中風是一種在老年族群中常見之中樞性神經損傷慢性疾病[1]，患者多數擁有正常行走上的困難，尤其例如單側偏癱之中風在動作上易具有不對稱性。坐式踏步機是越來越廣為使用於復健的下肢阻力式訓練機台，坐式踏步機具備例如風險性低、適用於各年齡層的特性。在許多醫院復健部裡將運動訓練機台納入為各種不同的復健訓練項目，幫助年長者或在行走上有困難的患者恢復其原本的功能[2]。目前在臨床上對於中風患者之下肢復健，經常使用下肢阻力式訓練機台以訓練下肢肌力。單側偏癱中風患者因在動作執行上可能與兩邊不對稱肌力及張力表現有關，鼓勵對稱性出力之視覺回饋或能幫助其動作執行能力[3]。故若是能夠發展一套適合多數中風疾患合適的訓練方法結合進行視覺回饋與下肢阻力式訓練機台訓練應能更產生其正向效果。過去關於各式運動機台之人因設計研究已經定義出機台之預期可調整的許多人因參數，例如：座椅距離、踩踏阻力、踩踏速度。本研究方法為利用紅外線動作擷取系統、無線肌電儀與測力板記錄受試者在兩種機台上進行的運動數據，計算運動學(關節角度、人體質量中心)、力動學(座椅反作用力、座椅壓力中心)與各項時間空間參數並進行探討。本研究目的為，研究以健康年輕族群找到於兩種坐式運動機台上最佳人因設計參數並依此提供給老年族群及中風患者運動復健時的參考依據，同時探討此三族群在運動過程中之生物力學效應。研究結果顯示適中的座椅距離(D2：90%腳長)的座椅距離有最好的下肢關節活動度。高頻率踩踏速度(S2：90 Steps/min)在三族群中均能產生較大的關節角速度峰值以及較穩定的上肢質量中心位移、座椅反作用力與左右側座椅壓力中心位移。高踩踏阻力(R2：5.3kgw)有較明顯的肌電訊號活化。然而視覺回饋在年輕族群及老年族群的作用並不明顯。但在以座椅壓力中心與上肢軀幹傾斜為視覺回饋訊號的條件下中風族群上肢質量中心位移有所減少，研判可能因年輕族群及老年族群均為健康族群，故利用視覺回饋於機台上增進運動模式或表現的程度受到限制，但是對於中風族群來說，視覺回饋應能夠起到讓受試者在完成既定任務的條件前提，維持自身穩定的動作控制。本研究已經針對三種座椅距離、兩種採踏阻力等級於不同踩踏速度下進行研究，例如關於更高強度踩踏阻力的生物力學效應仍然有待驗證。本研究建議未來在執行受試者(或是疾病患者)機台運動復健的生物力學效應之研究時，衡量實驗條件難易設定與實驗效果之間的平衡將是此類型實驗前期前驅實驗設計的關鍵。讓未來此類機台的使用者來可以更有效且明確的規劃訓練計畫內容，確認肌力訓練或復健的效果。

Stroke is a common chronic central nervous system injury in the elderly population [1], the majority of patients with the normal walking difficulties, especially for unilateral hemiplegia in the action is easy to have asymmetry. Sitting machines are increasingly used for rehabilitation of lower limb resistance training machines, such as low risk, suitable for all age. In many hospital rehabilitation departments take sports training machine into a variety of rehabilitation training, and help the elderly or patients who difficult to walk in the recovery of their original function [2]. At present in the clinical rehabilitation of the lower limb for patients with stroke, often use lower limb resistance training machine to train lower limb muscle strength. Unilateral hemiplegia stroke patients may be associated with asymmetric muscle strength and tension, encouraging visual feedback of symmetrical output may help to perform their ability [3]. It is possible to develop a set of training methods suitable for most stroke disorders combined with visual feedback and lower limb resistance training should be able to produce more positive results. In the past, people who have been working on various types of sports machines have defined many of the expected adjustments of the machine due to parameters such as seat distance, step resistance, step speed. In this study, we used the infrared ray motion capture system, the wireless EMG and the force plate to record the motion data of the subjects on the two types of machines, calculate the kinematics (joint angle, center of mass), the kinetics (seat reaction force, seat center of pressure) and the spatial parameters and to explore. The purpose of this study is to find the best human factors designed by healthy young people on the two sitting sports machines and to provide reference for the rehabilitation of the elderly and stroke patients. The results show a moderate seat distance (D2: 90% leg length) has the best lower limb joint activity. High frequency step speed (S2: 90 steps / min) can produce a large joint angular velocity peak and a more stable upper limb center of mass shift, seat reaction force and M-L seat center of pressure displacements. High step resistance (R2: 5.3kgw) has a more significant EMG activation. However, the role of visual feedback in young and old age groups is not obvious. It may be due to young group and elderly group are healthy groups, so the use of visual feedback on the machine to enhance the pattern of movement or the degree of performance is limited. But for the stroke group, the visual feedback should be able to make subjects maintain their own stable action control. This study provides that the future of such machine users can be more effective and clear planning training program content to confirm the effect of muscle training or rehabilitation.

1. Corriveau, H., et al., Evaluation of postural stability in the elderly with stroke. Arch Phys Med Rehabil, 2004. 85(7): p. 1095-101.
2. Oliveira, C.B., et al., Abnormal sensory integration affects balance control in hemiparetic patients within the first year after stroke. Clinics, 2011. 66(12): p. 2043-2048.
3. Seki, K., M. Sato, and Y. Handa, Increase of muscle activities in hemiplegic lower extremity during driving a cycling wheelchair. Tohoku J Exp Med, 2009. 219(2): p. 129-38.
4. Srivastava, A., et al., Bodyweight-supported treadmill training for retraining gait among chronic stroke survivors: A randomized controlled study. Ann Phys Rehabil Med, 2016. 59(4): p. 235-41.
5. De Marchis, C., et al., Feedback of mechanical effectiveness induces adaptations in motor modules during cycling. Frontiers in Computational Neuroscience, 2013. 7(35).
6. Stephenson, J.L., S.J. De Serres, and A. Lamontagne, The effect of arm movements on the lower limb during gait after a stroke. Gait Posture, 2010. 31(1): p. 109-15.
7. Kam, D.d., et al., Arm movements can increase leg muscle activity during submaximal recumbent stepping in neurologically intact individuals. American Physiological Society, 2013: p. 34-42.
8. Billinger, S.A., B.Y. Tseng, and P.M. Kluding, Modified total-body recumbent stepper exercise test for assessing peak oxygen consumption in people with chronic stroke. Phys Ther, 2008. 88(10): p. 1188-95.
9. Wilson, J.R., Fundamentals of systems ergonomics/human factors. Applied Ergonomics, 2014. 45(1): p. 5-13.
10. Association, I.E.
11. Steven J. Scheer, A.M., Ergonomics. Archives of Physical Medicine and Rehabilitation, 1997. 78(3): p. Pages S36-S45.
12. Dul, J., et al., A strategy for human factors/ergonomics: developing the discipline and profession. Ergonomics, 2012. 55(4): p. 377-395.
13. Woude, L.H.V.V.D., et al., Wheelchair ergonomics and physiological testing of prototypes. Ergonomics, 1986. 29(12): p. 1561-1573.
14. GREGOR, R.J., KNEE FLEXOR MOMENTS DURING PROPULSION IN CYCLING-A CREATIVE SOLUTION TO LOMBARD’S PARADOX. Journal of Biomechanics, 1985. 18(5): p. 307-316.
15. JORGE, M.L.H.a.M., A METHOD FOR BIOMECHANICAL ANALYSIS OF BICYCLE PEDALLING. Journal of Biomechanics, 1985. 18(9): p. 631-644.
16. Hug, F. and S. Dorel, Electromyographic analysis of pedaling: a review. J Electromyogr Kinesiol, 2009. 19(2): p. 182-98.
17. Duc, S., et al., Muscular activity during uphill cycling: effect of slope, posture, hand grip position and constrained bicycle lateral sways. J Electromyogr Kinesiol, 2008. 18(1): p. 116-27.
18. Dorel, S., A. Couturier, and F. Hug, Intra-session repeatability of lower limb muscles activation pattern during pedaling. J Electromyogr Kinesiol, 2008. 18(5): p. 857-65.
19. Dagnese, F., et al., Effects of a noncircular chainring system on muscle activation during cycling. J Electromyogr Kinesiol, 2011. 21(1): p. 13-7.
20. Marsh, A.P. and P.E. Martin, The relationship between cadence and lower extremity EMG in cyclists and noncyclists. Med Sci Sports Exerc, 1995. 27(2): p. 217-25.
21. Ericson, M., On the biomechanics of cycling. A study of joint and muscle load during exercise on the bicycle ergometer. Scand J Rehabil Med Suppl, 1986. 16: p. 1-43.
22. Hald, R.D. and E.J. Bottjen, Effect of Visual Feedback on Maximal and Submaximal Isokinetic Test Measurements of Normal Quadriceps and Hamstrings. Journal of Orthopaedic & Sports Physical Therapy, 1987. 9(2): p. 86-93.
23. Jorge, M. and M.L. Hull, Analysis of EMG measurements during bicycle pedalling. J Biomech, 1986. 19(9): p. 683-94.
24. Houtz, S.J. and F.J. Fischer, An analysis of muscle action and joint excursion during exercise on a stationary bicycle. J Bone Joint Surg Am, 1959. 41-a(1): p. 123-31.
25. Faria, E.W., D.L. Parker, and I.E. Faria, The science of cycling: factors affecting performance - part 2. Sports Med, 2005. 35(4): p. 313-37.
26. Neptune, R.R., S.A. Kautz, and M.L. Hull, The effect of pedaling rate on coordination in cycling. J Biomech, 1997. 30(10): p. 1051-8.
27. Burnfield, J.M., et al., Comparative analysis of speed's impact on muscle demands during partial body weight support motor-assisted elliptical training. Gait Posture, 2014. 39(1): p. 314-20.
28. Damiano, D.L., et al., Comparison of elliptical training, stationary cycling, treadmill walking and overground walking. Gait Posture, 2011. 34(2): p. 260-4.
29. Lou, S.Z., et al., Functional Evaluation of Elliptical Trainer with Human Motion Analysis. Journal of Biomechanics, 2007. 40: p. S329.
30. Miller, M.S., J.P. Peach, and T.S. Keller, Electromyographic analysis of a human powered stepper cycle during seated and standing riding. J Electromyogr Kinesiol, 2001. 11(6): p. 413-23.
31. Kao, P.C. and D.P. Ferris, The effect of movement frequency on interlimb coupling during recumbent stepping. Motor Control, 2005. 9(2): p. 144-63.
32. Williams, H. and T. Pountney, Effects of a static bicycling programme on the functional ability of young people with cerebral palsy who are non-ambulant. Dev Med Child Neurol, 2007. 49(7): p. 522-7.
33. Billinger, S.A., J.K. Loudon, and B.J. Gajewski, Validity of a total body recumbent stepper exercise test to assess cardiorespiratory fitness. J Strength Cond Res, 2008. 22(5): p. 1556-62.
34. Jennifer M. Ortman, V.A.V., and Howard Hogan, An Aging Nation: The Older Population in
the United States. 2014.
35. Velkoff, G.K.V.a.V.A., THE NEXT FOUR DECADES The Older Population in the United States: 2010 to 2050. 2010.
36. Frontera, W.R., et al., Muscle fiber size and function in elderly humans: a longitudinal study. J Appl Physiol (1985), 2008. 105(2): p. 637-42.
37. Janssen, I., et al., Skeletal muscle cutpoints associated with elevated physical disability risk in older men and women. Am J Epidemiol, 2004. 159(4): p. 413-21.
38. Gallagher, D., et al., Weight stability masks sarcopenia in elderly men and women. Am J Physiol Endocrinol Metab, 2000. 279(2): p. E366-75.
39. Marques, E.A., et al., Are resistance and aerobic exercise training equally effective at improving knee muscle strength and balance in older women? Arch Gerontol Geriatr, 2017. 68: p. 106-112.
40. Watt, J.R., et al., A three-dimensional kinematic and kinetic comparison of overground and treadmill walking in healthy elderly subjects. Clin Biomech (Bristol, Avon), 2010. 25(5): p. 444-9.
41. Balachandran, A., et al., Functional strength training: Seated machine vs standing cable training to improve physical function in elderly. Exp Gerontol, 2016. 82: p. 131-8.
42. Arampatzis, A., A. Peper, and S. Bierbaum, Exercise of mechanisms for dynamic stability control increases stability performance in the elderly. J Biomech, 2011. 44(1): p. 52-8.
43. Ng, C.L.W., et al., Minimal difference between aerobic and progressive resistance exercise on metabolic profile and fitness in older adults with diabetes mellitus: a randomised trial. Journal of Physiotherapy, 2010. 56(3): p. 163-170.
44. Garber, C.E., et al., American College of Sports Medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc, 2011. 43(7): p. 1334-59.
45. Kerr, A., et al., Specificity of recumbent cycling as a training modality for the functional movements; sit-to-stand and step-up. Clin Biomech (Bristol, Avon), 2007. 22(10): p. 1104-11.
46. Langhorne, P., J. Bernhardt, and G. Kwakkel, Stroke rehabilitation. The Lancet, 2011. 377(9778): p. 1693-1702.
47. Lendraitiene, E., et al., Balance evaluation techniques and physical therapy in post-stroke patients: A literature review. Neurol Neurochir Pol, 2017. 51(1): p. 92-100.
48. Awad, L.N., et al., Effects of repeated treadmill testing and electrical stimulation on post-stroke gait kinematics. Gait Posture, 2013. 37(1): p. 67-71.
49. Kang, T.W., J.H. Lee, and H.S. Cynn, Six-Week Nordic Treadmill Training Compared with Treadmill Training on Balance, Gait, and Activities of Daily Living for Stroke Patients: A Randomized Controlled Trial. J Stroke Cerebrovasc Dis, 2016. 25(4): p. 848-56.
50. Drużbicki, M., et al., The use of a treadmill with biofeedback function in assessment of relearning walking skills in post-stroke hemiplegic patients – a preliminary report. Neurologia i Neurochirurgia Polska, 2010. 44(6): p. 567-573.
51. Dobkin, B.H., Rehabilitation and Recovery of the Patient with Stroke. 2016: p. 963-971.
52. Chen, H.Y., et al., Kinesiological and kinematical analysis for stroke subjects with asymmetrical cycling movement patterns. J Electromyogr Kinesiol, 2005. 15(6): p. 587-95.
53. Mohamed, R.A. and A.E.-A.A. Sherief, Bicycle ergometer versus treadmill on balance and gait parameters in children with hemophilia. Egyptian Journal of Medical Human Genetics, 2015. 16(2): p. 181-187.
54. Dobkin, B.H., Strategies for stroke rehabilitation. The Lancet Neurology, 2004. 3(9): p. 528-536.
55. Stoller, O., et al., Efficacy of Feedback-Controlled Robotics-Assisted Treadmill Exercise to Improve Cardiovascular Fitness Early After Stroke: A Randomized Controlled Pilot Trial. J Neurol Phys Ther, 2015. 39(3): p. 156-65.
56. Walker, E.R., A.S. Hyngstrom, and B.D. Schmit, Influence of visual feedback on dynamic balance control in chronic stroke survivors. J Biomech, 2016. 49(5): p. 698-703.
57. Byl, N., et al., Clinical impact of gait training enhanced with visual kinematic biofeedback: Patients with Parkinson's disease and patients stable post stroke. Neuropsychologia, 2015. 79(Pt B): p. 332-43.
58. Pavare, Z., et al., Gait rehabilitation of post-stroke patients by treadmill gait training with visual feedback. Gait & Posture, 2015. 42: p. S69-S70.
59. Brown, D.A. and S.A. Kautz, Speed-dependent reductions of force output in people with poststroke hemiparesis. Phys Ther, 1999. 79(10): p. 919-30.
60. Yates, J.S., et al., Bicycle ergometry in subacute-stroke survivors: feasibility, safety, and exercise performance. J Aging Phys Act, 2004. 12(1): p. 64-74.
61. E.Kline, J., et al., Cortical Spectral Activity and Connectivity during Activeand Viewed Armand Leg Movement. Front. Neurosci., 2016. 10: p. 10:91.
62. Kisho Fukuchi, R., et al., Evaluation of alternative technical markers for the pelvic coordinate system. J Biomech, 2010. 43(3): p. 592-4.
63. Zhang, S., K.G. Clowers, and D. Powell, Ground reaction force and 3D biomechanical characteristics of walking in short-leg walkers. Gait Posture, 2006. 24(4): p. 487-92.