Pattern Recognition, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

References

1. 1.Yao‐Jen Chang, Shu-Fang Chen, Jun-Da Huang. (2011). A Kinect-based system for physical rehabilitation: A pilot study for young adults with motor disabilities. Research in Developmental Disabilities, 32(6), 2566–2570[10.1016/j.ridd.2011.07.002]
2. 2.Julie Sanford, Julie Moreland, Laurie R. Swanson, Paul W. Stratford, Carolyn Gowland. (1993). Reliability of the Fugl-Meyer Assessment for Testing Motor Performance in Patients Following Stroke. Physical Therapy, 73(7), 447–454[10.1093/ptj/73.7.447]
3. 3.Seung-Hee Lee, Yang‐Soo Lee, Jonghyun Kim. (2017). Automated Evaluation of Upper-Limb Motor Function Impairment Using Fugl-Meyer Assessment. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(1), 125–134[10.1109/tnsre.2017.2755667]
4. 4.Richard J. Adams, Matthew D. Lichter, Eileen Krepkovich, Allison Ellington, Marga White, Paul T. Diamond. (2014). Assessing Upper Extremity Motor Function in Practice of Virtual Activities of Daily Living. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23(2), 287–296[10.1109/tnsre.2014.2360149]
5. 5.Rajib Chowdhury, ATM Hasibul Hasan, Yusuf Ur Rahman, Shafikul Islam Khan, Ahmed Riyad Hussain, Shamim Ahsan. (2014). Pattern of neurological disease seen among patients admitted in tertiary care hospital. BMC Research Notes, 7(1), 202[10.1186/1756-0500-7-202]
6. 6.Shayne Lin, Jotvarinder Mann, Avril Mansfield, Rosalie H. Wang, Jocelyn E. Harris, Babak Taati. (2019). Investigating the feasibility and acceptability of real-time visual feedback in reducing compensatory motions during self-administered stroke rehabilitation exercises: A pilot study with chronic stroke survivors. Journal of Rehabilitation and Assistive Technologies Engineering, 6, 2055668319831631[10.1177/2055668319831631]
7. 7.I M Moldovan, L Tric, R Ursu, Anushka Podar, A Calin, A C Cantea, L A Dascalu, Cosmin Mihaiu. (2017). Virtual rehabilitation programme using the MIRA platform, Kinect and Leap Motion sensors in an 81 years old patient with ischemic stroke. , 325–328[10.1109/ehb.2017.7995427]
8. 8.Chung-Liang Lai, Yaling Huang, Tzu-Kuan Liao, Chien-Ming Tseng, Yung-Fu Chen, D. Erdenetsogt. (2015). A Microsoft Kinect-Based Virtual Rehabilitation System to Train Balance Ability for Stroke Patients. , 54–60[10.1109/cw.2015.44]
9. 9.Marika Demers, Daniel Chan Chun Kong, Mindy F. Levin. (2017). Acceptability of using a Kinect-based virtual reality intervention to remediate arm motor impairments in subacute stroke. , 1–2[10.1109/icvr.2017.8007496]
10. 10.Ummay Kulsum Urmi. (2017). Barriers of stroke patients during receiving treatment at CRP.