隨者科技的進步，擴增實境的技術日益成熟，逐漸變成復健練和認知科學研究的輔助設備。步行障礙為帕金森氏症（PD）患者常見的一個症狀。以往的研究發現，透過外部線索如視覺引導可以改善帕金森病患者在復健行走之能力。雖然目前已有使用虛擬引導來幫助帕金森氏症患者行走之輔具，但現有系統專注於直線行走之研究。非常少研究提供必要的生物回饋信息來幫助轉向運動。藉由可穿戴電腦結合收發器和頭戴顯示已整合為一台便攜式擴增現實（AR）的系統評估步態表現，以及產生虛擬引導信號，以幫助步帕金森氏症患者態起始，直線，與轉彎之步態。
可攜式擴增實境(AR)系統是由一個頭戴式顯示器（HMD）和重量小於500克的可穿戴電腦，以及動作感測器即時測量人體動作。動作資訊是根據3軸陀螺儀和3軸加速度計之慣性測量裝置組成，測量其關節角度，角速度，步行速度。在MTX（Xsens公司生產）和慣性傳感器模組放置在外踝，大腿和骶骨通過無線數據採集與測量動作數據。時間參數包括步行速度和步數，可由感測器測量腳跟接觸地面。空間參數包含步伐的長度，為透過定義出的步態位置進行二次積分。並每步重置以避免加速度的誤差積累。生物回饋透過著地和擺蕩期作為帕金森氏症患者之步態訓練之依據。在步態訓練時這些即時回饋資料，是由Virtools軟體將互動訊號與3D場景整合產生視覺光流(optic flow)，其包含垂直黑白相間的輔助影像來幫助患者行走，將虛擬的輔助影像投射在頭帶顯示器來幫助患者步態的改善。
直線行走10 米時使用AR 系統後患者的步數從18±4.3 減少為14±1.0。在轉彎行走時患者第二步完成的度數為16.8±5.9，使用AR 系統後增加為33.4±1.8。本實驗之擴增實境系統可以定義出行走之步態週期，並作為帕金森氏症患者在起始步態、直線行走及轉彎臨床訓練，具體來說，本實驗之擴增實境系統顯示一個虛擬的輔助資訊和病人的足跡作為視覺提示，幫助改善PD 患者之步態。透過臨床驗證評估可攜式擴增實境系統，對於帕金森氏症患者步態之改善。結果顯示在穿戴輔助系統後，帕金森氏症患者對於直線行走帶來幫助，更可以讓患者產生一個較大角度以及順利的轉向，幫助患者完成轉彎。

With the advance in information technology, augmented reality (AR) technology has been matured to become an assistive device for rehabilitation training and cognitive neuroscience studies. Gait disturbance is one of the main symptoms in advanced Parkinson’s disease (PD) patients. For rehabilitation aspect, previous studies have found that external cues such as visual cue can improve the walking abilities in PD patients to a considerable extent. Although there is a multiplicity of visual cue generation schemes for PD gait training, current systems focus on straight line walking. Few studies provide essential feedback information for turning movement. A wearable computer with motion transceiver and head-mount display has been integrated as a portable AR system for assessing the gait performance as well as generating virtual cues in response to the kinematic signal to assist gait initiation and turning impairment associated with PD.
The portable AR system is composed of a head-mounted display (HMD), a wearable computer weighted less than 500 g, and motion sensors for real-time detection of the human locomotion. The kinematical information of joint angle, angular velocity, walking speed were obtained from the inertial measurement unit (IMU) consisting of 3 gyroscopes and 3 accelerometers. The inertial and gyro sensor modules were placed on lateral malleolus, thigh and sacrum via a wireless data acquisition module for measuring kinematic data. The temporal gait parameters including walking speed and cadence can be derived from the inertial data of heel contact. The spatial parameters in step/stride length can be double integration of acceleration data to obtain the trajectory within one stride. It is noted that the step length should be reset to avoid the accumulation error of accelerometer. As the biofeedback for PD gait training, the partitioning of gait cycle into stance/swing phases is a crucial step. During the gait training, the virtual cues were created from Virtools software for interactive rendering ability of the 3D scenes which can produce visual stimulation patterns and optical flow field for training purposes. The optical flow consisted of a square field with vertically alternating black/white rectangles as well as the footprint of trained subject. The virtual cues were projected onto the HMD screen under the interaction with the gait pattern of patients.
In our pilot studies, the cadence was 18±4.3 (steps/minute), when PD patients walking without AR system and reduced to 14±1.0 (steps/minute), while using AR system. In the two-step of turning, the turning degree of the first step was 16.8±5.9 degrees in average for PD patients without AR system which is smaller than that with AR feedback (33.4±1.8 degrees). Our portable AR system has demonstrated the feasibility to detect main gait events which has been tested in clinical trials for improving gait initiation, straight walking and more importantly the turning around in clinical trials of PD subject. Specifically, our AR device displays a virtual tiled floor and patient’s footprint as visual cue for PD subject that helps regulating the gait patterns of PD subjects. Our results have demonstrated that the PD patient can make a bigger turn and finish the turn smoothly with the help of portable AR system. The improvement in gait performance in PD subject can be also assessed by the same portable AR system simultaneously which makes the gait training more plausible in a clinical setup environment.

[1] J. P. Martin, “Locomotion and the basal ganglia,” In J. P. Martin (Ed.), The basal ganglia and posture, London: Pitman medical, 20-35, 1967.
[2] M. E. Morris, F. Huxham, J. McGinley, K. Dodd, R. Jansek, “The biomechanics and motor control of gait in Parkinson disease,” Clinical Biomechanics, 16: 459-470, 2001.
[3] C. G. Canning, L. Ada, J. J. Johnson, S. McWhirter, “Walking capacity in mild to moderate Parkinson’s disease,” Archives of Physical Medicine and Rehabilitation, 87: 371-375, 2006.
[4] J. Messier, S. Adamovich, D. Jack, W. Hening, J. Sage, H. Poizner, “Visuomotor learning in immersive 3D virtual reality in Parkinson’s disease and in aging,” Experimental Brain Research, 179: 457-474, 2006.
[5] R. Azuma, Y. Baillot, R. Behringer, S. Feiner, S. Julier, B. MacIntyre, “Recent advances in augmented reality,” Computers and Graphics, 21: 34-47, 2001.
[6] S. Weghorst, J. Prothero, T. A. Furness, D. Anson, T. Riess, “Virtual Images in the treatment of Parkinson’s disease akinesia,” In Proceedings of Medicine Meets Virtual Reality II, 242-243, 1994.
[7] T. J. Riess, “Gait and Parkinson’s disease: a conceptual model for an augmented-reality based therapeutic device,” Studies in health technology and informatics, 58: 200-208, 1998.
[8] Y. Baram, J. Aharon-peretz, Y. Simionovici, L. Ron, “Walking on virtual tiles,” Neural Processing Letters, 16: 227-233, 2002.
[9] Y. Baram, A. Miller, “Virtual reality cues for improvement of gait in patients with multiple sclerosis,” Neurology, 66: 178-181, 2006.
[10] M. Ferrarin, M. Brambilla, L. Garavello, A. Di Candia, A. Pedotti, M. Rabuffetti, “Microprocessor-controlled optical stimulating device to improve the gait of patients with Parkinson’s disease,” Medical and Biological Engineering and Computing, 42: 328-332, 2004. 37
[11] J. H. McAuley, P. M. Daly, C. R. Curtis, “A preliminary investigation of a novel design of visual cue glasses that aid gait in Parkinson’s disease,” Clinical Rehabilitation, 23: 687-695, 2009.
[12] M. Ferrarin, M. Rabuffetti, M. Tettamanti, R. Pignatti, A. Mauro, and G. Albani, “Effect of optical flow versus attentional strategy on gait in Parkinson’s disease: a study with a portable optical stimulating device,” Journal of Neuro Engineering and Rehabilitation, 5: 1-9, 2008.
[13] Y. Chou, R. C. Wagenaar, E. Saltzman, J. E. Giphart, D. Young, R. Davidsdottir, A. Cronin-Golomb, “Effects of optic flow speed and lateral flow asymmetry on locomotion in younger and older adults: a virtual reality study,” Journal of Gerontology: Psychological Sciences, 64B: 222-231, 2009.
[14] A. Jean-Philippe, M. Serge, B. Olivier, “Influence of visual cues on gait in Parkinson’s disease: contribution to attention or sensory dependence?” Journal of the Neurological Sciences, 248: 192-195, 2006.
[15] T. A. Kaminsky, B. J. Dudgeon, F. F. Billingsley, P. H. Mitchell, S. J. Weghorst, “Virtual cues and functional mobility of people with Parkinson’s disease: a single-subject pilot study,” Journal of Rehabilitation Research & Development, 44: 437-448, 2007.
[16] Y. Jiang, K. E. Norman, “Effects of visual and auditory cues on gait initiation in people with Parkinson’s disease,” Clinical Rehabilitation, 36-45, 2006.
[17] J. H. Hollman, R. H. Brey, R. A. Robb, T. J. Bang, K. R. Kaufman, “Spatiotemporal gait deviations in a virtual reality environment,” Gait Posture, 23: 441-444, 2006.
[18] A. Salarian, H. Russmann, F. J. G. Vingerhoets, C. Dehollain, Y. Blanc, P. R. Burkhard, K. Aminian, “Gait assessment in Parkinson’s disease: toward an ambulatory system for long-term monitoring,” IEEE Transactions on Biomedical Engineering, 51: 1434-1443, 2004. 38
[19] D. S. Ward, K. R. Evenson, A. Vaughn, A. B. Rodgers, R. P. Troiano, “Accelerometer use in physical activity: best practices and research recommendations,” Medicine and Science in Sports and Exercise, 37: 582-588, 2005.
[20] T. Starner, S. Mann, B. Rhodes, J. Levine, J. Healey, D. Kirsh, R. Picard, A. Pentland, “Augmented reality through wearable computing,” Teleoperator and Virtual Environments, 6: 386-398, 1997.
[21] C. Baber, “Wearable computers: a human factors review,” International Journal of Human-Computer Interaction, 13: 123-145, 2001.
[22] S. T. Moore, H. G. MacDougall, J. M. Gracies, H. S. Cohen, W. G. Ondo, “Long-term monitoring of gait in Parkinson’s disease,” Gait Posture, 26: 200-207, 2007.
[23] H. Dejnabadi, B. M. Jolles, E Casanova, P. Fua, K. Aminian, “Estimation and visualization of sagittal kinematics of lower limbs orientation using body-fixed sensors,” IEEE Trans. Biomedical Engineering, 53: 1385-1395, 2006.
[24] Z. Ali, “Neuronal mechanisms of actively steering optic flow: global versus local motion,” Journal of Undergraduate Research, 3: 18-23, 2004.
[25] P. Crenna, I. Carpinella, M. Rabuffetti, E. Calabrese, P. Mazzoleni, R. Nemni, M. Ferrarin, “The association between impaired turning and normal straight walking in Parkinson’s disease,” Gait & Posture, 26: 172-178, 2007.
[26] E. L. Stack, A. M. Asburn, K. E. Jupp, “Strategies used by people with Parkinson’s disease who report difficulty turning,” Parkinsonism Related and Disord, 12: 87-92, 2006.
[27] M. K. Y. Mak, A. Patla, C. Hui-Chan, “Sudden turn during walking is impaired in people with Parkinson’s disease,” Experimental Brain Research, 190: 43-51, 2008.
[28] C. J. Hass, D. E. Waddell, R. P. Fleming, J. L. Juncos, R. J. Gregor, “Gait initiation and dynamic balance control in Parkinson’s disease,” Archives of 39 Physical Medicine and Rehabilitation, 86: 2172-2176, 2005.
[29] L. A. Talbot, J. M. Gaines, T. N. Huynh, E. J. Metter, “A home-based pedometer-driven walking program to increase physical activity in older adults with osteoarthritis of the knee: a preliminary study,” Journal of the American Geriatrics Society, 51: 387-392, 2003.
[30] M. E. Morris, “Locomotor training in people with Parkinson disease,” Physical Therapy, 86: 1426-1435, 2006.
[31] L. Rochester, A. Nieuwboer, K. Baker, V. Hetherington, A. M. Willems, F. Chavret, G. Kwakkel, E. Van Wegen, I. Lim, D. Jones, “The attentional cost of external rhythmical cues and their impact on gait in Parkinson’s disease: effect of cue modality and task complexity,” Journal of Neural Transmission, 114: 1243-1248, 2007.
[32] M. Martin, M. Shinberg, M. Kuchibhatla, L. Ray, J. J. Carollo, M. L. Schenkman, “Gait initiation in community-dwelling adults with Parkinson disease: comparison with older and younger adults without the disease,” Physical Therapy, 82: 566-577, 2002.
[33] R. L. Freedland, C. Festa, M. Sealy, A. McBean, P. Elghazaly, A. Capan, L. Brozycki, A. J. Nelson, J. Rothman, “The effects of pulsed auditory stimulation on various gait measurements in persons with Parkinson’s disease,” Neurorehabilitation, 17: 81-87, 2002.