Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps Approach

Efficacy of Sensor-Based Training Using Exergaming or Virtual Reality in Patients with Chronic Low Back Pain: A Systematic Review

In its chronic and non-specific form, low back pain is experienced by a large percentage of the population; its persistence impacts the quality of life and increases costs to the health care system. In recent years, the scientific literature highlights how treatment based on assessment and functional recovery is effective through IMU technology with biofeedback or exergaming as part of the tools available to assist the evaluation and treatment of these patients, who present not only with symptoms affecting the lumbar spine but often also incorrect postural attitudes. Aim: Evaluate the impact of technology, based on inertial sensors with biofeedback or exergaming, in patients with chronic non-specific low back pain. A systematic review of clinical studies obtained from PubMed, Scopus, Science Direct, and Web of Science databases from 1 January 2016 to 1 July 2024 was conducted, developing the search string based on keywords and combinations of terms with Boolean AND/OR operators; on the retrieved articles were applied inclusion and exclusion criteria. The procedure of publication selection will be represented with the PRISMA diagram, the risk of bias through the RoB scale 2, and methodological validity with the PEDro scale. Eleven articles were included, all RCTs, and most of the publications use technology with exergaming within about 1-2 months. Of the outcomes measured, improvements were reported in pain, disability, and increased function; the neuropsychological sphere related to experiencing the pathology underwent improvements. From the results obtained, the efficacy of using technology based on exergames and inertial sensors, in patients with chronic non-specific low back pain, was increased. Further clinical studies are required to achieve more uniformity in the proposed treatment to create a common guideline for health care providers.

The Movement Deviation Profile Can Differentiate Faller and Non-Faller Older Adults

BACKGROUND

The World Health Organization considers falls the second leading cause of death by accidental injury worldwide and one of the most frequent complications in older adults during activities of daily living. Several tasks related to fall risk have been individually assessed describing kinematic changes in older adults. The study proposal was to identify which functional task differentiates faller and non-faller older adults using the movement deviation profile (MDP).

METHODS

This cross-sectional study recruited 68 older adults aged ≥60 years by convenience sampling. Older adults were divided into 2 groups: with and without a history of falls (34 older adults in each group). The MDP analyzed the 3-dimensional angular kinematics data of tasks (ie, gait, walking turn, stair ascent and descent, sit-to-stand, and stand-to-sit), and the Z score of the mean MDP identified which task presented the greatest difference between fallers and non-fallers. A multivariate analysis with Bonferroni post hoc verified the interaction between groups considering angular kinematic data and the cycle time of the task. Statistical significance was set at 5% (p < .05).

RESULTS

Z score of the MDPmean showed an interaction between groups (λ = 0.67, F = 5.085, p < .0001). Fallers differed significantly from non-fallers in all tasks and the greatest difference was in stair descent (Z score = 0.89). The time to complete each task was not different between groups.

CONCLUSIONS

The MDP distinguished older adult fallers from non-fallers. The stair descent task should be highlighted because it presented the greatest difference between groups.

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.

Kohonen Neural Network Investigation of the Effects of the Visual, Proprioceptive and Vestibular Systems to Balance in Young Healthy Adult Subjects

Kohonen neural network (KNN) was used to investigate the effects of the visual, proprioceptive and vestibular systems using the sway information in the mediolateral (ML) and anterior-posterior (AP) directions, obtained from an inertial measurement unit, placed at the lower backs of 23 healthy adult subjects (10 males, 13 females, mean (standard deviation) age: 24.5 (4.0) years, height: 173.6 (6.8) centimeter, weight: 72.7 (9.9) kg). The measurements were based on the modified Clinical Test of Sensory Interaction and Balance (mCTSIB). KNN clustered the subjects’ time-domain sway measures by processing their sway’s root mean square position, velocity, and acceleration. Clustering effectiveness was established using external performance indicators such as purity, precision-recall, and F-measure. Differences in these measures, from the clustering of each mCTSIB condition with its condition, were used to extract information about the balance-related sensory systems, where smaller values indicated reduced sway differences. The results for the parameters of purity, precision, recall, and F-measure were higher in the AP direction as compared to the ML direction by 7.12%, 11.64%, 7.12%, and 9.50% respectively, with their differences statistically significant (p < 0.05) thus suggesting the related sensory systems affect majorly the AP direction sway as compared to the ML direction sway. Sway differences in the ML direction were lowest in the presence of the visual system. It was concluded that the effect of the visual system on the balance can be examined mostly by the ML sway while the proprioceptive and vestibular systems can be examined mostly by the AP direction sway.

Validating Virtual Time to Contact With Home Based Technology in Young and Older Adults

Virtual time to contact (VTC) is a measure of postural stability that estimates the virtual time it would take to reach an individual's stability boundary. This study aimed to validate VTC as measured by a depth sensor, and to determine if VTC from the depth sensor distinguishes between older adult fallers and non-fallers compared to a force platform. VTC was assessed in 10 young and 20 older adults by having participants lean in a circular direction followed by five balance tests: eyes open, dual task, eyes open foam, eyes closed, and eyes closed foam. Spearman's correlations and Bland-Altman plots were conducted to determine validity, and Receiver Operating Curves were constructed to discriminate between fallers and non-fallers. Significant correlations were found in the dual task (p = 0.03), eyes open foam (p < 0.01), and eyes closed foam conditions (p = 0.05). The depth sensor discriminated between fallers and non-fallers in the eyes open (p = 0.02), dual task (p = 0.03), and eyes open foam conditions (p = 0.04). VTC was in agreement between the two devices, and VTC derived from a depth sensor and may be used to discriminate between older adult fallers and non-fallers during challenging balance conditions.

Proximal-to-Distal Sequencing and Coordination Variability in the Volleyball Spike of Elite Youth Players: Effects of Gender and Growth

The aim of this article was to examine changes in elite youth volleyball players’ performance, proximal-to-distal sequencing, and coordination variability of the spike motion between the start and after 1 year of a talent development program. Eight boys and eight girls in late puberty/early adolescence were measured with 3D motion capturing for 2 consecutive years. Performance and performance variability increased and decreased, respectively, but both changes were not significantly correlated with growth. Gender differences were identified for proximal-to-distal sequencing, but a very strong similarity between both years was observed for all seven degrees of freedom (pelvis and trunk rotation, trunk flexion, shoulder horizontal adduction, shoulder internal rotation, elbow extension, and wrist flexion). The fact that this sequence was kept stable, despite marked growth effects, likely indicates that this sequence is biomechanically efficient and the motor control systems try to preserve it. Coordination variability was analyzed by coordination profiling with self-organizing maps. The decrease in coordination variability correlated strongly and significantly with increase in body height. Participants with stronger growth rates were observed to show smaller decreases in coordination variability, which possibly represents a mechanism to explore various coordination patterns to adapt to the more rapidly changing organismic constraints.

Assessing Dynamic Balance Performance During Exergaming Based on Speed and Curvature of Body Movements

Improving balance performance among the elderly is of utmost importance because of the increasing number of injuries and fatalities caused by fall incidences. Digital games controlled by body movements (exergames) have been proposed as a way to improve balance among older people. However, the assessment of balance performance in real-time during exergaming remains a challenging task. This assessment could be used to provide instantaneous feedback and automatically adjust the exergame difficulty. Such features could potentially increase the motivation of the player, thus augmenting the effectiveness of exergames. As clear differences in balance performance have been identified between older and younger people, distinguishing between older and younger adults can help identifying measures of balance performance. We used generalized linear models to investigate whether the assessment of balance performance based on movement speed can be improved by incorporating curvature of the movement trajectory into the analysis. Indeed, our results indicated that curvature improves the performance of the models. Five-fold cross validation indicated that our method is promising for the assessment of balance performance in real-time by showing more than 90% classification accuracy. Finally, this method could be valuable not only for exergaming, but also for real-time assessment of body movements in sports, rehabilitation, and medicine.

Changes in balance coordination and transfer to an unlearned balance task after slackline training: a self-organizing map analysis

How humans maintain balance and change postural control due to age, injury, immobility or training is one of the basic questions in motor control. One of the problems in understanding postural control is the large set of degrees of freedom in the human motor system. Therefore, a self-organizing map (SOM), a type of artificial neural network, was used in the present study to extract and visualize information about high-dimensional balance strategies before and after a 6-week slackline training intervention. Thirteen subjects performed a flamingo and slackline balance task before and after the training while full body kinematics were measured. Range of motion, velocity and frequency of the center of mass and joint angles from the pelvis, trunk and lower leg (45 variables) were calculated and subsequently analyzed with an SOM. Subjects increased their standing time significantly on the flamingo (average +2.93 s, Cohen's d = 1.04) and slackline (+9.55 s, d = 3.28) tasks, but the effect size was more than three times larger in the slackline. The SOM analysis, followed by a k-means clustering and marginal homogeneity test, showed that the balance coordination pattern was significantly different between pre- and post-test for the slackline task only (χ ² = 82.247; p < 0.001). The shift in balance coordination on the slackline could be characterized by an increase in range of motion and a decrease in velocity and frequency in nearly all degrees of freedom simultaneously. The observation of low transfer of coordination strategies to the flamingo task adds further evidence for the task-specificity principle of balance training, meaning that slackline training alone will be insufficient to increase postural control in other challenging situations.

van de Gronde J, J. C. Lamoth C, van Diest M, M. Maurits N, B. T. M. Roerdink J. Visual Data Exploration for Balance Quantification in Real-Time During Exergaming

Unintentional injuries are among the ten leading causes of death in older adults; falls cause 60% of these deaths. Despite their effectiveness to improve balance and reduce the risk of falls, balance training programs have several drawbacks in practice, such as lack of engaging elements, boring exercises, and the effort and cost of travelling, ultimately resulting in low adherence. Exergames, that is, digital games controlled by body movements, have been proposed as an alternative to improve balance. One of the main challenges for exergames is to automatically quantify balance during game-play in order to adapt the game difficulty according to the skills of the player. Here we perform a multidimensional exploratory data analysis, using visualization techniques, to find useful measures for quantifying balance in real-time. First, we visualize exergaming data, derived from 400 force plate recordings of 40 participants from 20 to 79 years and 10 trials per participant, as heat maps and violin plots to get quick insight into the nature of the data. Second, we extract known and new features from the data, such as instantaneous speed, measures of dispersion, turbulence measures derived from speed, and curvature values. Finally, we analyze and visualize these features using several visualizations such as a heat map, overlapping violin plots, a parallel coordinate plot, a projection of the two first principal components, and a scatter plot matrix. Our visualizations and findings suggest that heat maps and violin plots can provide quick insight and directions for further data exploration. The most promising measures to quantify balance in real-time are speed, curvature and a turbulence measure, because these measures show age-related changes in balance performance. The next step is to apply the present techniques to data of whole body movements as recorded by devices such as Kinect.

Exergames for unsupervised balance training at home: A pilot study in healthy older adults

Exercise videogames (exergames) are gaining popularity as tools for improving balance ability in older adults, yet few exergames are suitable for home-based use. The purpose of the current pilot study was to examine the effects of a 6-week unsupervised home-based exergaming training program on balance performance. Ten community dwelling healthy older adults (age: 75.9 ± 7.2 years) played a newly developed ice skating exergame for six weeks at home. In the game, the speed and direction of a virtual ice skater on a frozen canal were controlled using lateral weight shifts, which were captured using Kinect. Sway characteristics during quiet standing in eyes open (EO), eyes closed (EC) and dual task (DT) conditions were assessed in time and frequency domain before, and after two, four and six weeks of training. Balance was also evaluated using the narrow ridge balance test (NRBT). Multilevel modeling was applied to examine changes in balance ability. Participants played 631 (± 124)min over the intervention period and no subjects dropped out. Balance in terms of sway characteristics improved on average by 17.4% (EO) and 23.3% (EC) after six weeks of training (p<0.05). Differences in rate of improvement (p<0.05) were observed between participants. No intervention effects were found for quiet standing in DT conditions and on the NRBT. In conclusion, the pilot study showed that unsupervised home-based exergaming is feasible in community dwelling older adults, but also that participants do not benefit equally from the program, thereby emphasizing the need for more personalized exergame training programs.