Visual Occlusion in an Augmented Reality Post-Stroke Therapy Scenario

Allen, Max; Hoermann, Simon; Piumsomboon, Thammathip; Regenbrecht, Holger

doi:10.1145/2542242.2542250

Cited by 6 publications

(8 citation statements)

References 21 publications

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“…Nearly half of the papers (17/41, 42%) recruited only stroke patients [ 22 , 35 - 50 ], 15% (6/41) recruited only healthy participants [ 51 - 56 ], and 17% (7/41) recruited both patients and healthy participants [ 23 , 30 , 57 - 61 ]. One paper recruited participants for a requirements-gathering phase but no details were provided regarding their health status and demographics [ 62 ], and 20% (8/41) of papers did not recruit any participants [ 63 - 70 ].…”

Section: Resultsmentioning

confidence: 99%

“…Many papers generated such data directly from Kinect-based systems (n=12) [ 23 , 30 , 39 , 41 , 44 , 49 , 55 , 56 , 61 , 63 , 68 , 69 ]. Ten papers generated data from other sensors [ 35 , 50 , 53 , 56 , 58 , 60 , 61 , 67 , 69 , 71 ] such as an inertial wrist strap, 6 papers reported both performance and variable data [ 39 , 53 , 60 , 67 , 69 , 71 ], and 2 papers did not report data from individuals’ use of the rehabilitation systems [ 43 , 51 ]. For more detailed descriptions of these data, please see Multimedia Appendix 7 (Table K).…”

Section: Resultsmentioning

confidence: 99%

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Person-Generated Health Data in Simulated Rehabilitation Using Kinect for Stroke: Literature Review

Dimaguila

Gray

Merolli

2018

JMIR Rehabil Assist Technol

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BackgroundPerson- or patient-generated health data (PGHD) are health, wellness, and clinical data that people generate, record, and analyze for themselves. There is potential for PGHD to improve the efficiency and effectiveness of simulated rehabilitation technologies for stroke. Simulated rehabilitation is a type of telerehabilitation that uses computer technologies and interfaces to allow the real-time simulation of rehabilitation activities or a rehabilitation environment. A leading technology for simulated rehabilitation is Microsoft’s Kinect, a video-based technology that uses infrared to track a user’s body movements.ObjectiveThis review attempts to understand to what extent Kinect-based stroke rehabilitation systems (K-SRS) have used PGHD and to what benefit.MethodsThe review is conducted in two parts. In part 1, aspects of relevance for PGHD were searched for in existing systematic reviews on K-SRS. The following databases were searched: IEEE Xplore, Association of Computing Machinery Digital Library, PubMed, Biomed Central, Cochrane Library, and Campbell Collaboration. In part 2, original research papers that presented or used K-SRS were reviewed in terms of (1) types of PGHD, (2) patient access to PGHD, (3) PGHD use, and (4) effects of PGHD use. The search was conducted in the same databases as part 1 except Cochrane and Campbell Collaboration. Reference lists on K-SRS of the reviews found in part 1 were also included in the search for part 2. There was no date restriction. The search was closed in June 2017. The quality of the papers was not assessed, as it was not deemed critical to understanding PGHD access and use in studies that used K-SRS.ResultsIn part 1, 192 papers were identified, and after assessment only 3 papers were included. Part 1 showed that previous reviews focused on technical effectiveness of K-SRS with some attention on clinical effectiveness. None of those reviews reported on home-based implementation or PGHD use. In part 2, 163 papers were identified and after assessment, 41 papers were included. Part 2 showed that there is a gap in understanding how PGHD use may affect patients using K-SRS and a lack of patient participation in the design of such systems.ConclusionsThis paper calls specifically for further studies of K-SRS—and for studies of technologies that allow patients to generate their own health data in general—to pay more attention to how patients’ own use of their data may influence their care processes and outcomes. Future studies that trial the effectiveness of K-SRS outside the clinic should also explore how patients and carers use PGHD in home rehabilitation programs.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Person-Generated Health Data in Simulated Rehabilitation Using Kinect for Stroke: Literature Review

Dimaguila

Gray

Merolli

2018

JMIR Rehabil Assist Technol

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“…The remaining publications were 3 in "Qualitative analysis," 2 in "Usability assessment techniques," and 4 in "Informal evaluations" that predominantly used unstructured interviews and observations. Two studies of "Usability Evaluation Techniques" [32,33] performed the Think Aloud method during the execution of the experiments.…”

Section: Discussionmentioning

confidence: 99%

“…The upper body for the movement was the most commonly used to control applications, appearing in 14 studies (40%) and included shoulders, arm and forearm, wrists, and elbows. Hands were the second highest occurrence in studies, accounting for 31% (11 studies) of these, 3 contemplated hands and upper limbs [36,46,47]. The lower limbs accounted for 14% of studies (5), 4 of these concerned with walking [38][39][40]44], the other focusing on the lateral movement of the legs for patients with Parkinson's disease [41].…”

Section: Disabilities and Limbs Studiedmentioning

confidence: 99%

Usability Assessments for Augmented Reality Motor Rehabilitation Solutions: A Systematic Review

Cavalcanti

Santana

Gama

et al. 2018

International Journal of Computer Games Technology

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This article aims to understand which methods and user assessment approaches are most commonly used in motor rehabilitation studies that use Augmented Reality (AR) applications. The way in which this was performed and discussed was through a systematic review of the area. Firstly, the different uses of AR in these treatments, and the importance of improving usability studies to evaluate these interfaces and their interactions with their users, were discussed. Then, the systematic review of the literature was structured according to previous studies and covered the period from 2007 to September 2017, using the main scientific journals of the area. Of the 666 results collected in the initial search, 32 articles were selected within the planned requirements and scope. These publications were classified by relevance, using the QualSyst evaluation tool for health technology research, and the type of evaluation, approach, and methods used were catalogued. It was found that most of the studies prioritise methods of data collection, such as task execution and performance analysis. However, through QualSyst, it was verified that the best-evaluated studies chose to use a combination of quantitative and qualitative analysis, through the application of methods such as user preference questionnaires, interviews, and observations. The results presented in this review may benefit designers who intend to design and conduct usability assessments for AR within this context.

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“…The AR task mostly depends on the featured interaction idea, when a certain quality in tracking is guaranteed. Photo-realistic rendering, calculation of lightning effects [12] and the visualization of occlusion [2] improve the user experience (Usability). Therefore, the rendering part plays an important role.…”

Section: Detailed Properties Vs Evaluation Categoriesmentioning

confidence: 99%

Augmented Reality Evaluation: A Concept Utilizing Virtual Reality

Tiefenbacher

Lehment

Rigoll

2014

Lecture Notes in Computer Science

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Abstract. In recent years the field of augmented reality (AR) has seen great advances in interaction, tracking and rendering. New input devices and mobile hardware have enabled entirely new interaction concepts for AR content. The high complexity of AR applications results in lacking usability evaluation practices on part of the developer. In this paper, we present a thorough classification of factors influencing user experience, split into the broad categories of rendering, tracking and interaction. Based on these factors, we propose an architecture for evaluating AR experiences prior to deployment in an adapted virtual reality (VR) environment. Thus we enable rapid prototyping and evaluation of AR applications especially suited for applications in challenging industrial AR projects.

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Visual Occlusion in an Augmented Reality Post-Stroke Therapy Scenario

Cited by 6 publications

References 21 publications

Person-Generated Health Data in Simulated Rehabilitation Using Kinect for Stroke: Literature Review

Person-Generated Health Data in Simulated Rehabilitation Using Kinect for Stroke: Literature Review

Usability Assessments for Augmented Reality Motor Rehabilitation Solutions: A Systematic Review

Augmented Reality Evaluation: A Concept Utilizing Virtual Reality

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