Intelligent Frozen Shoulder Rehabilitation

被引:21
|
作者
Huang, Ming-Chun [1 ]
Lee, Si-Huei [2 ,3 ]
Yeh, Shih-Ching [4 ]
Chan, Rai-Chi [2 ,5 ]
Rizzo, Albert [6 ]
Xu, Wenyao [7 ]
Wu Han-Lin [2 ]
Lin Shan-Hui [2 ]
机构
[1] Case Western Reserve Univ, Dept Elect Engn & Comp Sci, Cleveland, OH 44106 USA
[2] Taipei Vet Gen Hosp, Dept Phys Med & Rehabil, Taipei, Taiwan
[3] Taipei Vet Gen Hosp, Virtual Real Lab, Taipei, Taiwan
[4] Natl Cent Univ, Dept Comp Sci & Informat Engn, Jhongli, Taiwan
[5] Taipei Vet Gen Hosp, Rehabil Ctr, Taipei, Taiwan
[6] Univ So Calif, Inst Creat Technol, Med Virtual Real Grp, Los Angeles, CA 90089 USA
[7] SUNY Buffalo, Dept Comp Sci & Engn, Buffalo, NY 14260 USA
关键词
MOVEMENT; GAMES;
D O I
10.1109/MIS.2014.35
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Frozen shoulder, or adhesive capsulitis, which reportedly affects 2â5 percent of the general population, is a shoulder condition characterized by painful and limited active and passive range of motion. The main treatment involves applying proper shoulder exercises and joint mobilization to break up adhesions at the joint capsules and improve joint mobility and functions. However, due to a lack of persistence, not all patients complete rehabilitation. To address this concern, this study focused on providing interactive treatments to encourage patients to participate in regular rehabilitation. Patients can inquire freely about their rehabilitation progress with real-time sensing and game-based feedback. In addition, six progressive and hierarchical training tasks make each training step adjustable based on the patient's physical condition. The authors used standard randomized clinical trial criterion to recruit 40 patients for a sequence of trials over a four-week period. The evaluation of the study group revealed that shoulder joint mobility and muscle strength of the patients significantly improved compared to that achieved by the traditional rehabilitation method. © 2014 IEEE.
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页码:22 / 28
页数:7
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