Assistive game controller for artificial intelligence-enhanced telerehabilitation post-stroke

The Artificial Intelligence Revolution in Stroke Care: A Decade of Scientific Evidence in Review

BACKGROUND

The emergence of artificial intelligence (AI) has significantly influenced the diagnostic evaluation of stroke and has revolutionized acute stroke care delivery. The scientific evidence evaluating the role of AI, especially in areas of stroke treatment and rehabilitation is limited but continues to accumulate. We performed a systemic review of current scientific evidence evaluating the use of AI in stroke evaluation and care and examined the publication trends during the past decade.

METHODS

A systematic search of electronic databases was conducted to identify all studies published from 2012 to 2022 that incorporated AI in any aspect of stroke care. Studies not directly relevant to stroke care in the context of AI and duplicate studies were excluded. The level of evidence and publication trends were examined.

RESULTS

A total of 623 studies were examined, including 101 reviews (16.2%), 9 meta-analyses (1.4%), 140 original articles on AI methodology (22.5%), 2 case reports (0.3%), 2 case series (0.3%), 31 case-control studies (5%), 277 cohort studies (44.5%), 16 cross-sectional studies (2.6%), and 45 experimental studies (7.2%). The highest published area of AI in stroke was diagnosis (44.1%) and the lowest was rehabilitation (12%). A 10-year trend analysis revealed a significant increase in AI literature in stroke care.

CONCLUSIONS

Most research on AI is in the diagnostic area of stroke care, with a recent noteworthy trend of increased research focus on stroke treatment and rehabilitation.

The Role of AI in Serious Games and Gamification for Health: Scoping Review

BACKGROUND

Artificial intelligence (AI) and game-based methods such as serious games or gamification are both emerging technologies and methodologies in health care. The merging of the two could provide greater advantages, particularly in the field of therapeutic interventions in medicine.

OBJECTIVE

This scoping review sought to generate an overview of the currently existing literature on the connection of AI and game-based approaches in health care. The primary objectives were to cluster studies by disease and health topic addressed, level of care, and AI or games technology.

METHODS

For this scoping review, the databases PubMed, Scopus, IEEE Xplore, Cochrane Library, and PubPsych were comprehensively searched on February 2, 2022. Two independent authors conducted the screening process using Rayyan software (Rayyan Systems Inc). Only original studies published in English since 1992 were eligible for inclusion. The studies had to involve aspects of therapy or education in medicine and the use of AI in combination with game-based approaches. Each publication was coded for basic characteristics, including the population, intervention, comparison, and outcomes (PICO) criteria; the level of evidence; the disease and health issue; the level of care; the game variant; the AI technology; and the function type. Inductive coding was used to identify the patterns, themes, and categories in the data. Individual codings were analyzed and summarized narratively.

RESULTS

A total of 16 papers met all inclusion criteria. Most of the studies (10/16, 63%) were conducted in disease rehabilitation, tackling motion impairment (eg, after stroke or trauma). Another cluster of studies (3/16, 19%) was found in the detection and rehabilitation of cognitive impairment. Machine learning was the main AI technology applied and serious games the main game-based approach used. However, direct interaction between the technologies occurred only in 3 (19%) of the 16 studies. The included studies all show very limited quality evidence. From the patients' and healthy individuals' perspective, generally high usability, motivation, and satisfaction were found.

CONCLUSIONS

The review shows limited quality of evidence for the combination of AI and games in health care. Most of the included studies were nonrandomized pilot studies with few participants (14/16, 88%). This leads to a high risk for a range of biases and limits overall conclusions. However, the first results present a broad scope of possible applications, especially in motion and cognitive impairment, as well as positive perceptions by patients. In future, the development of adaptive game designs with direct interaction between AI and games seems promising and should be a topic for future reviews.

Artificial Intelligence-Based Consumer Health Informatics Application: Scoping Review

BACKGROUND

There is no doubt that the recent surge in artificial intelligence (AI) research will change the trajectory of next-generation health care, making it more approachable and accessible to patients. Therefore, it is critical to research patient perceptions and outcomes because this trend will allow patients to be the primary consumers of health technology and decision makers for their own health.

OBJECTIVE

This study aimed to review and analyze papers on AI-based consumer health informatics (CHI) for successful future patient-centered care.

METHODS

We searched for all peer-reviewed papers in PubMed published in English before July 2022. Research on an AI-based CHI tool or system that reports patient outcomes or perceptions was identified for the scoping review.

RESULTS

We identified 20 papers that met our inclusion criteria. The eligible studies were summarized and discussed with respect to the role of the AI-based CHI system, patient outcomes, and patient perceptions. The AI-based CHI systems identified included systems in mobile health (13/20, 65%), robotics (5/20, 25%), and telemedicine (2/20, 10%). All the systems aimed to provide patients with personalized health care. Patient outcomes and perceptions across various clinical disciplines were discussed, demonstrating the potential of an AI-based CHI system to benefit patients.

CONCLUSIONS

This scoping review showed the trend in AI-based CHI systems and their impact on patient outcomes as well as patients' perceptions of these systems. Future studies should also explore how clinicians and health care professionals perceive these consumer-based systems and integrate them into the overall workflow.

Evaluating the usability of a co-designed power assisted exercise graphical user interface for people with stroke

BACKGROUND

Digital advancement of power assisted exercise equipment will advance exercise prescription for people with stroke (PwS). This article reports on the remote usability evaluation of a co-designed graphical user interface (GUI) and denotes an example of how video-conference software can increase reach to participants in the testing of rehabilitation technologies. The aim of this study was to evaluate the usability of two sequential versions of the GUI.

METHODS

We adopted a mixed methods approach. Ten professional user (PU) (2M/8F) and 10 expert user (EU) participants (2M/8F) were recruited. Data collection included a usability observation, a 'think aloud' walk through, task completion, task duration and user satisfaction as indicated by the Post Study System Usability Questionnaire (PSSUQ). Identification of usability issues informed the design of version 2 which included an additional submenu. Descriptive analysis was conducted upon usability issues and number of occurrences detected on both versions of the GUI. Inferential analysis enabled comparison of task duration and PSSUQ data between the PU and EU groups.

RESULTS

Analysis of the 'think aloud' walkthrough data enabled identification of 22 usability issues on version 1 from a total of 100 usability occurrences. Task completion for all tasks was 100%. Eight usability issues were directly addressed in the development of version 2. Two recurrent and 24 new usability issues were detected in version 2 with a total of 86 usability occurrences. Paired two tailed T-tests on task duration data indicated a significant decrease amongst the EU group for task 1.1 on version 2 (P = 0.03). The mean PSSUQ scores for version 1 was 1.44 (EU group) and 1.63 (PU group) compared with 1.40 (EU group) and 1.41 (PU group) for version 2.

CONCLUSIONS

The usability evaluation enabled identification of usability issues on version 1 of the GUI which were effectively addressed on the iteration of version 2. Testing of version 2 identified usability issues within the new submenu. Application of multiple usability evaluation methods was effective in identifying and addressing usability issues in the GUI to improve the experience of PAE for PwS. The use of video-conference software to conduct synchronous, remote usability testing is an effective alternative to face to face testing methods.

Artificial Intelligence-Driven Serious Games in Healthcare: A Scoping Review (Preprint).. [DOI: 10.2196/preprints.39840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Artificial Intelligence (AI)-driven serious games have been used in healthcare to offer a customizable and immersive experience. Summarizing the features of the current AI-driven serious games is very important to explore how they have been developed and used and their current state in order to plan on how to leverage them in the current and future healthcare needs.

OBJECTIVE

The current study aimed to explore the features of AI-driven serious games in healthcare as reported by previous research.

METHODS

We carried out a scoping review to achieve the above-mentioned objective. The most popular databases in information technology and health fields (e.g., MEDLINE and IEEE Xplore) were searched using keywords related to serious games and AI. These terms were selected based on the target intervention (i.e., AI) and the target disease (i.e., COVID-19). Two reviewers independently performed the study selection process. Three reviewers independently used Microsoft Excel to extract data from the included studies. A narrative approach was used for data synthesis.

RESULTS

The search process returned 1470 records. Of these records, 46 met all eligibility criteria. 60 different serious games were found in the included studies. Motor impairment was the most common health condition targeted by these serious games. Serious games in most of the studies were used for rehabilitation. The serious games in the majority of the included studies can be played by only single player. Most serious games were played on standalone devices (offline games). The most common genres of serious games were role-playing games, puzzle games, and platformer games. Unity was the most prominent game engine used to develop serious games. Personal computers (PCs) were the most common platforms used to play serious games. The most common algorithms used in the included studies were Support Vector Machine (SVM), Convolutional Neural Network (CNN), Artificial Neural Networks (ANN), and Random Forest (RF). The most common purposes of AI were the detection of disease and the evaluation of user's performance. The dataset size ranged from 36 to 795,600, with an average of about 52,124. The most common validation techniques used in the included studies were K-fold cross-validation and training test split validation. Accuracy was the most commonly used metric to evaluate the performance of AI models.

CONCLUSIONS

The last decade witnessed an increase in the development of AI-driven serious games for healthcare purposes and targeting various health conditions and leveraging multiple AI algorithms; this rising trend is expected to continue for years to come. While the evidence uncovered in this study shows promising applications of AI-driven serious games, larger and more rigorous, diverse, and robust studies may be needed to examine the efficacy and effectiveness of AI-driven serious games in different populations with different health conditions.

Robotic Table and Serious Games for Integrative Rehabilitation in the Early Poststroke Phase: Two Case Reports

BACKGROUND

BrightArm Compact is a new rehabilitation system for the upper extremities. It provides bimanual training with gradated gravity loading and mediates interactions with cognitively challenging serious games.

OBJECTIVE

The aim of this study is to design and test a robotic rehabilitation table-based virtual rehabilitation system for functional impact of the integrative training in the early poststroke phase.

METHODS

A new robotic rehabilitation table, controllers, and adaptive games were developed. The 2 participants underwent 12 experimental sessions in addition to the standard of care. Standardized measures of upper extremity function (primary outcome), depression, and cognition were administered before and after the intervention. Nonstandardized measures included game variables and subjective evaluations.

RESULTS

The 2 case study participants attained high total arm repetitions per session (504 and 957) and achieved high grasp and finger-extension counts. Training intensity contributed to marked improvements in affected shoulder strength (225% and 100% increase), grasp strength (27% and 16% increase), and pinch strength (31% and 15% increase). The shoulder flexion range increased by 17% and 18% and elbow supination range by 75% and 58%. Improvements in motor function were at or above minimal clinically important difference for the Fugl-Meyer Assessment (11 and 10 points), Chedoke Arm and Hand Activity Inventory (11 and 14 points), and Upper Extremity Functional Index (19 and 23 points). Cognitive and emotive outcomes were mixed. Subjective rating by participants and training therapists were positive (average 4, SD 0.22, on a 5-point Likert scale).

CONCLUSIONS

The design of the robotic rehabilitation table was tested on 2 participants in the early poststroke phase, and results are encouraging for upper extremity functional gains and technology acceptance.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04252170; https://clinicaltrials.gov/ct2/show/NCT04252170.

Novel integrative rehabilitation system for the upper extremity: Design and usability evaluation

PURPOSE

Design and test the usability of a novel virtual rehabilitation system for bimanual training of gravity supported arms, pronation/supination, grasp strengthening, and finger extension.

METHODS

A robotic rehabilitation table, therapeutic game controllers, and adaptive rehabilitation games were developed. The rehabilitation table lifted/lowered and tilted up/down to modulate gravity loading. Arms movement was measured simultaneously, allowing bilateral training. Therapeutic games adapted through a baseline process. Four healthy adults performed four usability evaluation sessions each, and provided feedback using the USE questionnaire and custom questions. Participant's game play performance was sampled and analyzed, and system modifications made between sessions.

RESULTS

Participants played four sessions of about 50 minutes each, with training difficulty gradually increasing. Participants averaged a total of 6,300 arm repetitions, 2,200 grasp counts, and 2,100 finger extensions when adding counts for each upper extremity. USE questionnaire data averaged 5.1/7 rating, indicative of usefulness, ease of use, ease of learning, and satisfaction with the system. Subjective feedback on the custom evaluation form was 84% favorable.

CONCLUSIONS

The novel system was well-accepted, induced high repetition counts, and the usability study helped optimize it and achieve satisfaction. Future studies include examining effectiveness of the novel system when training patients acute post-stroke.

Serious Games for Stroke Telerehabilitation of Upper Limb - A Review for Future Research

Maintaining appropriate home rehabilitation programs after stroke, with proper adherence and remote monitoring is a challenging task. Virtual reality (VR) - based serious games could be a strategy used in telerehabilitation (TR) to engage patients in an enjoyable and therapeutic approach. The aim of this review was to analyze the background and quality of clinical research on this matter to guide future research. The review was based on research material obtained from PubMed and Cochrane up to April 2020 using the PRISMA approach. The use of VR serious games has shown evidence of efficacy on upper limb TR after stroke, but the evidence strength is still low due to a limited number of randomized controlled trials (RCT), a small number of participants involved, and heterogeneous samples. Although this is a promising strategy to complement conventional rehabilitation, further investigation is needed to strengthen the evidence of effectiveness and support the dissemination of the developed solutions.

Feasibility of integrative games and novel therapeutic game controller for telerehabilitation of individuals chronic post-stroke living in the community

BACKGROUND

Intensive, adaptable and engaging telerehabilitation is needed to enhance recovery and maximize outcomes. Such services may be provided under early supported discharge, or later for chronic populations. A novel virtual reality game-based telerehabilitation system was designed for individuals post-stroke to enhance their bimanual upper extremity motor function, cognition, and wellbeing.

OBJECTIVES

To evaluate the feasibility of novel therapeutic game controller and telerehabilitation system for home use.

METHODS

Individuals chronic post-stroke and their caregivers were recruited (n = 8 + 8) for this feasibility study. One was a screen failure and seven completed 4 weeks (20 sessions) of home-based therapy with or without remote monitoring. Standardized clinical outcome measures were taken pre- and post-therapy. Game performance outcomes were sampled at every session, while participant and caregiver subjective evaluations were done weekly.

RESULTS

There was a 96% rate of compliance to protocol, resulting in an average of 13,000 total arm repetitions/week/participant. Group analysis showed significant (p <.05) improvements in grasp strength (effect size [ES] = 0.15), depression (Beck Depression Inventory II, ES = 0.75), and cognition (Neuropsychological Assessment Battery for Executive Function, ES = 0.46). Among the 49 outcome variables, 36 variables (73.5%) improved significantly (p = .001, binomial sign test). Technology acceptance was very good with system rating by participants at 3.7/5 and by caregivers at 3.5/5.

CONCLUSIONS

These findings indicate the feasibility and efficacy of the system in providing home-based telerehabilitation. The BrightBrainer system needs to be further evaluated in randomized control trials and with individuals early post-stroke.