The Practical Work of Ensuring Effective Use of Serious Games in a Rehabilitation Clinic: A Qualitative Study

Current status and clinical perspectives of extended reality for myoelectric prostheses: review

Training with "Extended Reality" or X-Reality (XR) systems can undoubtedly enhance the control of the myoelectric prostheses. However, there is no consensus on which factors improve the efficiency of skill transfer from virtual training to actual prosthesis abilities. This review examines the current status and clinical applications of XR in the field of myoelectric prosthesis training and analyses possible influences on skill migration. We have conducted a thorough search on databases in the field of prostheses using keywords such as extended reality, virtual reality and serious gaming. Our scoping review encompassed relevant applications, control methods, performance evaluation and assessment metrics. Our findings indicate that the implementation of XR technology for myoelectric rehabilitative training on prostheses provides considerable benefits. Additionally, there are numerous standardised methods available for evaluating training effectiveness. Recently, there has been a surge in the number of XR-based training tools for myoelectric prostheses, with an emphasis on user engagement and virtual training evaluation. Insufficient attention has been paid to significant limitations in the behaviour, functionality, and usage patterns of XR and myoelectric prostheses, potentially obstructing the transfer of skills and prospects for clinical application. Improvements are recommended in four critical areas: activities of daily living, training strategies, feedback, and the alignment of the virtual environment with the physical devices.

Serious Games Are Not Serious Enough for Myoelectric Prosthetics

Serious games show a lot of potential for use in movement rehabilitation (eg, after a stroke, injury to the spinal cord, or limb loss). However, the nature of this research leads to diversity both in the background of the researchers and in the approaches of their investigation. Our close examination and categorization of virtual training software for upper limb prosthetic rehabilitation found that researchers typically followed one of two broad approaches: (1) focusing on the game design aspects to increase engagement and muscle training and (2) concentrating on an accurate representation of prosthetic training tasks, to induce task-specific skill transfer. Previous studies indicate muscle training alone does not lead to improved prosthetic control without a transfer-enabling task structure. However, the literature shows a recent surge in the number of game-based prosthetic training tools, which focus on engagement without heeding the importance of skill transfer. This influx appears to have been strongly influenced by the availability of both software and hardware, specifically the launch of a commercially available acquisition device and freely available high-profile game development engines. In this Viewpoint, we share our perspective on the current trends and progress of serious games for prosthetic training.

Conceptual Ambiguity Surrounding Gamification and Serious Games in Health Care: Literature Review and Development of Game-Based Intervention Reporting Guidelines (GAMING)

BACKGROUND

In health care, the use of game-based interventions to increase motivation, engagement, and overall sustainability of health behaviors is steadily becoming more common. The most prevalent types of game-based interventions in health care research are gamification and serious games. Various researchers have discussed substantial conceptual differences between these 2 concepts, supported by empirical studies showing differences in the effects on specific health behaviors. However, researchers also frequently report cases in which terms related to these 2 concepts are used ambiguously or even interchangeably. It remains unclear to what extent existing health care research explicitly distinguishes between gamification and serious games and whether it draws on existing conceptual considerations to do so.

OBJECTIVE

This study aims to address this lack of knowledge by capturing the current state of conceptualizations of gamification and serious games in health care research. Furthermore, we aim to provide tools for researchers to disambiguate the reporting of game-based interventions.

METHODS

We used a 2-step research approach. First, we conducted a systematic literature review of 206 studies, published in the Journal of Medical Internet Research and its sister journals, containing terms related to gamification, serious games, or both. We analyzed their conceptualizations of gamification and serious games, as well as the distinctions between the two concepts. Second, based on the literature review findings, we developed a set of guidelines for researchers reporting on game-based interventions and evaluated them with a group of 9 experts from the field.

RESULTS

Our results show that less than half of the concept mentions are accompanied by an explicit definition. To distinguish between the 2 concepts, we identified four common approaches: implicit distinction, synonymous use of terms, serious games as a type of gamified system, and distinction based on the full game dimension. Our Game-Based Intervention Reporting Guidelines (GAMING) consist of 25 items grouped into four topics: conceptual focus, contribution, mindfulness about related concepts, and individual concept definitions.

CONCLUSIONS

Conceptualizations of gamification and serious games in health care literature are strongly heterogeneous, leading to conceptual ambiguity. Following the GAMING can support authors in rigorous reporting on study results of game-based interventions.

A review of the evolution of scientific literature on technology-assisted approaches using RGB-D sensors for musculoskeletal health monitoring

The human musculoskeletal (MSK) system (also known as the locomotor system) provides strength and assistance to perform functional tasks and daily life activities. The MSK health monitoring plays a vital role in maintaining the body mobility and quality of life. Manual approaches for musculoskeletal health monitoring are subjective and require a clinician's intervention. The evolution in motion tracking technology enables us to capture the fine details of body movements. The research community has proposed various approaches to help clinicians in diagnosis and monitor treatment sessions. This paper succinctly reviews the evolution of technology-assisted approaches for musculoskeletal health monitoring, using motion capture sensors. To streamline the search through the literature database, the PICOS framework and PRISMA method have been incorporated. The present study reviews methods to transform motion capture data into kinematics variables and factors that affect the tracking performance of RGB-D sensors. Furthermore, widely utilized time-series filters for skeletal data denoising and smoothing for kinematics analysis, stochastic models for movement modeling, rule-based and template-based approaches for rehabilitation exercises assessment, and telerehabilitation sessions for remote health monitoring are explored. This article analyzes skeletal tracking methods by providing advantages and drawbacks of the state of the art rehabilitation sessions assessment, skeletal joint kinematics analysis, and MSK Telerehabilitation approaches. It also discusses the possible future research avenues to improve musculoskeletal disorder diagnosis and treatment monitoring. Our review signifies that RGB-D sensor-based approaches are inexpensive and portable for disorder diagnosis and treatment monitoring. It can also be a viable option for clinicians to provide contactless healthcare access to patients in the current scenario of the COVID-19 pandemic.

Perception of Game-Based Rehabilitation in Upper Limb Prosthetic Training: Survey of Users and Researchers

Background

Serious games have been investigated for their use in multiple forms of rehabilitation for decades. The rising trend to use games for physical fitness in more recent years has also provided more options and garnered more interest for their use in physical rehabilitation and motor learning. In this study, we report the results of an opinion survey of serious games in upper limb prosthetic training.

Objective

This study investigates and contrasts the expectations and preferences for game-based prosthetic rehabilitation of people with limb difference and researchers.

Methods

Both participant groups answered open and closed questions as well as a questionnaire to assess their user types. The distribution of the user types was compared with a Pearson chi-square test against a sample population. The data were analyzed using the thematic framework method; answers fell within the themes of usability, training, and game design. Researchers shared their views on current challenges and what could be done to tackle these.

Results

A total of 14 people with limb difference and 12 researchers participated in this survey. The open questions resulted in an overview of the different views on prosthetic training games between the groups. The user types of people with limb difference and researchers were both significantly different from the sample population, with χ²₅=12.3 and χ²₅=26.5, respectively.

Conclusions

We found that the respondents not only showed a general willingness and tentative optimism toward the topic but also acknowledged hurdles limiting the adoption of these games by both clinics and users. The results indicate a noteworthy difference between researchers and people with limb difference in their game preferences, which could lead to design choices that do not represent the target audience. Furthermore, focus on long-term in-home experiments is expected to shed more light on the validity of games in upper limb prosthetic rehabilitation.

Rehabilitation Exergames: Use of Motion Sensing and Machine Learning to Quantify Exercise Performance in Healthy Volunteers

Background

Performing physiotherapy exercises in front of a physiotherapist yields qualitative assessment notes and immediate feedback. However, practicing the exercises at home lacks feedback on how well patients are performing the prescribed tasks. The absence of proper feedback might result in patients performing the exercises incorrectly, which could worsen their condition. We present an approach to generate performance scores to enable tracking the progress by both the patient at home and the physiotherapist in the clinic.

Objective

This study aims to propose the use of 2 machine learning algorithms, dynamic time warping (DTW) and hidden Markov model (HMM), to quantitatively assess the patient’s performance with respect to a reference.

Methods

Movement data were recorded using a motion sensor (Kinect V2), capable of detecting 25 joints in the human skeleton model, and were compared with those of a reference. A total of 16 participants were recruited to perform 4 different exercises: shoulder abduction, hip abduction, lunge, and sit-to-stand exercises. Their performance was compared with that of a physiotherapist as a reference.

Results

Both algorithms showed a similar trend in assessing participant performance. However, their sensitivity levels were different. Although DTW was more sensitive to small changes, HMM captured a general view of the performance, being less sensitive to the details.

Conclusions

The chosen algorithms demonstrated their capacity to objectively assess the performance of physical therapy. HMM may be more suitable in the early stages of a physiotherapy program to capture and report general performance, whereas DTW could be used later to focus on the details. The scores enable the patient to monitor their daily performance. They can also be reported back to the physiotherapist to track and assess patient progress, provide feedback, and adjust the exercise program if needed.