Analisa Upper-limb Movement Sequence (UMS) menggunakan Absolute Trajectory Error dan Hand Speed Movement untuk Rehabilitasi Pasca Stroke
Abstract
2019 World Health Organization informed that the two-thirds of stroke patients have a permanent dissability. Disability disorders can affect performance or daily activities such as eating, drinking, wearing clothes, bathing, and others. Disability caused by stroke can be treated by exercising motor function and muscle strength. Studies in upper extremity rehabilitation report that the most important element in the disability recovery process is monitoring the progress of the rehabilitation itself. To monitor and evaluate upper arm rehabilitation, most therapists still rely on clinical assessments based on ordinal scales or charts and are only monitored in terms of the patient's upper arm movement. In this study, we will analyze the rehabilitation movement based on virtual reality games using the Absolute Trajectory Error and Hand Speed Movement methods. While the rehabilitation movement used in this study will apply the Upper-limb Movement Sequence (UMS) method. 5 subjects contributed to data collection over three sessions and five iterations. Their movements were recorded using the Kinect Xbox V2 sensor with 10 Hz sampling data. Mean absolute trajectory error (ATE) and hand speed method were used to analyze arm movements during VR games. Although this study used healthy subjects, 80% of them experienced an increase in movement, and this condition was evidenced by a decrease in ATE values in each session. Trajectory data can be used as the basis for analysis of arm movements during the VR game rehabilitation process, where with these data errors in hand position, hand speed to reach the target, and movement errors can be analyzed more deeply. Moreover, the mean ATE and hand speed movements can show the progress or changes in hand movement during the rehabilitation process clearly.
Downloads
References
J. Kim et al., “Global Stroke Statistics 2019,” International Journal of Stroke, vol. 15, no. 8. pp. 819–838, 2020. doi: 10.1177/1747493020909545.
M. Molinari and M. Masciullo, “Stroke and potential benefits of brain-computer interface,” Handb. Clin. Neurol., vol. 168, pp. 25–32, Jan. 2020, doi: 10.1016/B978-0-444-63934-9.00003-2.
B. Yu et al., “The Effects of the Biceps Brachii and Brachioradialis on Elbow Flexor Muscle Strength and Spasticity in Stroke Patients,” Neural Plast., vol. 2022, 2022, doi: 10.1155/2022/1295908.
B. N. Sahyudi et al., “Investigation of upper limb movement for VR based post stroke rehabilitation device,” Proc. - 2018 IEEE 14th Int. Colloq. Signal Process. its Appl. CSPA 2018, pp. 52–56, May 2018, doi: 10.1109/CSPA.2018.8368684.
B. N. Cahyadi et al., “Muscle Fatigue Detections during Arm Movement using EMG Signal,” in IOP Conference Series: Materials Science and Engineering, 2019, vol. 557, no. 1. doi: 10.1088/1757-899X/557/1/012004.
S. S. Mahmoud, Z. Cao, J. Fu, X. Gu, and Q. Fang, “Occupational Therapy Assessment for Upper Limb Rehabilitation: A Multisensor-Based Approach.,” Front. Digit. Heal., vol. 3, p. 784120, Dec. 2021, doi: 10.3389/fdgth.2021.784120.
N. H. Ismail et al., “Investigation of upper arm muscle activation for the progress monitoring in stroke rehabilitation,” AIP Conf. Proc., vol. 2045, Dec. 2018, doi: 10.1063/1.5080841.
M. S. H. Majid, W. Khairunizam, A. B. Shahriman, I. Zunaidi, B. N. Sahyudi, and M. Zuradzman, “EMG Feature Extractions for Upper-Limb Functional Movement during Rehabilitation,” 2018 Int. Conf. Intell. Informatics Biomed. Sci. ICIIBMS 2018, pp. 314–320, Nov. 2018, doi: 10.1109/ICIIBMS.2018.8549932.
R. Suhaimi, K. S. Talha, K. Wan, and M. A. Ariffin, “Design of movement sequences for arm rehabilitation of post-stroke,” Proc. - 5th IEEE Int. Conf. Control Syst. Comput. Eng. ICCSCE 2015, pp. 320–324, May 2016, doi: 10.1109/ICCSCE.2015.7482205.
Z. L. Htoon, S. N. I. Sidek, S. Fatai, and T. Yunahar, “Assessment of upper limb muscle tone level based on estimated impedance parameters,” IECBES 2016 - IEEE-EMBS Conf. Biomed. Eng. Sci., pp. 742–747, 2016, doi: 10.1109/IECBES.2016.7843549.
M. Trombetta, P. P. Bazzanello Henrique, M. R. Brum, E. L. Colussi, A. C. B. De Marchi, and R. Rieder, “Motion Rehab AVE 3D: A VR-based exergame for post-stroke rehabilitation,” Comput. Methods Programs Biomed., vol. 151, pp. 15–20, Nov. 2017, doi: 10.1016/J.CMPB.2017.08.008.
M. Kutlu, C. T. Freeman, E. Hallewell, A. M. Hughes, and D. S. Laila, “Upper-limb stroke rehabilitation using electrode-array based functional electrical stimulation with sensing and control innovations,” Med. Eng. Phys., vol. 38, no. 4, pp. 366–379, Apr. 2016, doi: 10.1016/J.MEDENGPHY.2016.01.004.
C. Duret, O. Courtial, and A. G. Grosmaire, “Kinematic measures for upper limb motor assessment during robot-mediated training in patients with severe sub-acute stroke,” Restor. Neurol. Neurosci., vol. 34, no. 2, pp. 237–245, Mar. 2016, doi: 10.3233/RNN-150565.
S. S. Esfahlani, B. Muresan, A. Sanaei, and G. Wilson, “Validity of the Kinect and Myo armband in a serious game for assessing upper limb movement,” Entertain. Comput., vol. 27, pp. 150–156, Aug. 2018, doi: 10.1016/J.ENTCOM.2018.05.003.
H. L. Lee, W. Khairunizam, B. N. Cahyadi, W. A. Mustafa, and S. Z. S. Idrus, “Progress Monitoring in Upper Limb Stroke Rehabilitation by Using Muscle Activation and Hand Speed,” J. Phys. Conf. Ser., vol. 1529, no. 4, Jun. 2020, doi: 10.1088/1742-6596/1529/4/042019.
B. N. Cahyadi et al., “Upper Limb Muscle Strength Analysis for Movement Sequence Based on Maximum Voluntary Contraction Using EMG Signal,” 2018. doi: 10.1109/ICASSDA.2018.8477638.
H. S. Lee, Y. J. Park, and S. W. Park, “The effects of virtual reality training on function in chronic stroke patients: A systematic review and meta-analysis,” Biomed Res. Int., vol. 2019, 2019, doi: 10.1155/2019/7595639.
J. Huang, M. Lin, J. Fu, Y. Sun, and Q. Fang, “An Immersive Motor Imagery Training System for Post-Stroke Rehabilitation Combining VR and EMG-based Real-Time Feedback,” Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Int. Conf., vol. 2021, pp. 7590–7593, Nov. 2021, doi: 10.1109/EMBC46164.2021.9629767.
W.-S. Kim et al., “Clinical Application of Virtual Reality for Upper Limb Motor Rehabilitation in Stroke: Review of Technologies and Clinical Evidence.,” J. Clin. Med., vol. 9, no. 10, pp. 1–20, Oct. 2020, doi: 10.3390/jcm9103369.
H. Ai, A. Zhu, J. Wang, X. Yu, and L. Chen, “Buffer compliance control of space robots capturing a non‐cooperative spacecraft based on reinforcement learning,” Appl. Sci., vol. 11, no. 13, Jul. 2021, doi: 10.3390/APP11135783.
“Distance between points - Math Open Reference.” https://www.mathopenref.com/coorddist.html (accessed Sep. 25, 2022).