中風是全球造成長期失能的主要原因之一，而上肢運動功能恢復仍是一項重要的復健挑戰。傳統鏡像治療可提供視覺回饋，但缺乏實際的動作輔助；機器人輔助治療則可提供重複性的動作訓練，卻未能充分結合鏡像視覺回饋。因此，若能同步整合視覺與本體感覺回饋，將有助於促進雙側上肢協調及大腦皮質功能重組。
本研究分為兩部分。第一部分開發並驗證一套視覺－動作雙鏡機器人輔助上肢訓練系統，整合雙目立體視覺、MediaPipe 無標記動作追蹤技術及 Universal Robots UR16e 機器手臂。系統可即時重建主動側上肢之三維運動軌跡，並以鏡像方式透過機器手臂引導對側上肢同步運動，以提供同步的視覺與動作回饋。驗證包含三項實驗：（1）以 Kestrel 4200 光學動作捕捉系統為基準，於六名受試者、五種動作模式下評估無標記三維手腕追蹤之準確度；（2）於六名受試者、五種任務與兩種動作速度下，評估機器手臂再現所追蹤動作之準確度與重測信度；（3）於 18 名受試者、三種任務下，比較機器人鏡像治療（Robot-Mirror Therapy, RMT）與機器人治療（Robot Therapy, RT）條件之雙側手部軌跡相似度與上肢關節角度對稱性。第二部分則評估本系統之神經生理效應。
無標記追蹤相對於動作捕捉系統之三維 RMSE 為 0.98–1.60 公分，軸相關係數為 0.87–0.99。機器手臂再現人體動作之延遲為 0.52–0.59 秒，時間校正後之 RMSE 於平移任務為 0.95–1.98 公分、於前臂旋前／旋後為 2.87–3.52°，軸相關係數為 0.97–0.99，機器人與手部之速度比為 0.91–1.08；手部與機器手臂之旋轉活動度未達顯著差異（快速：99.06 ± 5.10° 對 94.95 ± 5.89°，p = .116；慢速：100.69 ± 3.82° 對 97.21 ± 3.71°，p = .173）。重測信度為中等至優良（同日 ICC = 0.72–0.98；跨日 ICC = 0.73–0.97）。雙側動作時，主動側與被動側手腕之肢間延遲為 0.33–0.41 秒；經延遲補償後，各軸相關係數達 0.90–0.99，三維 RMSE 由 35.82–40.91 mm 降至 22–26 mm。關節活動範圍大致呈雙側對稱，鏡子之有無僅在一項比較中造成差異（Reach-to-90° 旋前／旋後任務之肘關節屈伸最大角度比值）；在機器手臂直接驅動之自由度上，被動側活動範圍略小於主動側（左／右 ROM 於前臂旋前／旋後約 88–92 %、於肘關節屈伸約 92–94 %）。
神經生理評估共招募 21 名健康成人，在機器人鏡像治療（Robot-Mirror Therapy, RMT）、機器人治療（Robot Therapy, RT）、鏡像治療（Mirror Therapy, MT）、動作想像（Motor Imagery, MI）及無治療（No Treatment, NT）五種條件下執行模擬倒水任務，並利用功能性近紅外光譜（functional near-infrared spectroscopy, fNIRS）量測初級運動皮質（primary motor cortex, M1）、初級體感皮質（primary somatosensory cortex, S1）、前運動皮質（premotor cortex, PMC）、下頂葉小葉（inferior parietal lobule, IPL）及楔前葉（precuneus, PC）之腦部活化情形。結果顯示，雙側 M1、雙側 PMC 及左側 S1 之 RMT 活化高於 MT、MI 與 NT；右側 S1 之 RMT 活化高於 MI 與 NT；右側 IPL 之 RMT 活化高於 NT；左側 PC 之 RMT 活化高於 MI 與 NT（皆 q < .05）。RT 於右側 M1 與雙側 PMC 之活化高於 NT。左側 IPL 與右側 PC 未發現條件間的顯著差異。在側化指標方面，RMT 於所有 ROI 均未呈現顯著側化，顯示較為均衡的雙側參與。各條件間之功能性連結則無顯著差異。
綜合上述結果，本研究所提出之視覺－動作雙鏡機器人上肢訓練系統具備良好的動作追蹤精度、穩定性及雙側動作同步性，並能有效提供同步的視覺與本體感覺回饋，提升感覺動作皮質活化，顯示其具有應用於中風後上肢復健之潛力。

Stroke is a leading cause of long-term disability worldwide, and upper-limb motor recovery remains challenging. Conventional mirror therapy provides visual feedback but lacks physical assistance, whereas robot-assisted therapy offers repetitive movement training without fully utilizing mirror-induced visual feedback. Combining synchronized visual and proprioceptive feedback may therefore better promote bilateral motor coordination and cortical reorganization.
This study was conducted in two parts. Part I developed and validated a visual-and-motion dual-mirror robot-assisted upper-limb training system integrating stereo vision, MediaPipe-based markerless tracking, and a Universal Robots UR16e collaborative robot. The system reconstructs the active limb's three-dimensional motion and mirrors it to guide the contralateral limb through the robotic arm in real time, providing simultaneous visual and motion feedback. Validation comprised three experiments: (1) the accuracy of markerless three-dimensional wrist tracking, benchmarked against a Kestrel 4200 optical motion-capture system in six participants across five movement patterns; (2) the accuracy and test–retest reliability of the robotic reproduction of the tracked motion, assessed in six participants across five tasks at two movement speeds; and (3) bilateral hand-trajectory similarity and upper-extremity joint-angle symmetry in 18 participants performing three tasks under Robot-Mirror Therapy (RMT) and Robot Therapy (RT). Part II then evaluated the neurophysiological effects of the system.
Markerless tracking achieved a three-dimensional RMSE of 0.98–1.60 cm with axis correlations of 0.87–0.99 relative to motion capture. The robotic arm reproduced human movement with a latency of 0.52–0.59 s, a time-shifted RMSE of 0.95–1.98 cm for translational tasks and 2.87–3.52° for forearm pronation–supination, targeted-axis correlations of 0.97–0.99, and robot-to-hand velocity ratios of 0.91–1.08; rotational range of motion did not differ significantly between the hand and the robot (fast: 99.06 ± 5.10° vs. 94.95 ± 5.89°, p = .116; slow: 100.69 ± 3.82° vs. 97.21 ± 3.71°, p = .173). Test–retest reliability was moderate-to-excellent (within-day ICC = 0.72–0.98; between-day ICC = 0.73–0.97). During bilateral movement, the active and passive wrists moved with an inter-limb delay of 0.33–0.41 s; after latency compensation, per-axis correlations reached 0.90–0.99 and the residual three-dimensional RMSE fell from 35.82–40.91 mm to 22–26 mm. Joint excursions were largely symmetric, and the presence of the mirror altered symmetry in only one comparison (maximum elbow flexion–extension ratio in Reach-to-90° Pronation–Supination); the passive limb consistently under-rotated relative to the active limb in the directly driven degrees of freedom (left/right ROM ≈ 88–92 % for forearm pronation–supination and 92–94 % for elbow flexion–extension).
For the neurophysiological evaluation, 21 healthy adults performed a water-pouring task under five conditions—Robot-Mirror Therapy (RMT), Robot Therapy (RT), Mirror Therapy (MT), Motor Imagery (MI), and No Treatment (NT)—while functional near-infrared spectroscopy (fNIRS) measured activation in the primary motor cortex (M1), primary somatosensory cortex (S1), premotor cortex (PMC), inferior parietal lobule (IPL), and precuneus (PC). In the bilateral M1, the bilateral PMC, and the left S1, RMT showed higher activation than MT, MI, and NT; in the right S1, RMT showed higher activation than MI and NT; in the right IPL, RMT showed higher activation than NT; and in the left PC, RMT showed higher activation than MI and NT (all q < .05). RT showed higher activation than NT in the right M1 and the bilateral PMC. No significant between-condition differences were found in the left IPL or the right PC. For the laterality index, RMT showed no significant lateralization in any ROI, indicating balanced bilateral engagement. No significant between-condition differences were found in functional connectivity.
These findings suggest that the proposed system provides synchronized visual–proprioceptive feedback and has potential for post-stroke upper-limb rehabilitation.

[1] V. L. Feigin et al., “World Stroke Organization (WSO): Global Stroke Fact Sheet 2022,” Int. J. Stroke, vol. 17, no. 1, pp. 18–29, Jan. 2022, doi: 10.1177/17474930211065917.
[2] C.-Y. Hsieh, W.-C. Tsao, R.-T. Lin, and A.-C. Chao, “Three years of the nationwide post-acute stroke care program in Taiwan,” J. Chin. Med. Assoc., vol. 81, no. 1, pp. 87–88, Jan. 2018, doi: 10.1016/j.jcma.2017.09.003.
[3] J. Bernhardt et al., “Agreed definitions and a shared vision for new standards in stroke recovery research: The Stroke Recovery and Rehabilitation Roundtable taskforce,” Int. J. Stroke, vol. 12, no. 5, pp. 444–450, Jul. 2017, doi: 10.1177/1747493017711816.
[4] H. T. Hendricks, J. Van Limbeek, A. C. Geurts, and M. J. Zwarts, “Motor recovery after stroke: A systematic review of the literature,” Arch. Phys. Med. Rehabil., vol. 83, no. 11, pp. 1629–1637, Nov. 2002, doi: 10.1053/apmr.2002.35473.
[5] D. J. Clarke and A. Forster, “Improving post-stroke recovery: the role of the multidisciplinary health care team,” J. Multidiscip. Healthc., vol. 8, pp. 433–442, Sep. 2015, doi: 10.2147/JMDH.S68764.
[6] A. C. Lo et al., “Robot-Assisted Therapy for Long-Term Upper-Limb Impairment after Stroke,” N. Engl. J. Med., vol. 362, no. 19, pp. 1772–1783, May 2010, doi: 10.1056/NEJMoa0911341.
[7] J. Mehrholz, M. Pohl, T. Platz, J. Kugler, and B. Elsner, “Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke,” Cochrane Database Syst. Rev., vol. 2018, no. 9, Sep. 2018, doi: 10.1002/14651858.CD006876.pub5.
[8] V. S. Ramachandran and E. L. Altschuler, “The use of visual feedback, in particular mirror visual feedback, in restoring brain function,” Brain, vol. 132, no. 7, pp. 1693–1710, Jul. 2009, doi: 10.1093/brain/awp135.
[9] E. S. Donkor, “Stroke in the 21st Century: A Snapshot of the Burden, Epidemiology, and Quality of Life,” Stroke Res. Treat., vol. 2018, no. 1, p. 3238165, 2018, doi: 10.1155/2018/3238165.
[10] P. Langhorne, J. Bernhardt, and G. Kwakkel, “Stroke rehabilitation,” The Lancet, vol. 377, no. 9778, pp. 1693–1702, May 2011, doi: 10.1016/S0140-6736(11)60325-5.
[11] T. Platz, Ed., Clinical pathways in stroke rehabilitation: evidence-based clinical practice recommendations. Cham, Switzerland: Springer, 2021.
[12] S. Brunnstrom, “Motor Testing Procedures in Hemiplegia: Based on Sequential Recovery Stages,” Phys. Ther., vol. 46, no. 4, pp. 357–375, Apr. 1966, doi: 10.1093/ptj/46.4.357.
[13] C. J. Winstein et al., “Guidelines for Adult Stroke Rehabilitation and Recovery: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association,” Stroke, vol. 47, no. 6, Jun. 2016, doi: 10.1161/STR.0000000000000098.
[14] V. S. Ramachandran and D. Rogers-Ramachandran, “Synaesthesia in phantom limbs induced with mirrors,” Proc. R. Soc. Lond. B Biol. Sci., vol. 263, no. 1369, pp. 377–386, Apr. 1996, doi: 10.1098/rspb.1996.0058.
[15] G. Rizzolatti and L. Craighero, “THE MIRROR-NEURON SYSTEM,” Annu. Rev. Neurosci., vol. 27, no. 1, pp. 169–192, Jul. 2004, doi: 10.1146/annurev.neuro.27.070203.144230.
[16] H. Thieme et al., “Mirror therapy for improving motor function after stroke,” Cochrane Database Syst. Rev., vol. 2018, no. 7, p. CD008449, Jul. 2018, doi: 10.1002/14651858.CD008449.pub3.
[17] C. Dohle, J. Püllen, A. Nakaten, J. Küst, C. Rietz, and H. Karbe, “Mirror Therapy Promotes Recovery From Severe Hemiparesis: A Randomized Controlled Trial,” Neurorehabil. Neural Repair, vol. 23, no. 3, pp. 209–217, Mar. 2009, doi: 10.1177/1545968308324786.
[18] H.-Y. Hsu, L.-C. Kuo, Y.-C. Lin, F.-C. Su, T.-H. Yang, and C.-W. Lin, “Effects of a Virtual Reality–Based Mirror Therapy Program on Improving Sensorimotor Function of Hands in Chronic Stroke Patients: A Randomized Controlled Trial,” Neurorehabil. Neural Repair, vol. 36, no. 6, pp. 335–345, Jun. 2022, doi: 10.1177/15459683221081430.
[19] C.-W. Lin, L.-C. Kuo, Y.-C. Lin, F.-C. Su, Y.-A. Lin, and H.-Y. Hsu, “Development and Testing of a Virtual Reality Mirror Therapy System for the Sensorimotor Performance of Upper Extremity: A Pilot Randomized Controlled Trial,” IEEE Access, vol. 9, pp. 14725–14734, 2021, doi: 10.1109/ACCESS.2021.3050656.
[20] J. H. Cauraugh, N. Lodha, S. K. Naik, and J. J. Summers, “Bilateral Movement Training and Stroke Motor Recovery Progress: A Structured Review and Meta-Analysis,” Hum. Mov. Sci., vol. 29, no. 5, pp. 853–870, Oct. 2010, doi: 10.1016/j.humov.2009.09.004.
[21] J. H. Cauraugh and J. J. Summers, “Neural plasticity and bilateral movements: A rehabilitation approach for chronic stroke,” Prog. Neurobiol., vol. 75, no. 5, pp. 309–320, Apr. 2005, doi: 10.1016/j.pneurobio.2005.04.001.
[22] S. M. Waller and J. Whitall, “Bilateral arm training: Why and who benefits?,” NeuroRehabilitation, vol. 23, no. 1, pp. 29–41, 2008.
[23] K. Fong, K. Ting, C. Chan, and L. Li, “Mirror therapy with bilateral arm training for hemiplegic upper extremity motor functions in patients with chronic stroke,” vol. 25, no. 1, 2019.
[24] W. Liao, C. Wu, Y. Hsieh, K. Lin, and W. Chang, “Effects of robot-assisted upper limb rehabilitation on daily function and real-world arm activity in patients with chronic stroke: a randomized controlled trial,” Clin. Rehabil., vol. 26, no. 2, pp. 111–120, Feb. 2012, doi: 10.1177/0269215511416383.
[25] W. Liao, C. Wu, Y. Hsieh, K. Lin, and W. Chang, “Effects of robot-assisted upper limb rehabilitation on daily function and real-world arm activity in patients with chronic stroke: a randomized controlled trial,” Clin. Rehabil., vol. 26, no. 2, pp. 111–120, Feb. 2012, doi: 10.1177/0269215511416383.
[26] J. M. Park, H. J. Park, S. Y. Yoon, Y. W. Kim, J. I. Shin, and S. C. Lee, “Effects of Robot-Assisted Therapy for Upper Limb Rehabilitation After Stroke: An Umbrella Review of Systematic Reviews,” Stroke, vol. 56, no. 5, pp. 1243–1252, May 2025, doi: 10.1161/STROKEAHA.124.048183.
[27] N. Sharma, V. M. Pomeroy, and J.-C. Baron, “Motor Imagery: A Backdoor to the Motor System After Stroke?,” Stroke, vol. 37, no. 7, pp. 1941–1952, Jul. 2006, doi: 10.1161/01.STR.0000226902.43357.fc.
[28] A. Y. Kho, K. P. Y. Liu, and R. C. K. Chung, “Meta‐analysis on the effect of mental imagery on motor recovery of the hemiplegic upper extremity function,” Aust. Occup. Ther. J., vol. 61, no. 2, pp. 38–48, Apr. 2014, doi: 10.1111/1440-1630.12084.
[29] J. A. Kleim and T. A. Jones, “Principles of Experience-Dependent Neural Plasticity: Implications for Rehabilitation After Brain Damage,” J. Speech Lang. Hear. Res., vol. 51, no. 1, Feb. 2008, doi: 10.1044/1092-4388(2008/018).
[30] S. C. Cramer, “Repairing the human brain after stroke: I. Mechanisms of spontaneous recovery,” Ann. Neurol., vol. 63, no. 3, pp. 272–287, Mar. 2008, doi: 10.1002/ana.21393.
[31] N. Murase, J. Duque, R. Mazzocchio, and L. G. Cohen, “Influence of interhemispheric interactions on motor function in chronic stroke,” Ann. Neurol., vol. 55, no. 3, pp. 400–409, Mar. 2004, doi: 10.1002/ana.10848.
[32] R. Skopec, “Evolution Continues with Quantum Biology and Artificial Intelligence,” J. Biosens. Bioelectron., vol. 09, no. 04, 2018, doi: 10.4172/2155-6210.1000262.
[33] S. J. H. Yoo, “Precuneus.” Nov. 2025. [Online]. Available: https://radiopaedia.org/articles/precuneus-1?lang=us
[34] D. J. Bell, “Inferior parietal lobule.” Apr. 2021. [Online]. Available: https://radiopaedia.org/articles/inferior-parietal-lobule
[35] C. Grefkes and G. R. Fink, “Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches,” Brain, vol. 134, no. 5, pp. 1264–1276, May 2011, doi: 10.1093/brain/awr033.
[36] S. T. Francis, E. F. Kelly, R. Bowtell, W. J. R. Dunseath, S. E. Folger, and F. McGlone, “fMRI of the Responses to Vibratory Stimulation of Digit Tips,” NeuroImage, vol. 11, no. 3, pp. 188–202, Mar. 2000, doi: 10.1006/nimg.2000.0541.
[37] M. I. Garry, A. Loftus, and J. J. Summers, “Mirror, mirror on the wall: viewing a mirror reflection of unilateral hand movements facilitates ipsilateral M1 excitability,” Exp. Brain Res., vol. 163, no. 1, pp. 118–122, May 2005, doi: 10.1007/s00221-005-2226-9.
[38] M. E. Michielsen et al., “The neuronal correlates of mirror therapy: an fMRI study on mirror induced visual illusions in patients with stroke,” J. Neurol. Neurosurg. Psychiatry, vol. 82, no. 4, pp. 393–398, Apr. 2011, doi: 10.1136/jnnp.2009.194134.
[39] C. M. Stinear, P. A. Barber, J. P. Coxon, M. K. Fleming, and W. D. Byblow, “Priming the motor system enhances the effects of upper limb therapy in chronic stroke,” Brain, vol. 131, no. 5, pp. 1381–1390, May 2008, doi: 10.1093/brain/awn051.
[40] S. Saleh, M. Yarossi, T. Manuweera, S. Adamovich, and E. Tunik, “Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study,” NeuroImage Clin., vol. 13, pp. 46–54, 2017, doi: 10.1016/j.nicl.2016.11.012.
[41] C. M. Stinear, W. D. Byblow, M. Steyvers, O. Levin, and S. P. Swinnen, “Kinesthetic, but not visual, motor imagery modulates corticomotor excitability,” Exp. Brain Res., vol. 168, no. 1–2, pp. 157–164, Jan. 2006, doi: 10.1007/s00221-005-0078-y.
[42] E. D. Vidoni, N. E. Acerra, E. Dao, S. K. Meehan, and L. A. Boyd, “Role of the primary somatosensory cortex in motor learning: An rTMS study,” Neurobiol. Learn. Mem., vol. 93, no. 4, pp. 532–539, May 2010, doi: 10.1016/j.nlm.2010.01.011.
[43] W. R. Staines, W. E. McIlroy, S. J. Graham, and S. E. Black, “Bilateral movement enhances ipsilesional cortical activity in acute stroke: A pilot functional MRI study,” Neurology, vol. 56, no. 3, pp. 401–404, Feb. 2001, doi: 10.1212/WNL.56.3.401.
[44] E. Naito et al., “Internally Simulated Movement Sensations during Motor Imagery Activate Cortical Motor Areas and the Cerebellum,” J. Neurosci., vol. 22, no. 9, pp. 3683–3691, May 2002, doi: 10.1523/JNEUROSCI.22-09-03683.2002.
[45] J. Grèzes and J. Decety, “Functional anatomy of execution, mental simulation, observation, and verb generation of actions: A meta‐analysis,” Hum. Brain Mapp., vol. 12, no. 1, pp. 1–19, Nov. 2000, doi: 10.1002/1097-0193(200101)12:1<1::AID-HBM10>3.0.CO;2-V.
[46] T. Hanakawa, I. Immisch, K. Toma, M. A. Dimyan, P. Van Gelderen, and M. Hallett, “Functional Properties of Brain Areas Associated With Motor Execution and Imagery,” J. Neurophysiol., vol. 89, no. 2, pp. 989–1002, Feb. 2003, doi: 10.1152/jn.00132.2002.
[47] G. Berlucchi and S. Aglioti, “The body in the brain: neural bases of corporeal awareness,” Trends Neurosci., vol. 20, no. 12, pp. 560–564, Dec. 1997, doi: 10.1016/S0166-2236(97)01136-3.
[48] F. Hamzei, C. H. Läppchen, V. Glauche, I. Mader, M. Rijntjes, and C. Weiller, “Functional Plasticity Induced by Mirror Training: The Mirror as the Element Connecting Both Hands to One Hemisphere,” Neurorehabil. Neural Repair, vol. 26, no. 5, pp. 484–496, Jun. 2012, doi: 10.1177/1545968311427917.
[49] K. Matthys et al., “Mirror-Induced Visual Illusion of Hand Movements: A Functional Magnetic Resonance Imaging Study,” Arch. Phys. Med. Rehabil., vol. 90, no. 4, pp. 675–681, Apr. 2009, doi: 10.1016/j.apmr.2008.09.571.
[50] N. Wenderoth, F. Debaere, S. Sunaert, and S. P. Swinnen, “The role of anterior cingulate cortex and precuneus in the coordination of motor behaviour,” Eur. J. Neurosci., vol. 22, no. 1, pp. 235–246, Jul. 2005, doi: 10.1111/j.1460-9568.2005.04176.x.
[51] A. Solodkin, P. Hlustik, E. E. Chen, and S. L. Small, “Fine Modulation in Network Activation during Motor Execution and Motor Imagery,” Cereb. Cortex, vol. 14, no. 11, pp. 1246–1255, Nov. 2004, doi: 10.1093/cercor/bhh086.
[52] A. E. Cavanna and M. R. Trimble, “The precuneus: a review of its functional anatomy and behavioural correlates,” Brain, vol. 129, no. 3, pp. 564–583, Mar. 2006, doi: 10.1093/brain/awl004.
[53] F. Zhang et al., “MediaPipe Hands: On-device Real-time Hand Tracking,” Jun. 18, 2020, arXiv: arXiv:2006.10214. doi: 10.48550/arXiv.2006.10214.
[54] G. Di Leo, C. Liguori, and A. Paolillo, “Propagation of uncertainty through stereo triangulation,” in 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings, May 2010, pp. 12–17. doi: 10.1109/IMTC.2010.5488057.
[55] T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature,” Geosci. Model Dev., vol. 7, no. 3, pp. 1247–1250, Jun. 2014, doi: 10.5194/gmd-7-1247-2014.
[56] D. Kiracofe, “Using cross-correlation to line up two periodic signals.” 2014. doi: https://www.mechanicalvibration.com/tutorial.html.
[57] T. K. Koo and M. Y. Li, “A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research,” J. Chiropr. Med., vol. 15, no. 2, pp. 155–163, Jun. 2016, doi: 10.1016/j.jcm.2016.02.012.
[58] G. Dotti, M. Caruso, D. Fortunato, M. Knaflitz, A. Cereatti, and M. Ghislieri, “A Statistical Approach for Functional Reach-to-Grasp Segmentation Using a Single Inertial Measurement Unit,” Sensors, vol. 24, no. 18, p. 6119, Sep. 2024, doi: 10.3390/s24186119.
[59] M. Salminen, J. Perttunen, J. Avela, and A. Vehkaoja, “A novel method for accurate division of the gait cycle into seven phases using shank angular velocity,” Gait Posture, vol. 111, pp. 1–7, Jun. 2024, doi: 10.1016/j.gaitpost.2024.04.006.
[60] F. Klein, S. Debener, K. Witt, and C. Kranczioch, “fMRI-based validation of continuous-wave fNIRS of supplementary motor area activation during motor execution and motor imagery,” Sci. Rep., vol. 12, no. 1, p. 3570, Mar. 2022, doi: 10.1038/s41598-022-06519-7.
[61] F. F. Jöbsis, “Noninvasive, Infrared Monitoring of Cerebral and Myocardial Oxygen Sufficiency and Circulatory Parameters,” Science, vol. 198, no. 4323, pp. 1264–1267, Dec. 1977, doi: 10.1126/science.929199.
[62] M. Cope and D. T. Delpy, “System for long-term measurement of cerebral blood and tissue oxygenation on newborn infants by near infra-red transillumination,” Med. Biol. Eng. Comput., vol. 26, no. 3, pp. 289–294, May 1988, doi: 10.1007/BF02447083.
[63] A. Villringer and B. Chance, “Non-invasive optical spectroscopy and imaging of human brain function,” Trends Neurosci., vol. 20, no. 10, pp. 435–442, Oct. 1997, doi: 10.1016/S0166-2236(97)01132-6.
[64] G. Strangman, J. P. Culver, J. H. Thompson, and D. A. Boas, “A Quantitative Comparison of Simultaneous BOLD fMRI and NIRS Recordings during Functional Brain Activation,” NeuroImage, vol. 17, no. 2, pp. 719–731, Oct. 2002, doi: 10.1006/nimg.2002.1227.
[65] S. R. Soekadar, S. H. Kohl, M. Mihara, and A. Von Lühmann, “Optical brain imaging and its application to neurofeedback,” NeuroImage Clin., vol. 30, p. 102577, 2021, doi: 10.1016/j.nicl.2021.102577.
[66] G. Rizzolatti and C. Sinigaglia, “The functional role of the parieto-frontal mirror circuit: interpretations and misinterpretations,” Nat. Rev. Neurosci., vol. 11, no. 4, pp. 264–274, Apr. 2010, doi: 10.1038/nrn2805.
[67] M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” NeuroImage, vol. 63, no. 2, pp. 921–935, Nov. 2012, doi: 10.1016/j.neuroimage.2012.03.049.
[68] M. L. Seghier, “Laterality index in functional MRI: methodological issues,” Magn. Reson. Imaging, vol. 26, no. 5, pp. 594–601, Jun. 2008, doi: 10.1016/j.mri.2007.10.010.
[69] C.-M. Lu, Y.-J. Zhang, B. B. Biswal, Y.-F. Zang, D.-L. Peng, and C.-Z. Zhu, “Use of fNIRS to assess resting state functional connectivity,” J. Neurosci. Methods, vol. 186, no. 2, pp. 242–249, Feb. 2010, doi: 10.1016/j.jneumeth.2009.11.010.
[70] R. C. Mesquita, M. A. Franceschini, and D. A. Boas, “Resting state functional connectivity of the whole head with near-infrared spectroscopy,” Biomed. Opt. Express, vol. 1, no. 1, pp. 324–336, Jul. 2010, doi: 10.1364/BOE.1.000324.
[71] R. A. Fisher, “Frequency Distribution of the Values of the Correlation Coefficient in Samples from an Indefinitely Large Population,” Biometrika, vol. 10, no. 4, p. 507, May 1915, doi: 10.2307/2331838.
[72] R. G. Carson, “Neural pathways mediating bilateral interactions between the upper limbs,” Brain Res. Rev., vol. 49, no. 3, pp. 641–662, Nov. 2005, doi: 10.1016/j.brainresrev.2005.03.005.
[73] F. J. A. Deconinck, A. R. P. Smorenburg, A. Benham, A. Ledebt, M. G. Feltham, and G. J. P. Savelsbergh, “Reflections on Mirror Therapy: A Systematic Review of the Effect of Mirror Visual Feedback on the Brain,” Neurorehabil. Neural Repair, vol. 29, no. 4, pp. 349–361, May 2015, doi: 10.1177/1545968314546134.
[74] Y. Benjamini and Y. Hochberg, “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing,” J. R. Stat. Soc. Ser. B Methodol., vol. 57, no. 1, pp. 289–300, 1995.
[75] F. Faul, E. Erdfelder, A.-G. Lang, and A. Buchner, “G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences,” Behav. Res. Methods, vol. 39, no. 2, pp. 175–191, May 2007, doi: 10.3758/BF03193146.
[76] Y. Inagaki, M. Nakatsuka, Y. Naito, and D. Sawamura, “Topography of Cortical Activation with Mirror Visual Feedback and Electromyography-Triggered Electrical Stimulation: A Functional Near-Infrared Spectroscopy Study in Healthy Older Adults,” Brain Sci., vol. 15, no. 10, p. 1074, Oct. 2025, doi: 10.3390/brainsci15101074.
[77] J. Cohen, Statistical power analysis for the behavioral sciences, 2. ed., Reprint. New York, NY: Psychology Press, 2009.
[78] E. Girden, ANOVA. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 1992. doi: 10.4135/9781412983419.
[79] D. Lakens, “Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs,” Front. Psychol., vol. 4, 2013, doi: 10.3389/fpsyg.2013.00863.
[80] J. S. Kelso, D. L. Southard, and D. Goodman, “On the coordination of two-handed movements.,” J. Exp. Psychol. Hum. Percept. Perform., vol. 5, no. 2, pp. 229–238, 1979, doi: 10.1037/0096-1523.5.2.229.
[81] Y. Liu, “A REVIEW OF BIMANUAL COORDINATION MEASUREMENTS,” 2023.
[82] G. Feng et al., “Effective unilateral/bilateral robot-assisted training for upper limb motor function rehabilitation: a cross-sectional study,” Front. Hum. Neurosci., vol. 19, p. 1571624, Jun. 2025, doi: 10.3389/fnhum.2025.1571624.
[83] E. K. Vonstad, X. Su, B. Vereijken, K. Bach, and J. H. Nilsen, “Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training,” Sensors, vol. 20, no. 23, p. 6940, Dec. 2020, doi: 10.3390/s20236940.
[84] L. Wade, L. Needham, P. McGuigan, and J. Bilzon, “Applications and limitations of current markerless motion capture methods for clinical gait biomechanics,” PeerJ, vol. 10, p. e12995, Feb. 2022, doi: 10.7717/peerj.12995.
[85] P. I. Corke and M. C. Good, “Dynamic effects in visual closed-loop systems,” IEEE Trans. Robot. Autom., vol. 12, no. 5, pp. 671–683, Oct. 1996, doi: 10.1109/70.538973.
[86] S. Hesse, G. Schulte-Tigges, M. Konrad, A. Bardeleben, and C. Werner, “Robot-assisted arm trainer for the passive and active practice of bilateral forearm and wrist movements in hemiparetic subjects11An organization with which 1 or more of the authors is associated has received or will receive financial benefits from a commercial party having a direct financial interest in the results of the research supporting this article.,” Arch. Phys. Med. Rehabil., vol. 84, no. 6, pp. 915–920, Jun. 2003, doi: 10.1016/S0003-9993(02)04954-7.
[87] H. I. Krebs et al., “Robot-Aided Neurorehabilitation: A Robot for Wrist Rehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 15, no. 3, pp. 327–335, Sep. 2007, doi: 10.1109/TNSRE.2007.903899.
[88] A. Frisoli et al., “Positive effects of robotic exoskeleton training of upper limb reaching movements after stroke,” J. NeuroEngineering Rehabil., vol. 9, p. 36, Jun. 2012, doi: 10.1186/1743-0003-9-36.
[89] H. Rodgers et al., “Robot assisted training for the upper limb after stroke (RATULS): a multicentre randomised controlled trial,” The Lancet, vol. 394, no. 10192, pp. 51–62, Jul. 2019, doi: 10.1016/S0140-6736(19)31055-4.
[90] Y. Zimmermann et al., “Human–Robot Attachment System for Exoskeletons: Design and Performance Analysis,” IEEE Trans. Robot., vol. 39, no. 4, pp. 3087–3105, Aug. 2023, doi: 10.1109/TRO.2023.3268587.
[91] Z. Bons, T. Dickinson, R. Clark, K. Beardsley, and S. K. Charles, “Compensating for Soft-Tissue Artifact Using the Orientation of Distal Limb Segments During Electromagnetic Motion Capture of the Upper Limb,” J. Biomech. Eng., vol. 144, no. 7, p. 071007, Jul. 2022, doi: 10.1115/1.4053366.
[92] E. Papaleo et al., “Upper-limb kinematic reconstruction during stroke robot-aided therapy,” Med. Biol. Eng. Comput., vol. 53, no. 9, pp. 815–828, Sep. 2015, doi: 10.1007/s11517-015-1276-9.
[93] M. E. Khallaf, “Effect of Gravity and Task Specific Training of Elbow Extensors on Upper Extremity Function after Stroke,” Neurol. Res. Int., vol. 2018, pp. 1–9, Jul. 2018, doi: 10.1155/2018/4172454.
[94] H. Reddy, “Functional brain reorganization for hand movement in patients with multiple sclerosis: defining distinct effects of injury and disability,” Brain, vol. 125, no. 12, pp. 2646–2657, Dec. 2002, doi: 10.1093/brain/awf283.
[95] C. Weiller et al., “Brain Representation of Active and Passive Movements,” NeuroImage, vol. 4, no. 2, pp. 105–110, Oct. 1996, doi: 10.1006/nimg.1996.0034.
[96] M. N. Lafitte et al., “Cerebral Correlates of Robot-Assisted Upper Limb Motion Driven by Motor Intention in Healthy Individuals: An fNIRS Study,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 33, pp. 2322–2334, 2025, doi: 10.1109/TNSRE.2025.3578796.
[97] W. Li, G. Zhu, Y. Jiang, C. Miao, G. Zhang, and D. Xu, “Cortical response characteristics of passive, active, and resistance movements: a multi-channel fNRIS study,” Front. Hum. Neurosci., vol. 18, p. 1419140, Aug. 2024, doi: 10.3389/fnhum.2024.1419140.
[98] M. Muthalib et al., “Effects of Increasing Neuromuscular Electrical Stimulation Current Intensity on Cortical Sensorimotor Network Activation: A Time Domain fNIRS Study,” PLOS ONE, vol. 10, no. 7, p. e0131951, Jul. 2015, doi: 10.1371/journal.pone.0131951.
[99] H. Ding et al., “The role of ipsilateral motor network in upper limb movement,” Front. Physiol., vol. 14, p. 1199338, Jul. 2023, doi: 10.3389/fphys.2023.1199338.
[100] D. T. Bundy and E. C. Leuthardt, “The Cortical Physiology of Ipsilateral Limb Movements,” Trends Neurosci., vol. 42, no. 11, pp. 825–839, Nov. 2019, doi: 10.1016/j.tins.2019.08.008.
[101] Y. Qiu et al., “Synergistic Immediate Cortical Activation on Mirror Visual Feedback Combined With a Soft Robotic Bilateral Hand Rehabilitation System: A Functional Near Infrared Spectroscopy Study,” Front. Neurosci., vol. 16, p. 807045, Feb. 2022, doi: 10.3389/fnins.2022.807045.
[102] D. H. Kim, K.-D. Lee, T. C. Bulea, and H.-S. Park, “Increasing motor cortex activation during grasping via novel robotic mirror hand therapy: a pilot fNIRS study,” J. NeuroEngineering Rehabil., vol. 19, no. 1, p. 8, Dec. 2022, doi: 10.1186/s12984-022-00988-7.
[103] P.-H. Chang et al., “The cortical activation pattern by a rehabilitation robotic hand: a functional NIRS study,” Front. Hum. Neurosci., vol. 8, 2014, doi: 10.3389/fnhum.2014.00049.
[104] J. Limanowski and F. Blankenburg, “Integration of Visual and Proprioceptive Limb Position Information in Human Posterior Parietal, Premotor, and Extrastriate Cortex,” J. Neurosci., vol. 36, no. 9, pp. 2582–2589, Mar. 2016, doi: 10.1523/JNEUROSCI.3987-15.2016.
[105] T. Zama, Y. Takahashi, and S. Shimada, “Simultaneous EEG-NIRS Measurement of the Inferior Parietal Lobule During a Reaching Task With Delayed Visual Feedback,” Front. Hum. Neurosci., vol. 13, p. 301, Sep. 2019, doi: 10.3389/fnhum.2019.00301.
[106] M. Diers, C. Christmann, C. Koeppe, M. Ruf, and H. Flor, “Mirrored, imagined and executed movements differentially activate sensorimotor cortex in amputees with and without phantom limb pain,” Pain, vol. 149, no. 2, pp. 296–304, May 2010, doi: 10.1016/j.pain.2010.02.020.
[107] R. M. Hardwick, S. Caspers, S. B. Eickhoff, and S. P. Swinnen, “Neural correlates of action: Comparing meta-analyses of imagery, observation, and execution,” Neurosci. Biobehav. Rev., vol. 94, pp. 31–44, Nov. 2018, doi: 10.1016/j.neubiorev.2018.08.003.
[108] S. Hétu et al., “The neural network of motor imagery: An ALE meta-analysis,” Neurosci. Biobehav. Rev., vol. 37, no. 5, pp. 930–949, Jun. 2013, doi: 10.1016/j.neubiorev.2013.03.017.
[109] S.-G. Kim et al., “Functional Magnetic Resonance Imaging of Motor Cortex: Hemispheric Asymmetry and Handedness,” Science, vol. 261, no. 5121, pp. 615–617, Jul. 1993, doi: 10.1126/science.8342027.
[110] T. Verstynen, J. Diedrichsen, N. Albert, P. Aparicio, and R. B. Ivry, “Ipsilateral Motor Cortex Activity During Unimanual Hand Movements Relates to Task Complexity,” J. Neurophysiol., vol. 93, no. 3, pp. 1209–1222, Mar. 2005, doi: 10.1152/jn.00720.2004.
[111] M. E. Stoykov and S. Madhavan, “Motor Priming in Neurorehabilitation,” J. Neurol. Phys. Ther., vol. 39, no. 1, pp. 33–42, Jan. 2015, doi: 10.1097/NPT.0000000000000065.
[112] M. W. Cole, T. Ito, D. Schultz, R. Mill, R. Chen, and C. Cocuzza, “Task activations produce spurious but systematic inflation of task functional connectivity estimates,” NeuroImage, vol. 189, pp. 1–18, Apr. 2019, doi: 10.1016/j.neuroimage.2018.12.054.
[113] T. Ito, L. J. Hearne, and M. W. Cole, “A cortical hierarchy of localized and distributed processes revealed via dissociation of task activations, connectivity changes, and intrinsic timescales,” NeuroImage, vol. 221, p. 117141, Nov. 2020, doi: 10.1016/j.neuroimage.2020.117141.