Archive for category Video Games/Exergames

[Abstract] Virtual reality for stroke rehabilitation – Review

Abstract

BACKGROUND:
Virtual reality and interactive video gaming have emerged as recent treatment approaches in stroke rehabilitation with commercial gaming consoles in particular, being rapidly adopted in clinical settings. This is an update of a Cochrane Review published first in 2011 and then again in 2015.

OBJECTIVES:
Primary objective: to determine the efficacy of virtual reality compared with an alternative intervention or no intervention on upper limb function and activity.Secondary objectives: to determine the efficacy of virtual reality compared with an alternative intervention or no intervention on: gait and balance, global motor function, cognitive function, activity limitation, participation restriction, quality of life, and adverse events.

SEARCH METHODS:
We searched the Cochrane Stroke Group Trials Register (April 2017), CENTRAL, MEDLINE, Embase, and seven additional databases. We also searched trials registries and reference lists.

SELECTION CRITERIA:
Randomised and quasi-randomised trials of virtual reality (“an advanced form of human-computer interface that allows the user to ‘interact’ with and become ‘immersed’ in a computer-generated environment in a naturalistic fashion”) in adults after stroke. The primary outcome of interest was upper limb function and activity. Secondary outcomes included gait and balance and global motor function.

DATA COLLECTION AND ANALYSIS:
Two review authors independently selected trials based on pre-defined inclusion criteria, extracted data, and assessed risk of bias. A third review author moderated disagreements when required. The review authors contacted investigators to obtain missing information.

MAIN RESULTS:
We included 72 trials that involved 2470 participants. This review includes 35 new studies in addition to the studies included in the previous version of this review. Study sample sizes were generally small and interventions varied in terms of both the goals of treatment and the virtual reality devices used. The risk of bias present in many studies was unclear due to poor reporting. Thus, while there are a large number of randomised controlled trials, the evidence remains mostly low quality when rated using the GRADE system. Control groups usually received no intervention or therapy based on a standard-care approach.

PRIMARY OUTCOME:
results were not statistically significant for upper limb function (standardised mean difference (SMD) 0.07, 95% confidence intervals (CI) -0.05 to 0.20, 22 studies, 1038 participants, low-quality evidence) when comparing virtual reality to conventional therapy. However, when virtual reality was used in addition to usual care (providing a higher dose of therapy for those in the intervention group) there was a statistically significant difference between groups (SMD 0.49, 0.21 to 0.77, 10 studies, 210 participants, low-quality evidence).

SECONDARY OUTCOMES:
when compared to conventional therapy approaches there were no statistically significant effects for gait speed or balance. Results were statistically significant for the activities of daily living (ADL) outcome (SMD 0.25, 95% CI 0.06 to 0.43, 10 studies, 466 participants, moderate-quality evidence); however, we were unable to pool results for cognitive function, participation restriction, or quality of life. Twenty-three studies reported that they monitored for adverse events; across these studies there were few adverse events and those reported were relatively mild.

AUTHORS’ CONCLUSIONS:
We found evidence that the use of virtual reality and interactive video gaming was not more beneficial than conventional therapy approaches in improving upper limb function. Virtual reality may be beneficial in improving upper limb function and activities of daily living function when used as an adjunct to usual care (to increase overall therapy time). There was insufficient evidence to reach conclusions about the effect of virtual reality and interactive video gaming on gait speed, balance, participation, or quality of life. This review found that time since onset of stroke, severity of impairment, and the type of device (commercial or customised) were not strong influencers of outcome. There was a trend suggesting that higher dose (more than 15 hours of total intervention) was preferable as were customised virtual reality programs; however, these findings were not statistically significant.

Update of
Virtual reality for stroke rehabilitation. [Cochrane Database Syst Rev. 2015]

via Virtual reality for stroke rehabilitation. – PubMed – NCBI

Advertisements

, , , , , ,

Leave a comment

[Abstract] Active exergames to improve cognitive functioning in neurological disabilities: a systematic review and meta-analysis.

Exergames represent a way to perform physical activity through active video games, serving as potentially useful tool in the field of neurorehabilitation. However, little is known regarding the possible role of exergames in improving cognitive functions in persons suffering from neurological disabilities.A search for relevant articles was carried out on PubMed/Medline, Scopus, PEDro, and Google Scholar. Only randomized controlled studies and non-randomized but controlled studies were retained. The following additional inclusion criteria were applied: studies focused on physical activity interventions carried out by means of exergames; populations targeted were affected by neurological disabilities; and reported results were related to cognitive outcomes. We calculated standardized mean differences (SMD) and pooled results using a random effects meta-analysis.Of 520 abstracts screened, thirteen studies met the criteria to be included yielding a total of 465 participants, 233 randomized to exergames, and 232 allocated to the alternative or no intervention. The included studies varied in terms of studied populations (e.g., multiple sclerosis, post-stroke hemiparesis, Parkinson’s disease, dementia, dyslexia, Down syndrome), type and duration of interventions, and cognitive outcome measures. Exergames significantly improved executive functions (SMD=0.53, p=0.005; 8 studies, n=380) and visuo-spatial perception (SMD=0.65, p<0.0001; 5 studies, n=209) when compared to the alternative or no intervention. There were no significant differences for attention (SMD=0.57, p=0.07; 7 studies, n=250) and global cognition (SMD=0.05, p=0.80; 6 studies, n=161).Exergames are a highly-flexible tool for rehabilitation of both cognitive and motor functions in adult populations suffering from various neurological disabilities and developmental neurological disorders. Additional high-quality clinical trials with larger samples and more specific cognitive outcomes are needed to corroborate these preliminary findings.Exergames could be considered either as a supplemental treatment to conventional rehabilitation, or as strategy to extend benefits of conventional programs at home.

via Active exergames to improve cognitive functioning in neurological disabilities: a systematic… – Abstract – Europe PMC

, ,

Leave a comment

Movement Therapy of the Upper Extremities with a Robotic Ball in Stroke Patients: Results of a Randomized Controlled Crossover Study – Full Text

Abstract

Background Stroke is associated with motor impairments of the upper extremities. The defining goal of rehabilitation is independent execution of activities of daily living. New therapy procedures use different hardware components to implement digital therapy contents. These can be useful complements to established therapy protocols.

Objectives The aim of this study was to examine the effect of movement therapy with a robotic ball on motor function parameters in stroke patients.

Materials and Methods 25 patients (60.0±10.0 years, 172.5±13.8 cm, 79.5±13.8 kg, 89.8±72.6 months post-stroke) took part in this crossover study. The intervention and control periods comprised 12 weeks each. Training with the robotic ball was done in addition to standard therapy two times a week for 45 min each. Different game activities were carried out with the help of a tablet and a smartphone.

Results Isometric grip strength improved by 4.5±3.6 kg (p=0.000), and unilateral dexterity increased by 7.5±6.3 successful tries (p=0.000) in the round block test. The self-reported disabilities of the arm, shoulder and hand were assessed using the QuickDASH questionnaire and showed improvements by 12.4±13.0 points (p=0.001).

Conclusions Additional therapy using the robotic ball improved upper extremity motor function and self-perceived health status in chronic stroke patients. However, performance stagnated when standard therapy was implemented alone. Moderately affected patients seem to benefit the most. The presence of very severe motor or cognitive symptoms led, in part, to some dropouts. The results need to be verified using larger patient populations.

Introduction

In Germany, up to 75% of the approx. 196,000 initial and 66,000 repeated strokes are survived [1] [2] [3] [4]. For the affected patients, it is often the trigger for persistent physical limitations, which in 85% of the cases are manifested by the cardinal symptom of spastic or flaccid hemiparesis of the upper extremities. Restriction or even loss of function of the hand and arm drastically impacts the daily life of the affected individual [1] [5] [6] [7]. Demographic change has increased the incidence of stroke. Improved acute care has enabled more people to survive the event, resulting in a greater number of patients and a growing demand for therapy [1] [2] [6]. Reduced range of motion, pain, sensory disturbances and increased muscle tone are characteristic patient symptoms [8] [9]. Loss of arm function is the consequence in about half of stroke cases [8], unlike rehabilitation of independent mobility, which can be achieved in up to 85% of patients [10]. Consequently, relatively less time is devoted to recovery of hand and arm function [11]. In addition to effects on motor function there are often psychological and social consequences [12]. A variety of physical activity measures should contribute to the compensation and restoration of skills and abilities [13] [14]. Since it is still possible to make progress even weeks after a stroke, it is imperative to develop more effective therapeutic methods, especially in the case of sustained loss of upper extremity function [8]. Thus the severity and location of the cerebral insult as well as comorbidities are decisive for the further rehabilitation process [15] [16]. The success of Constraint-induced Movement Therapy (CIMT) [7] [17] [18] [19] [20] [21] [22] [23] [24] demonstrates the necessity and possible benefit of using the upper extremities during training and everyday life.

In this context as well as due to innovative developments in technology-supported concepts and components [25] [26] [27] [28] [29] [30] in the field of stroke rehabilitation such as exergaming, [31] [32] [33] [34] the “Sphero 2.0” robotic ball was reviewed in combination with game-playing applications as a supplemental therapeutic activity [35]. The potential benefit for stroke patients regarding improving motor parameters has been documented in review articles on technology-supported therapeutic measures [36] [37]. Hardware and software components from the entertainment industry or telecommunications are used to develop new therapy activities. There are examples of the Microsoft Kinect webcam used in stroke rehabilitation [30] [38] [39] [40] [41] [42] [43] [44] as well as for game consoles such as the Nintendo Wii [45] [46] [47], Sony Playstation [48] or Microsoft XBox [44]. In addition, smartphones [34], tablets [49] [50] [51] [52] or virtual reality goggles [53] are being used to employ generally commercially available games with potential therapeutic benefits, or to use their sensor systems for movement detection and control. Therapeutic content can be found in commercial video games such as Wii Sports [54] or Kinect Sports [44] as well as games programmed specifically for therapeutic applications [42] [55]. Their common element is the required use of the affected body half to achieve the respective game objective. The activity can reflect daily activities such as grasping and moving a glass [56], cooking [38], or striking selected piano keys [38] [49]. […]

Continue —> Thieme E-Journals – Neurology International Open / Full Text

Fig. 2 The “Sphero 2.0” robotic ball by Orbotix (Boulder, CO, USA).

, , , , , , ,

Leave a comment

[Abstract] Suitability of Kinect for measuring whole body movement patterns during exergaming.

Abstract

Exergames provide a challenging opportunity for home-based training and evaluation of postural control in the elderly population, but affordable sensor technology and algorithms for assessment of whole body movement patterns in the home environment are yet to be developed.

The aim of the present study was to evaluate the use of Kinect, a commonly available video game sensor, for capturing and analyzing whole body movement patterns.

Healthy adults (n=20) played a weight shifting exergame under five different conditions with varying amplitudes and speed of sway movement, while 3D positions of ten body segments were recorded in the frontal plane using Kinect and a Vicon 3D camera system. Principal Component Analysis (PCA) was used to extract and compare movement patterns and the variance in individual body segment positions explained by these patterns. Using the identified patterns, balance outcome measures based on spatiotemporal sway characteristics were computed.

The results showed that both Vicon and Kinect capture >90% variance of all body segment movements within three PCs. Kinect-derived movement patterns were found to explain variance in trunk movements accurately, yet explained variance in hand and foot segments was underestimated and overestimated respectively by as much as 30%. Differences between both systems with respect to balance outcome measures range 0.3–64.3%.

The results imply that Kinect provides the unique possibility of quantifying balance ability while performing complex tasks in an exergame environment.

via Suitability of Kinect for measuring whole body movement patterns during exergaming – ScienceDirect

, , , , ,

Leave a comment

[Abstract] MaLT – Combined Motor and Language Therapy Tool for Brain Injury Patients Using Kinect.

Abstract

BACKGROUND:

The functional connectivity and structural proximity of elements of the language and motor systems result in frequent co-morbidity post brain injury. Although rehabilitation services are becoming increasingly multidisciplinary and “integrated”, treatment for language and motor functions often occurs in isolation. Thus, behavioural therapies which promote neural reorganisation do not reflect the high intersystem connectivity of the neurologically intact brain. As such, there is a pressing need for rehabilitation tools which better reflect and target the impaired cognitive networks.

OBJECTIVES:

The objective of this research is to develop a combined high dosage therapy tool for language and motor rehabilitation. The rehabilitation therapy tool developed, MaLT (Motor and Language Therapy), comprises a suite of computer games targeting both language and motor therapy that use the Kinect sensor as an interaction device. The games developed are intended for use in the home environment over prolonged periods of time. In order to track patients’ engagement with the games and their rehabilitation progress, the game records patient performance data for the therapist to interrogate.

METHODS:

MaLT incorporates Kinect-based games, a database of objects and language parameters, and a reporting tool for therapists. Games have been developed that target four major language therapy tasks involving single word comprehension, initial phoneme identification, rhyme identification and a naming task. These tasks have 8 levels each increasing in difficulty. A database of 750 objects is used to programmatically generate appropriate questions for the game, providing both targeted therapy and unique gameplay every time. The design of the games has been informed by therapists and by discussions with a Public Patient Involvement (PPI) group.

RESULTS:

Pilot MaLT trials have been conducted with three stroke survivors for the duration of 6 to 8 weeks. Patients’ performance is monitored through MaLT’s reporting facility presented as graphs plotted from patient game data. Performance indicators include reaction time, accuracy, number of incorrect responses and hand use. The resultant games have also been tested by the PPI with a positive response and further suggestions for future modifications made.

CONCLUSION:

MaLT provides a tool that innovatively combines motor and language therapy for high dosage rehabilitation in the home. It has demonstrated that motion sensor technology can be successfully combined with a language therapy task to target both upper limb and linguistic impairment in patients following brain injury. The initial studies on stroke survivors have demonstrated that the combined therapy approach is viable and the outputs of this study will inform planned larger scale future trials.

KEYWORDS:

 

via MaLT – Combined Motor and Language Therapy Tool for Brain Injury Patients Using Kinect. – PubMed – NCBI

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

Leave a comment

[WEB SITE] Games, Gloves, and Grip: PTs Rehab Arms and Hands Post-Stroke With YouGrabber

Published on 

http://www.dreamstime.com/stock-photos-virtual-reality-concept-cement-texture-background-image74595193

Playing virtual reality games could be as effective as adding extra physical therapy sessions to a stroke patient’s rehab regimen, according to researchers.

“It is not a question of choosing one thing over the other, rather of having different training alternatives to provide variation,” says Iris Brunner, author of a study, published recently in Neurology, that explored a variety of medical uses for virtual reality.

“Virtual reality cannot replace physical therapy. But it can be experienced as a game, motivating patients to do an extra treatment session,” adds Brunner, associate professor with the University of Aarhus and Hammel Neurocenter, in Denmark.

Brunner and her team’s study included 120 stroke patients with mild to severe hand weakness, all of whom were randomly assigned to add 16 hour-long therapy sessions to their routine rehabilitation over a month. One group performed physical therapy, while the other group played a virtual reality game called YouGrabber, notes a media release from HealthDay.

In the game, Brunner explains, “the patients wear gloves with sensors, and their movements are tracked by an infrared camera and transferred to a virtual arm on screen.”

“In different scenarios, they can grasp objects that come toward them or pick carrots. In other games, patients steer a plane or a car with their movement. The therapist chooses the movements to be trained and the level of difficulty.”

Fifty patients in the physical therapy group and 52 in the virtual reality group completed the study and were evaluated after 3 months.

The researchers found no difference between the two groups with regard to the improvement in their hand and arm function.

“Patients who started out with moderately to mildly impaired arm and hand motor function achieved, on average, a level of good motor function,” Brunner states, while those with severe weakness were able to use their arms to make movements.

Patients with severe hand weakness appreciated how even small movements translated to the virtual arms on screen, she adds. And even the older patients liked the virtual reality game, she notes, possibly because the graphics are simpler than those in commercial video games.

Brunner concludes by noting that larger studies are needed to understand the potential value of virtual reality as a stroke recovery treatment.

[Source: HealthDay]

 

via Games, Gloves, and Grip: PTs Rehab Arms and Hands Post-Stroke With YouGrabber – Rehab Managment

, , , , , , , , ,

Leave a comment

[Abstract+References] Motion-Based Serious Games for Hand Assistive Rehabilitation

Abstract

Cerebral Palsy, trauma, and strokes are common causes for the loss of hand movements and the decrease in muscle strength for both children and adults. Improving fine motor skills usually involves the synchronization of wrists and fingers by performing appropriate tasks and activities. This demo introduces a novel patient-centered framework for the gamification of hand therapies in order to facilitate and encourage the rehabilitation process. This framework consists of an adaptive therapy-driven 3D environment augmented with our motion-based natural user interface. An intelligent game generator is developed, which translates the patient’s gestures into navigational movements with therapy-driven goals, while adapting the level of difficulty based on the patient profile and real-time performance. A comprehensive evaluation and clinical-based assessments were conducted in a local children disability center, and highlights of the results are presented.

References

1
2
3
4
5

via Motion-Based Serious Games for Hand Assistive Rehabilitation

, , , , ,

Leave a comment

[Abstract+References] A Serious Games Platform for Cognitive Rehabilitation with Preliminary Evaluation

Abstract

In recent years Serious Games have evolved substantially, solving problems in diverse areas. In particular, in Cognitive Rehabilitation, Serious Games assume a relevant role. Traditional cognitive therapies are often considered repetitive and discouraging for patients and Serious Games can be used to create more dynamic rehabilitation processes, holding patients’ attention throughout the process and motivating them during their road to recovery. This paper reviews Serious Games and user interfaces in rehabilitation area and details a Serious Games platform for Cognitive Rehabilitation that includes a set of features such as: natural and multimodal user interfaces and social features (competition, collaboration, and handicapping) which can contribute to augment the motivation of patients during the rehabilitation process. The web platform was tested with healthy subjects. Results of this preliminary evaluation show the motivation and the interest of the participants by playing the games.

References

  1. 1.
    Burke, J. W., McNeill, M. D. J., Charles, D. K., Morrow, P. J., Crosbie, J. H., and McDonough, S. M., Optimising engagement for stroke rehabilitation using serious games. Vis. Comput. 25:1085–1099, 2009.CrossRefGoogle Scholar
  2. 2.
    Burke, J. W., McNeill, M. D. J., Charles, D. K., Morrow, P. J., Crosbie, J. H., McDonough, S. M. Augmented reality games for upper-limb stroke rehabilitation. In: 2010 second international conference on games and virtual worlds for serious applications (VS-GAMES). pp. 75–78. 2010.Google Scholar
  3. 3.
    Maclean, N., Pound, P., Wolfe, C., and Rudd, A., Qualitative analysis of stroke patients’ motivation for rehabilitation. Br. Med. J. 321:1051–1054, 2000.CrossRefGoogle Scholar
  4. 4.
    Krichevets, A. N., Sirotkina, E. B., Yevsevicheva, I. V., and Zeldin, L. M., Computer games as a means of movement rehabilitation. Disabil. Rehabil. 17:100–105, 1995.CrossRefPubMedGoogle Scholar
  5. 5.
    Rego, P., Moreira, P. M., Reis, L. P., Serious games for rehabilitation: a survey and a classification towards a taxonomy. In: 5th Iberian conference on information systems and technologies. Vol. I. pp. 349–354. Santiago de Compostela, Spain, 2010.Google Scholar
  6. 6.
    Rego, P. A., Moreira, P. M., Reis, L. P., New forms of interaction in serious games for rehabilitation. In: Cruz-Cunha, M. M., (Ed.), Handbook of research on serious games as educational, business, and research tools: development and design. IGI Global, 2012.Google Scholar
  7. 7.
    Rego, P. A., Moreira, P. M., and Reis, L. P., A serious games framework for health rehabilitation. Int. J. Healthc. Inf. Syst. Inf. (IJHISI) 9:1–21, 2014.CrossRefGoogle Scholar
  8. 8.
    Rego, P. A., Moreira, P. M., Reis, L. P., Architecture for serious games in health rehabilitation. In: Rocha, Á., Correia, A. M., Tan, F. B., Stroetmann, K. A.. (Eds.), New perspectives in information systems and technologies, volume 2, Vol. 276. pp. 307–317. Springer International Publishing, 2014.Google Scholar
  9. 9.
    Mendes, L., Dores, A. R., Rego, P. A., Moreira, P. M., Barbosa, F., Reis, L. P., Viana, J., Coelho, A., and Sousa, A., Virtual centre for the rehabilitation of road accident victims (VICERAVI). In: Rocha, A., CalvoManzano, J., Reis, L. P., and Cota, M. P. (Eds.), 7th Iberian conference on information systems and technologies (CISTI 2012), vol. I. AISTI, Madrid, pp. 817–822, 2012.Google Scholar
  10. 10.
    Rocha, R., Reis, L. P., Rego, P. A., Moreira, P. M., Serious games for cognitive rehabilitation: Forms of interaction and social dimension. In: 2015 10th Iberian conference on information systems and technologies (CISTI). pp. 1–6. 2015.Google Scholar
  11. 11.
    Alankus, G., Lazar, A., May, M., Kelleher, C., Towards customizable games for stroke rehabilitation. In: Proceedings of the SIGCHI conference on human factors in computing systems. pp. 2113–2122. ACM, Atlanta, Georgia, USA, 2010.Google Scholar
  12. 12.
    Ma, M., and Bechkoum, K., Serious games for movement therapy after stroke. IEEE international conference on systems, man and cybernetics. International Convention & Exhibition Center, Suntec Singapore, pp. 1872–1877, 2008.Google Scholar
  13. 13.
    Karray, F., Alemzadeh, M., Saleh, J. A., and Arab, M. N., Human-computer interaction: overview on state of the art. Int. J. Smart Sens. Intell. Syst. 1:137–159, 2008.Google Scholar
  14. 14.
    Oviatt, S., Multimodal interfaces. In: Julie, A. J., Andrew, S., (Eds.), The human-computer interaction handbook, pp. 286–304. L. Erlbaum Associates Inc, 2003.Google Scholar
  15. 15.
    Rego, P. A., Moreira, P. M., Reis, L. P., Natural user interfaces in serious games for rehabilitation: a prototype and playability study. In: Rocha, Á., Gonçalves, R., Cota, M. P., Reis, L. P., (Eds.), First Iberian Workshop on Serious Games and Meaningful Play (SGaMePlay’2011) – Proceedings of the 6th iberian conference on information systems and technologies, Vol. I. pp. 229–232. Chaves, Portugal, 2011.Google Scholar
  16. 16.
    Rego, P. A., Moreira, P. M., Reis, L. P., Natural and multimodal user interfaces in serious games for health rehabilitation. In: MASH’14: Multi-agent systems for healthcare / AAMAS’14 – 13th international conference on autonomous agents and multiagent systems. IFAMAAS, 2014.Google Scholar
  17. 17.
    Jaimes, A., and Sebe, N., Multimodal human-computer interaction: a survey. Comput. Vis. Image Underst. 108:116–134, 2007.CrossRefGoogle Scholar
  18. 18.
    Jain, J., Lund, A., Wixon, D., The future of natural user interfaces. In: CHI ‘11 extended abstracts on human factors in computing systems. pp. 211–214. ACM, 1979527, 2011.Google Scholar
  19. 19.
    Chai, J. Y., Hong, P., Zhou, M. X., A probabilistic approach to reference resolution in multimodal user interfaces. In: Proceedings of the 9th international conference on intelligent user interfaces. pp. 70–77. ACM, Funchal, Madeira, Portugal, 2004.Google Scholar
  20. 20.
    Faria, B. M., Reis, L. P., Lau, N., Soares, J. C., and Vasconcelos, S., Patient classification and automatic configuration of an intelligent wheelchair. In: Filipe, J., and Fred, A. (Eds.), Agents and artificial intelligence, vol. 358. Springer, Berlin Heidelberg, pp. 268–282, 2013.CrossRefGoogle Scholar
  21. 21.
    Johnston, M., Bangalore, S., MATCHkiosk: a multimodal interactive city guide. In: Proceedings of the ACL 2004 on Interactive poster and demonstration sessions. pp. 33. Association for Computational Linguistics, 2004.Google Scholar
  22. 22.
    Ibrahim, A., and Johansson, P., Multimodal dialogue systems: a case study for interactive TV. In: Carbonell, N., and Stephanidis, C. (Eds.), Universal access theoretical perspectives, practice, and experience: 7th ERCIM international workshop on user interfaces for all, Paris, France, October 24–25, 2002, revised papers. Springer Berlin Heidelberg, Berlin, pp. 209–218, 2003.CrossRefGoogle Scholar
  23. 23.
    Morikawa, C., and Lyons, M. J., Design and evaluation of vision-based head and face tracking interfaces for assistive input. In: Georgios, K. (Ed.), Assistive technologies and computer access for motor disabilities. IGI Global, Hershey, pp. 180–205, 2014.CrossRefGoogle Scholar
  24. 24.
    Ronzhin, A., Karpov, A., Assistive multimodal system based on speech recognition and head tracking. In: Proceedings of 13th European Signal Processing Conference. 2005Google Scholar
  25. 25.
    Reis, L., Faria, B., Vasconcelos, S., Lau, N., Invited paper: multimodal interface for an intelligent wheelchair. In: Ferrier, J.-L., Gusikhin, O., Madani, K., Sasiadek, J., (Eds.), Informatics in control, automation and robotics, Vol. 325. pp. 1–34. Springer International Publishing, 2015Google Scholar
  26. 26.
    Ogiela, M. R., and Hachaj, T., Natural user interfaces in medical image analysis: cognitive analysis of brain and carotid artery images. Springer International Publishing, Switzerland, 2014.Google Scholar
  27. 27.
    Steinberg, G., Natural user interfaces. In: ACM SIGCHI conference on human factors in computing systems. 2012.Google Scholar
  28. 28.
    Faria, B. M., Reis, L. P., Lau, N., Moreira, A. P., Petry, M., Ferreira, L. M., Intelligent wheelchair driving: bridging the gap between virtual and real intelligent wheelchairs. In: Pereira, F., Machado, P., Costa, E., Cardoso, A., (Eds.), Progress in artificial intelligence. Vol. 9273, pp. 445–456. Springer International Publishing, 2015.Google Scholar
  29. 29.
    Faria, B. M., Reis, L. P., Lau, N., A methodology for creating an adapted command language for driving an intelligent wheelchair. J. Intell. Robot. Syst. 80, 2015.Google Scholar
  30. 30.
    Faria, B., Reis, L., and Lau, N., Adapted control methods for cerebral palsy users of an intelligent wheelchair. J. Intell. Robot. Syst. 77:299–312, 2015.CrossRefGoogle Scholar
  31. 31.
    Faria, B. M., Silva, A., Faias, J., Reis, L. P., Lau, N., Intelligent wheelchair driving: a comparative study of cerebral palsy adults with distinct boccia experience. In: Rocha, Á., Correia, A. M., Tan, F. B., Stroetmann, K. A., (Eds.), New perspectives in information systems and technologies, volume 2. Vol. 276. pp. 329–340. Springer International Publishing, 2014.Google Scholar
  32. 32.
    Faria, B. M., Vasconcelos, S., and Reis, L. P., Evaluation of distinct input methods of an intelligent wheelchair in simulated and real environments: a performance and usability study. Assist. Technol. Off. J. RESNA 25:88–98, 2013.CrossRefGoogle Scholar
  33. 33.
    Faria, B., Reis, L., Teixeira, S., Faias, J., Lau, N., Intelligent wheelchair simulator for users’ training cerebral palsy children’s case study. In: 8th Iberian conference on information systems and technologies (CISTI). 2013.Google Scholar
  34. 34.
    Faria, B. M., Vasconcelos, S., Reis, L. P., Lau, N., A methodology for creating intelligent wheelchair users’ profiles. In: ICAART 2012 – 4th International conference on agents and artificial intelligence. pp. 171–179. 2012.Google Scholar
  35. 35.
    Moussa, M. B., Magnenat-Thalmann, N., Applying affect recognition in serious games: the playmancer project. In: Egges, A., Geraerts, R., Overmars, M., (Eds.), Motion in games. pp. 53–62. Springer, 2009.Google Scholar
  36. 36.
    Gerling, K., Livingston, I., Nacke, L., Mandryk, R., Full-body motion-based game interaction for older adults. In: Proceedings of the SIGCHI conference on human factors in computing systems. pp. 1873–1882. ACM, Austin, Texas, USA, 2012.Google Scholar
  37. 37.
    Chang, Y.-J., Chen, S.-F., and Chuang, A.-F., A gesture recognition system to transition autonomously through vocational tasks for individuals with cognitive impairments. Res. Dev. Disabil. 32:2064–2068, 2011.CrossRefPubMedGoogle Scholar
  38. 38.
    Ciger, J., Herbeliny, B., Thalmannz, D., Evaluation of gaze tracking technology for social interaction in virtual environments. In: Proceedings of the 2nd workshop on modeling and motion capture techniques for virtual environments (CAPTECH04). 2004.Google Scholar
  39. 39.
    Jacob, R. J. K., Karn, K. S., Eye tracking in human-computer interaction and usability research: ready to deliver the promises. The mind’s eye: cognitive the mind’s eye: cognitive and applied aspects of eye movement research. pp. 573–603. 2003.Google Scholar
  40. 40.
    Mohamed, A. O., Silva, M. P. D., Courboulay, V., A history of eye gaze tracking. Tech. Rep.2008.Google Scholar
  41. 41.
    Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., and Taylor, J. G., Emotion recognition in human-computer interaction. IEEE Signal Process. Mag. 18:32–80, 2001.CrossRefGoogle Scholar
  42. 42.
    Li, S. Z., and Jain, A. K., Handbook of face recognition. Springer Science & Business Media, Germany, 2011.CrossRefGoogle Scholar
  43. 43.
    Menache, A., Understanding motion capture for computer animation and video games. Morgan Kaufmann, 2000.Google Scholar
  44. 44.
    Kirishima, T., Sato, K., and Chihara, K., Real-time gesture recognition by learning and selective control of visual interest points. IEEE Trans. Pattern Anal. Mach. Intell. 27:351–364, 2005.CrossRefPubMedGoogle Scholar
  45. 45.
    Gavrila, D. M., The visual analysis of human movement: a survey. Comput. Vis. Image Underst. 73:82–98, 1999.CrossRefGoogle Scholar
  46. 46.
    Bradski, G. R., Computer vision face tracking for use in a perceptual user interface. In: Proceedings of the fourth IEEE workshop on applications of computer vision (WACV’98). 1998.Google Scholar
  47. 47.
    Wachs, J. P., Kölsch, M., Stern, H., and Edan, Y., Vision-based hand-gesture applications. Commun. ACM 54:60–71, 2011.CrossRefGoogle Scholar
  48. 48.
    Microsoft kinect for Windows. Available: https://developer.microsoft.com/en-us/windows/kinect, 2016.
  49. 49.
    Leap motion. Available: https://www.leapmotion.com/ 2016.
  50. 50.
    Duchowski, A. T., A breadth-first survey of eye-tracking applications. Behav. Res. Methods Instrum. Comput. 34:455–470, 2002.CrossRefPubMedGoogle Scholar
  51. 51.
    Duchowski, A., Eye tracking methodology: theory and practice. Springer Science & Business Media, 2007.Google Scholar
  52. 52.
    Bulling, A., and Gellersen, H., Toward mobile Eye-based human-computer interaction. IEEE Pervasive Comput. 9:8–12, 2010.CrossRefGoogle Scholar
  53. 53.
    Dickie, C., Vertegaal, R., Sohn, C., Cheng, D., Eyelook: using attention to facilitate mobile media consumption. In: Proceedings of the 18th annual ACM symposium on user interface software and technology. pp. 103–106. ACM, Seattle, WA, USA, 2005.Google Scholar
  54. 54.
    Zhai, S., Morimoto, C., Ihde, S., Manual and gaze input cascaded (MAGIC) pointing. In: Proceedings of the SIGCHI conference on Human factors in computing systems: the CHI is the limit. pp. 246–253. ACM, Pittsburgh, Pennsylvania, United States, 1999.Google Scholar
  55. 55.
    Tobii. Available: http://www.tobii, 2015.
  56. 56.
    Schneiderman, R., Accuracy, apps advance speech recognition [special reports]. IEEE Signal Process. Mag. 32:12–125, 2015.CrossRefGoogle Scholar
  57. 57.
    Schroeder, M. R., Computer speech: recognition, compression, synthesis. Springer Science & Business Media, 2004.Google Scholar
  58. 58.
    Igarashi, T., Hughes, J. F., Voice as sound: using non-verbal voice input for interactive control. In: Proceedings of the 14th annual ACM symposium on User interface software and technology. pp. 155–156. ACM, Orlando, Florida, 2001.Google Scholar
  59. 59.
    Sporka, A. J., Kurniawan, S. H., and Slavík, P., Non-speech operated emulation of keyboard. In: Clarkson, J., Langdon, P., and Robinson, P. (Eds.), Designing accessible technology. Springer London, London, pp. 145–154, 2006.CrossRefGoogle Scholar
  60. 60.
    Bilmes, J. A., Li, X., Malkin, J., Kilanski, K., Wright, R., Kirchhoff, K., Subramanya, A., Harada, S., Landay, J. A., Dowden, P., Chizeck, H., The vocal joystick: a voice-based human-computer interface for individuals with motor impairments. In: Proceedings of the conference on human language technology and empirical methods in natural language processing. pp. 995–1002. Association for Computational Linguistics, 2005.Google Scholar
  61. 61.
    Poláček, O., Sporka, A. J., and Míkovec, Z., Measuring performance of a predictive keyboard operated by humming. In: Miesenberger, K., Karshmer, A., Penaz, P., and Zagler, W. (Eds.), Computers helping people with special needs: 13th international conference, ICCHP 2012, Linz, Austria, July 11-13, 2012, proceedings, part II. Springer Berlin Heidelberg, Berlin, pp. 467–474, 2012.CrossRefGoogle Scholar
  62. 62.
    Harada, S., Wobbrock, J. O., and Landay, J. A., Voice games: investigation into the use of Non-speech voice input for making computer games more accessible. In: Campos, P., Graham, N., Jorge, J., Nunes, N., Palanque, P., and Winckler, M. (Eds.), Human-computer interaction – INTERACT 2011: 13th IFIP TC 13 international conference, Lisbon, Portugal, September 5-9, 2011, proceedings, part I. Springer Berlin Heidelberg, Berlin, pp. 11–29, 2011.CrossRefGoogle Scholar
  63. 63.
    Sporka, A. J., Kurniawan, S. H., Mahmud, M., Slavík, P., Non-speech input and speech recognition for real-time control of computer games. In: Proceedings of the 8th international ACM SIGACCESS conference on computers and accessibility. pp. 213–220. ACM, Portland, Oregon, USA, 2006.Google Scholar
  64. 64.
    Pierre-Yves, O., The production and recognition of emotions in speech: features and algorithms. Int. J. Hum. Comput. Stud. 59:157–183, 2003.CrossRefGoogle Scholar
  65. 65.
    Ververidis, D., and Kotropoulos, C., Emotional speech recognition: resources, features, and methods. Speech Comm. 48:1162–1181, 2006.CrossRefGoogle Scholar
  66. 66.
    Schiel, F., Steininger, S., Türk, U., The SmartKom multimodal corpus at BAS. In: Proc. 3rd Int. Conf. on Language Resources and Evaluation (LREC 2002). pp. 35–41. 2002.Google Scholar
  67. 67.
    France, D. J., Shiavi, R. G., Silverman, S., Silverman, M., and Wilkes, M., Acoustical properties of speech as indicators of depression and suicidal risk. IEEE Trans. Biomed. Eng. 47:829–837, 2000.CrossRefPubMedGoogle Scholar
  68. 68.
    Ozdas, A., Shiavi, R. G., Silverman, S. E., Silverman, M. K., and Wilkes, D. M., Investigation of vocal jitter and glottal flow spectrum as possible cues for depression and near-term suicidal risk. IEEE Trans. Biomed. Eng. 51:1530–1540, 2004.CrossRefPubMedGoogle Scholar
  69. 69.
    Schröder, M., Heylen, D., and Poggi, I., Perception of non-verbal emotional listener feedback. In: Hoffmann, R., and Mixdorff, H. (Eds.), Speech prosody 2006, vol. 40. TUDpress, Dresden, pp. 43–46, 2006.Google Scholar
  70. 70.
    Kostoulas, T., Mporas, I., Kocsis, O., Ganchev, T., Katsaounos, N., Santamaria, J. J., Jimenez-Murcia, S., Fernandez-Aranda, F., and Fakotakis, N., Affective speech interface in serious games for supporting therapy of mental disorders. Exp. Syst. Appl. 39:11072–11079, 2012.CrossRefGoogle Scholar
  71. 71.
    Hayward, V., Astley, O. R., Cruz-Hernandez, M., Grant, D., and Robles-De-La-Torre, G., Haptic interfaces and devices. Sens. Rev. 24:16–29, 2004.CrossRefGoogle Scholar
  72. 72.
    Göger, D., Weiß, K., Burghart, C., Wörn, H., Sensitive skin for a humanoid robot. In: Proceedings of the 2006 international conference on human-centered robotic systems. 2006.Google Scholar
  73. 73.
    AAPB. Available: http://www.aapb.org/, 2011.
  74. 74.
    Conconi, A., Ganchev, T., Kocsis, O., Papadopoulos, G., Fernandez-Aranda, F., Jimenez-Murcia, S., PlayMancer: a serious gaming 3D environment. In: International conference on automated solutions for cross media content and multi-channel distribution (AXMEDIS ‘08). pp. 111–117. Institute of Electrical and Electronics Engineers (IEEE), 2008.Google Scholar
  75. 75.
    Nacke, L. E., Kalyn, M., Lough, C., Mandryk, R .L., Biofeedback game design: using direct and indirect physiological control to enhance game interaction. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. pp. 103–112. ACM, Vancouver, BC, Canada, 2011.Google Scholar
  76. 76.
    Kuikkaniemi, K., Laitinen, T., Turpeinen, M., Saari, T., Kosunen, I., Ravaja, N., The influence of implicit and explicit biofeedback in first-person shooter games. In: Proceedings of the SIGCHI conference on human factors in computing systems. pp. 859–868. ACM, Atlanta, Georgia, USA, 2010.Google Scholar
  77. 77.
    Flynn, S., Palma, P., and Bender, A., Feasibility of using the Sony PlayStation 2 gaming platform for an individual poststroke: a case report. J. Neurol. Phys. Ther. 31:180–189, 2007.CrossRefPubMedGoogle Scholar
  78. 78.
    Saposnik, G., Teasell, R., Mamdani, M., Hall, J., McIlroy, W., Cheung, D., Thorpe, K., Cohen, L., and Bayley, M., Effectiveness of virtual reality using Wii gaming technology in stroke rehabilitation: a pilot randomized clinical trial and proof of principle. Stroke41:1477–1484, 2010.CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    Nintendo: Wii console. Available: http://www.nintendo.com/wii/console, 2014.
  80. 80.
    Sony: playstation move. Available: http://pt.playstation.com/psmove/, 2014.
  81. 81.
    Vanacken, L., Notelaers, S., Raymaekers, C., Coninx, K., van den Hoogen, W., Jsselsteijn, W. I., Feys, P., Game-based collaborative training for arm rehabilitation of MS patients: a proof-of-concept game. In: Proceedings of the GameDays 2010. pp. 65–75. 2010.Google Scholar
  82. 82.
    Battocchi, A., Gal, E., Ben Sasson, A., Painesi, F., Venuti, P., Zancanaro, M., Weiss, P. L., Collaborative puzzle game – an interface for studying collaboration and social interaction for children who are typically developed or who have autistic spectrum disorder. In: Proceedings of the 7th International Conference series on disability, virtual reality and associated technologies (ICDVRAT). pp. 127–134. 2008.Google Scholar
  83. 83.
    Battocchi, A., Pianesi, F., Tomasini, D., Zancanaro, M., Esposito, G., Venuti, P., Sasson, A. B., Gal, E., Weiss, P. L., Collaborative puzzle game: a tabletop interactive game for fostering collaboration in children with Autism Spectrum Disorders (ASD). In: Proceedings of the ACM international conference on interactive tabletops and surfaces. pp. 197–204. ACM, Banff, Alberta, Canada, 2009.Google Scholar
  84. 84.
    Caglio, M., Latini-Corazzini, L., D’agata, F., Cauda, F., Sacco, K., Monteverdi, S., Zettin, M., Duca, S., and Geminiani, G., Video game play changes spatial and verbal memory: rehabilitation of a single case with traumatic brain injury. Cogn. Process. 10:195–197, 2009.CrossRefGoogle Scholar
  85. 85.
    Cameirão, M. S., Badia, S. B., Zimmerli, L., Oller, E. D., and Vershure, P. F. M. J., The rehabilitation gaming system: a review. Stud. Health Technol. Inform. 145:65–83, 2009.PubMedGoogle Scholar
  86. 86.
  87. 87.
    Maia, L., Gaspar, C., Azevedo, M., Loureiro, M. J., and Silva, C. F., Reabilitação cognitiva assistida por computador: o programa RehaCom e a sua utilização no GEARNeurop. Psiquiatr. Clín. 25:83–105, 2004.Google Scholar
  88. 88.
    Parrot software. Available: http://www.parrotsoftware.com/, 2016.
  89. 89.
    Fundación intras. Available: http://www.intras.es/index.php?id=75, 2014.
  90. 90.
    StatCounter: GlobalStats. Available: http://gs.statcounter.com/#browser-ww-monthly-201409-201509-bar, 2015.
  91. 91.
    Bangor, A., Kortum, P., and Miller, J., Determining what individual SUS scores mean: adding an adjective rating scale. J. Usability Stud. 4:114–123, 2009.Google Scholar

via A Serious Games Platform for Cognitive Rehabilitation with Preliminary Evaluation | SpringerLink

, , , , ,

Leave a comment

[Abstract] Feasibility study of a serious game based on Kinect system for functional rehabilitation of the lower limbs

Summary

Introduction

Conventional functional rehabilitation costs time, money and effort for the patients and for the medical staff. Serious games have been used as a new approach to improve the performance as well as to possibly reduce medical cost in the future for cognitive rehabilitation and body balance control. The objective of this present work was to perform a feasibility study on the use of a new real-time serious game system for improving the musculoskeletal rehabilitation of the lower limbs.

Materials and methods

A basic functional rehabilitation exercise database was established with different levels of difficulties. A 3D virtual avatar was created and scaled to represent each subject-specific body. A portable and affordable Kinect sensor was used to capture real-time kinematics during each exercise. A specific data coupling process was developed. An evaluation campaign was established to assess the developed system.

Results

The squats exercise was the hardest challenge. Moreover, the performance of each functional rehabilitation exercise depended on the physiological profile of each participant. Our game system was clear and attractive for all functional rehabilitation exercises. All testing subjects felt motivated and secure when playing the rehabilitation game.

Discussion

The comparison with other systems showed that our system was the first one focusing on the functional rehabilitation exercises of the lower limbs.

Conclusions

Our system showed useful functionalities for a large range of applications (rehabilitation at home, sports training). Looking forward, new in-situation exercises will be investigated for specific musculoskeletal disorders.

via Feasibility study of a serious game based on Kinect system for functional rehabilitation of the lower limbs – ScienceDirect

, , , , , , ,

Leave a comment

[Technical note] Serious game and functional rehabilitation for the lower limbs

Summary

Introduction

Conventional functional rehabilitation consists of a therapeutic consultation, a motor exercise assignment, and an execution task with or without assistance of the therapist. The objective of this technical note was to present a new real-time 3D serious game system concept for musculoskeletal rehabilitation of the lower limbs.

Materials and method

A generic development workflow of real-time 3D serious game systems for functional rehabilitation of the lower limbs was proposed. A user-friendly system flowchart was also established for a better interaction between end-users and the game system.

Result and discussion

Different system components like avatar modeling, subject registration, rehabilitation game and feedback visualization and control were detailed and their advantages and limitations were discussed.

Conclusions

3D serious game technologies open new perspectives for a large range of rehabilitation applications (at home or in clinic environment, sports training).

via Serious game and functional rehabilitation for the lower limbs – ScienceDirect

, , , , ,

Leave a comment

%d bloggers like this: