Posts Tagged Head-Mounted

[ARTICLE] Walking with head-mounted virtual and augmented reality devices: Effects on position control and gait biomechanics – Full Text

Abstract

What was once a science fiction fantasy, virtual reality (VR) technology has evolved and come a long way. Together with augmented reality (AR) technology, these simulations of an alternative environment have been incorporated into rehabilitation treatments. The introduction of head-mounted displays has made VR/AR devices more intuitive and compact, and no longer limited to upper-limb rehabilitation. However, there is still limited evidence supporting the use of VR and AR technology during locomotion, especially regarding the safety and efficacy relating to walking biomechanics. Therefore, the objective of this study is to explore the limitations of such technology through gait analysis. In this study, thirteen participants walked on a treadmill in normal, virtual and augmented versions of the laboratory environment. A series of spatiotemporal parameters and lower-limb joint angles were compared between conditions. The center of pressure (CoP) ellipse area (95% confidence ellipse) was significantly different between conditions (p = 0.002). Pairwise comparisons indicated a significantly greater CoP ellipse area for both the AR (p = 0.002) and VR (p = 0.005) conditions when compared to the normal laboratory condition. Furthermore, there was a significant difference in stride length (p<0.001) and cadence (p<0.001) between conditions. No statistically significant difference was found in the hip, knee and ankle joint kinematics between the three conditions (p>0.082), except for maximum ankle plantarflexion (p = 0.001). These differences in CoP ellipse area indicate that users of head-mounted VR/AR devices had difficulty maintaining a stable position on the treadmill. Also, differences in the gait parameters suggest that users walked with an unusual gait pattern which could potentially affect the effectiveness of gait rehabilitation treatments. Based on these results, position guidance in the form of feedback and the use of specialized treadmills should be considered when using head-mounted VR/AR devices.

Introduction

Over the past two decades, the application of virtual reality (VR) technology in a healthcare setting has become increasingly popular. It has been incorporated into clinical practices such as in the rehabilitation of stroke survivors, as well as patients with cerebral palsy and multiple sclerosis [13]. There is ample evidence suggesting that VR-based rehabilitation facilitates upper limb motion [4] and dynamic balance [5] among stroke survivors. More recently, research groups have also investigated the use of VR in dynamic situations (i.e. treadmill walking), aiming to improve balance and facilitate gait recovery [69].

In current clinical practice, gait retraining often includes treadmill training under the supervision of practitioners or through provision of real-time biofeedback. It is a widely adopted technique that aims to permanently correct faulty gait patterns and has been found to be effective in both walking and running gait modifications [1012]. For example, a recently published randomized controlled trial showed that gait retraining was an effective intervention for reduction of knee loading and also improved symptoms among patients with early knee osteoarthritis [10]. Incorporation of VR technology into conventional gait retraining has the potential to further enhance training outcomes. VR allows users to actively interact with a simulated environment in real-time and offers the opportunity to practice skills acquired in the virtual environments to everyday life [13]. VR-based gait retraining has the potential to facilitate implicit learning, enhance variety, and actively engage the patient during training. These attributes are crucial in the optimization of motor learning and could maximize the training effect [14].

Walking is normally an automatic process. It has been suggested that conscious modification to walking patterns could affect gait retraining adaptations [15]. A previous study found that subjects who trained with distraction were able to retain the training effect longer than the group who focused on correction [15]. VR-based retraining could include different tasks and games while the patients modify their gait pattern as it could help patients to maintain focus and promote implicit motor learning. Moreover, the training environment, feedback type and level of difficulty of tasks can be manipulated within the VR environment relatively effortlessly for the clinician, as compared to conventional gait retraining. Variation in training has been shown to promote a more robust motor pattern and favor adaptation [16,17]. Moreover, motivation and adherence among patients can also be improved with more variation and an adjustable level of difficulty provided in the VR-based training [18]. Stroke survivors were previously found to be more actively engaged in a VR-based training than a conventional task-oriented intervention to improve motor function [19]. The training environment can be designed to simulate real-life activities and include task-specific training and a natural experience can be achieved through immersive VR devices, such as using a head-mounted display (HMD) [20]. Studies have supported task-specific motor skill training with VR in helping to drive neuroplasticity in individuals with progressive neurodegenerative disorder [4,21].

Although multiple studies have reported positive results of gait retraining using VR among various patient groups within the lab [1,5,22,23], there is still little understanding of the limitations and challenges for using VR technology clinically. One overriding concern for using VR technology in clinical applications, especially an HMD, is safety. The user may not be able to recognize his/her own body position when using an immersive VR device, which could result in physical injuries, particularly if the user fails to stay within the boundaries of the treadmill. Suspension devices (i.e. an over-head harness) have been used for protection during VR-based gait rehabilitation [8], and a recent study showed that both young and older adults were able to use HMD during walking without adverse effects [21]. However, the limit of VR technology on safety was not quantified or discussed. Recent technological advances in both the hardware and software of HMD might allow for safer use. However, there is still a need for evidence-based support and quantifiable data, which could help with practical considerations among VR applications in a clinical setting.

Another concern for gait rehabilitation would be the regularity and quality of gait. Through studying spatiotemporal gait parameters, some studies have reported that walking in a projected VR environment can induce gait instability even in healthy participants [24,25]. Nowadays, VR-based gait retraining using HMD focuses primarily on gait restoration after stroke [8]; the changes in natural gait due to the use of HMD may not be clinically significant. However, it is crucial for particular patient groups undergoing gait modification to maintain a certain level of regularity in their gait pattern. For instance, knee loading can be affected by spatiotemporal parameters such as cadence and step length [26] and VR was previously found to alter such parameters in an over-ground setting [24]. The treatment effect of gait retraining in reducing knee loading would likely be affected if the patient’s baseline walking gait was already altered by the use of HMD or other VR devices. The aforementioned studies did not quantify the changes in walking biomechanics when using a HMD, therefore, this study aimed to identify gait parameters that were affected by the use of HMD.

An alternative to VR is Augmented Reality (AR), which does not fully immerse the user in a simulated environment but includes virtual elements that are superimposed on a real-world view [27]. For example, external cues on foot placement could be overlaid on to the walking surface in order to facilitate gait adjustments [28,29]. The addition of feedback in AR-based gait retraining allows for variations in training and could enhance the gait retraining effect. Yet, there is also a lack of understanding of the limitation of using AR devices. Therefore, this study also aimed to examine the biomechanical changes induced by the HMD within an AR setting.

This study was designed to assess whether the use of commercially available HMD in VR and AR settings were suitable for clinical gait retraining. Specifically, the aim was to quantify the limitations of current VR and AR technology based on two practical concerns for clinical applications: 1) safety: the ability of the user to maintain a relatively stable position within the treadmill and 2) natural gait patterns: deviation of walking biomechanics from that of normal-treadmill walking. We hypothesized that there would be variations in the control of body position relative to the treadmill between both VR and AR conditions when compared with normal-treadmill walking. Also, based on altered gait biomechanics reported with the use of HMD in an over-ground setting [24], we hypothesized there would be variation in the spatiotemporal and joint kinematic measures while walking in VR and AR conditions, when compared with normal-treadmill walking.

Materials and methods

Participants

A total of 13 participants (7 females, 6 males; age = 24.6 ± 4.5 years; weight = 63.1 ± 14.5 kg; height = 1.68 ± 0.11 m) were recruited for this study through convenient sampling, which is a comparable sample size to previous studies [3032]. Participants were free of any musculoskeletal, neurological, neuromuscular or cardiovascular pathology that might hinder walking. The experimental procedures were reviewed and approved by the Departmental Research Committee of the department of Rehabilitation Sciences, The Hong Kong Polytechnic University (Ref.: HSEARS20161018001) and written informed consent was obtained from all participants prior to the experiment.

Experimental procedures

Participants were asked to walk at a self-selected pace for four minutes to allow for treadmill adaptation prior to data collection [33]. Anthropometric data, including leg length, knee width and ankle width [3436], were recorded and 39 reflective markers were affixed to specific bony landmarks based on the Vicon Plug-in-Gait® full body model [34]. The marker model was previously established for the measurement of lower-limb kinematics [35]. This study was designed to assess HMD in VR and AR settings using a commercially available model within a typical clinical setting. Thus, the conditions were designed to be simple and without the use of additional lab equipment. All walking trials were conducted on a dual-belt instrumented treadmill (Force-sensing tandem treadmill, AMTI, Watertown, MA, USA; length x width = 1.2 x 0.6 m). Participants wore their own usual shoes and walked under different conditions at 3.0 km/h (0.83 m/s) for three minutes each. The three conditions were Control, VR and AR, details were as follows:

Control: Treadmill walking without the HMD;

Virtual reality (VR): Immersive 360° panoramic image of the laboratory captured by the Samsung Gear 360 Cam (Samsung, Seoul, South Korea), set up instructions and image file used are provided in the supporting information (S1 File and S1 Fig).

Augmented reality (AR): Real-time display through the rear camera of the HMD, set up instructions are provided in the supporting information (S2 File).

For the AR and VR conditions, participants wore a head-mounted VR device (Samsung Gear VR SM-R322 and Samsung Galaxy S7, Samsung, Seoul, South Korea; width x height x depth: 201.93 x 92.71 x 116.33 mm). The immersive VR/AR environment within this study refers to the panoramic display in a first-person perspective with complete visual obstruction to the real-world environment. The HMD used in this study weighs a total of 470 g, which is comparable to typical commercial HMD models (HTC VIVE Pro: 555 g [37] and Oculus Rift DK2: 440 g [38]). Adjustments to the device were made for fit, focus, and orientation for each participant. Participant’s comfort was confirmed through subjective reporting before the beginning of each walking trial.

The test sequence was randomized using a web-based software (www.randomizer.org). To ensure safety, participants were supported by an overhead safety harness providing 0% bodyweight support. The experimental setup is indicated in Fig 1. The individual in Fig 1 of this manuscript has given written informed consent (as outlined in PLOS consent form) to publish the photograph.

thumbnail

Ground reaction force and coordinates of the center of pressure (CoP) were sampled through the instrumented treadmill at 1,000 Hz. Marker trajectories were sampled at 200 Hz using an 8-camera motion capture system (Vicon, Oxford Metrics Group, UK). The instrumented treadmill and motion capture system were synchronized and were set for data collection for three minutes after the treadmill reached the testing speed.[…]

 

Continue —->  Walking with head-mounted virtual and augmented reality devices: Effects on position control and gait biomechanics

, , , , ,

Leave a comment

[Abstract + References] Multimodal Head-Mounted Virtual-Reality Brain-Computer Interface for Stroke Rehabilitation – Conference paper

Abstract

Rehabilitation after stroke requires the exploitation of active movement by the patient in order to efficiently re-train the affected side. Individuals with severe stroke cannot benefit from many training solutions since they have paresis and/or spasticity, limiting volitional movement. Nonetheless, research has shown that individuals with severe stroke may have modest benefits from action observation, virtual reality, and neurofeedback from brain-computer interfaces (BCIs). In this study, we combined the principles of action observation in VR together with BCI neurofeedback for stroke rehabilitation to try to elicit optimal rehabilitation gains. Here, we illustrate the development of the REINVENT platform, which takes post-stroke brain signals indicating an attempt to move and drives a virtual avatar arm, providing patient-driven action observation in head-mounted VR. We also present a longitudinal case study with a single individual to demonstrate the feasibility and potentially efficacy of the REINVENT system.

References

  1. 1.
    Mozaffarian, D., et al.: American heart association statistics committee and stroke statistics subcommittee: heart disease and stroke statistics–2015 update: a report from the American heart association. Circulation 131, e29–e322 (2015)Google Scholar
  2. 2.
    Miller, E.L., et al.: American heart association council on cardiovascular nursing and the stroke council: comprehensive overview of nursing and interdisciplinary rehabilitation care of the stroke patient: a scientific statement from the American heart association. Stroke 41, 2402–2448 (2010)CrossRefGoogle Scholar
  3. 3.
    Celnik, P., Webster, B., Glasser, D., Cohen, L.: Effects of action observation on physical training after stroke. Stroke J. Cereb. Circ. 39, 1814–1820 (2008)CrossRefGoogle Scholar
  4. 4.
    Ertelt, D., et al.: Action observation has a positive impact on rehabilitation of motor deficits after stroke. NeuroImage 36(Suppl 2), T164–T173 (2007)CrossRefGoogle Scholar
  5. 5.
    Garrison, K.A., Aziz-Zadeh, L., Wong, S.W., Liew, S.-L., Winstein, C.J.: Modulating the motor system by action observation after stroke. Stroke 44, 2247–2253 (2013)CrossRefGoogle Scholar
  6. 6.
    Ballester, B.R., et al.: The visual amplification of goal-oriented movements counteracts acquired non-use in hemiparetic stroke patients. J. Neuroeng. Rehabil. 12, 50 (2015)CrossRefGoogle Scholar
  7. 7.
    Vourvopoulos, A., Bermúdez i Badia, S.: Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis. J. Neuroeng. Rehabil. 13, 69 (2016)CrossRefGoogle Scholar
  8. 8.
    Maclean, N., Pound, P., Wolfe, C., Rudd, A.: Qualitative analysis of stroke patients’ motivation for rehabilitation. BMJ 321, 1051–1054 (2000)CrossRefGoogle Scholar
  9. 9.
    Paraskevopoulos, I., Tsekleves, E., Warland, A., Kilbride, C.: Virtual reality-based holistic framework: a tool for participatory development of customised playful therapy sessions for motor rehabilitation. In: 2016 8th International Conference on Games and Virtual Worlds for Serious Applications (VS-Games), September (2016)Google Scholar
  10. 10.
    Wolpaw, J.R.: Brain-Computer Interfaces: Principles and Practice. Oxford University Press, Oxford (2012)CrossRefGoogle Scholar
  11. 11.
    Vourvopoulos, A., Bermudez i Badia, S.: Usability and cost-effectiveness in brain-computer interaction: is it user throughput or technology related? In: Proceedings of the 7th Augmented Human International Conference. ACM, Geneva, Switzerland (2016)Google Scholar
  12. 12.
    Schomer, D.L., Lopes da Silva, F.H.: Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins, Philadelphia (2011)Google Scholar
  13. 13.
    Kropotov, J.D.: Chapter 2.2 – Alpha rhythms. In: Kropotov, J.D. (ed.) Functional Neuromarkers for Psychiatry, pp. 89–105. Academic Press, San Diego (2016)CrossRefGoogle Scholar
  14. 14.
    Pfurtscheller, G., Lopes da Silva, F.H.: Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol. 110, 1842–1857 (1999)CrossRefGoogle Scholar
  15. 15.
    Wu, J., et al.: Connectivity measures are robust biomarkers of cortical function and plasticity after stroke. Brain 138, 2359–2369 (2015)CrossRefGoogle Scholar
  16. 16.
    Zhou, R.J., et al.: Predicting gains with visuospatial training after stroke using an EEG measure of frontoparietal circuit function. Front. Neurol. 9, 597 (2018)CrossRefGoogle Scholar
  17. 17.
    Soekadar, S.R., Birbaumer, N., Slutzky, M.W., Cohen, L.G.: Brain–machine interfaces in neurorehabilitation of stroke. Neurobiol. Dis. 83, 172–179 (2015)CrossRefGoogle Scholar
  18. 18.
    Friedman, D.: Brain-computer interfacing and virtual reality. In: Nakatsu, R., Rauterberg, M., Ciancarini, P. (eds.) Handbook of Digital Games and Entertainment Technologies, pp. 151–171. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-4560-50-4_2CrossRefGoogle Scholar
  19. 19.
    Vourvopoulos, A., Ferreira, A., Bermúdez i Badia, S.: NeuRow: an immersive VR environment for motor-imagery training with the use of brain-computer interfaces and vibrotactile feedback. In: 3rd International Conference on Physiological Computing Systems, Lisbon (2016)Google Scholar
  20. 20.
    Slater, M.: Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments. Philos. Trans. R. Soc. B Biol. Sci. 364, 3549–3557 (2009)CrossRefGoogle Scholar
  21. 21.
    Spicer, R., Anglin, J., Krum, D.M., Liew, S.L.: REINVENT: a low-cost, virtual reality brain-computer interface for severe stroke upper limb motor recovery. In: 2017 IEEE Virtual Reality (VR), pp. 385–386 (2017)Google Scholar
  22. 22.
    Klem, G.H., Luders, H.O., Jasper, H.H., Elger, C.: The ten-twenty electrode system of the international federation. Electroencephalogr. Clin. Neurophysiol. 52, 3–6 (1999). The International Federation of Clinical NeurophysiologyGoogle Scholar
  23. 23.
    Kothe, C.: Lab streaming layer (LSL). https://github.com/sccn/labstreaminglayer. Accessed 26 Oct 2015 (2014)
  24. 24.
    Fugl-Meyer, A.R., Jääskö, L., Leyman, I., Olsson, S., Steglind, S.: The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scand. J. Rehabil. Med. 7, 13–31 (1975)Google Scholar
  25. 25.
    Duncan, P.W., Wallace, D., Lai, S.M., Johnson, D., Embretson, S., Laster, L.J.: The stroke impact scale version 2.0: evaluation of reliability, validity, and sensitivity to change. Stroke 30, 2131–2140 (1999)CrossRefGoogle Scholar
  26. 26.
    Kennedy, R.S., Lane, N.E., Berbaum, K.S., Lilienthal, M.G.: Simulator sickness questionnaire: an enhanced method for quantifying simulator sickness. Int. J. Aviat. Psychol. 3, 203–220 (1993)CrossRefGoogle Scholar
  27. 27.
    Bailey, J.O., Bailenson, J.N., Casasanto, D.: When does virtual embodiment change our minds? Presence Teleoperators Virtual Environ. 25, 222–233 (2016)CrossRefGoogle Scholar
  28. 28.
    Witmer, B.G., Singer, M.J.: Measuring presence in virtual environments: a presence questionnaire. Presence Teleoperator Virtual Environ. 7, 225–240 (1998)CrossRefGoogle Scholar
  29. 29.
    Bouchard, S., Robillard, G., Renaud, P., Bernier, F.: Exploring new dimensions in the assessment of virtual reality induced side effects. J. Comput. Inf. Technol. 1, 20–32 (2011)Google Scholar
  30. 30.
    Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004)CrossRefGoogle Scholar
  31. 31.
    Makeig, S.: Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalogr. Clin. Neurophysiol. 86, 283–293 (1993)CrossRefGoogle Scholar
  32. 32.
    Neuper, C., Wörtz, M., Pfurtscheller, G.: ERD/ERS patterns reflecting sensorimotor activation and deactivation. Prog. Brain Res. 159, 211–222 (2006)CrossRefGoogle Scholar
  33. 33.
    Pfurtscheller, G., Aranibar, A.: Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. Electroencephalogr. Clin. Neurophysiol. 46, 138–146 (1979)CrossRefGoogle Scholar
  34. 34.
    Pfurtscheller, G., Brunner, C., Schlögl, A., Lopes da Silva, F.H.: Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31, 153–159 (2006)CrossRefGoogle Scholar
  35. 35.
    Liew, S.-L., et al.: Laterality of poststroke cortical motor activity during action observation is related to hemispheric dominance. Neural Plast. 2018, 14 (2018)CrossRefGoogle Scholar
  36. 36.
    Ritter, P., Moosmann, M., Villringer, A.: Rolandic alpha and beta EEG rhythms’ strengths are inversely related to fMRI-BOLD signal in primary somatosensory and motor cortex. Hum. Brain Mapp. 30, 1168–1187 (2009)CrossRefGoogle Scholar
  37. 37.
    Westlake, K.P., et al.: Resting state alpha-band functional connectivity and recovery after stroke. Exp. Neurol. 237, 160–169 (2012)CrossRefGoogle Scholar
  38. 38.
    Dubovik, S., et al.: EEG alpha band synchrony predicts cognitive and motor performance in patients with ischemic stroke. https://www.hindawi.com/journals/bn/2013/109764/abs/

via Multimodal Head-Mounted Virtual-Reality Brain-Computer Interface for Stroke Rehabilitation | SpringerLink

, , , , , , , , , , , , ,

Leave a comment

%d bloggers like this: