In this study, the novel mobile dynamometric platform, OREKA, was utilized to perform an extensive analysis of the centre of pressure behaviour during different tilt motion exercises. This platform ...is based on a parallel manipulator mechanism and can perform rotations around both horizontal axes and a vertical translation. A group of participants took part in an experimental campaign involving the completion of a set of exercises. The aim was to evaluate the platform's potential practical application and investigate the impact of visual on-screen feedback on centre of pressure motion through multiple balance indicators. The use of the OREKA platform enables the study of the impact on a user's balance control behaviour under different rotational perturbations, depending on the availability of real-time visual feedback on a screen. Furthermore, it presented data identifying postural control variations among clinically healthy individuals. These findings are fundamental to comprehending the dynamics of body balance. Further investigation is needed to explore these initial findings and fully unlock the potential of the OREKA platform for balance assessment methodologies.
Timed Up and Go (TUG) test is one of the most popular clinical tools aimed at the assessment of functional mobility and fall risk in older adults. The automation of the analysis of TUG movements is ...of great medical interest not only to speed up the test but also to maximize the information inferred from the subjects under study. In this context, this article describes a dataset collected from a cohort of 69 experimental subjects (including 30 adults over 60 years), during the execution of several repetitions of the TUG test. In particular, the dataset includes the measurements gathered with four wearables devices embedding four sensors (accelerometer, gyroscope magnetometer and barometer) located on four body locations (waist, wrist, ankle and chest). As a particularity, the dataset also includes the same measurements recorded when the young subjects repeat the test while wearing a commercial geriatric simulator, consisting of a set of weighted vests and other elements intended to replicate the limitations caused by aging. Thus, the generated dataset also enables the investigation into the potential of such tools to emulate the actual dynamics of older individuals.
Ageing incurs a natural decline of postural control which has been linked to an increased risk of falling. Accurate balance assessment is important in identifying postural instability and informing ...targeted interventions to prevent falls in older adults. Inertial sensor (IMU) technology offers a low-cost means for objective quantification of human movement. This paper describes two studies carried out to advance the use of IMU-based balance assessments in older adults. Study 1 (N = 39) presents the development of two new IMU-derived balance measures. Study 2 (N = 248) reports a reliability analysis of IMU postural stability measures and validates the novel balance measures through comparison with clinical scales. We also report a statistical fall risk estimation algorithm based on IMU data captured during static balance assessments alongside a method of improving this fall risk estimate by incorporating standard clinical fall risk factor data. Results suggest that both new balance measures are sensitive to balance deficits captured by the Berg Balance Scale (BBS) and Timed Up and Go test. Results obtained from the fall risk classifier models suggest they are more accurate (67.9%) at estimating fall risk status than a model based on BBS (59.2%). While the accuracies of the reported models are lower than others reported in the literature, the simplicity of the assessment makes it a potentially useful screening tool for balance impairments and falls risk. The algorithms presented in this paper may be suitable for implementation on a smartphone and could facilitate unsupervised assessment in the home.
Accurate assessment of balance recovery throughout treatment of a sport-related concussion is imperative. This study examined differences in balance from diagnosis to return-to-play initiation in ...adolescent patients post-concussion. Second, this study investigated the extent to which the Balance Error Scoring System (BESS) correlated with center-of-pressure (COP) measures.
Forty participants performed the BESS while standing on a force platform such that COP data were obtained simultaneously. Spatial and velocity COP-based measures were computed for the double-stance conditions.
BESS scores and COP-based measures indicated improved balance performance between visits. Specifically, 62.5/65.0% of participants exhibited improved firm/foam BESS final scores, respectively, and 56.4-71.8% exhibited improved COP-based measures. However, once normative ranges were referenced to identify maintained performance, the percentage of participants who substantially improved differed from initial findings (BESS: 2.5/7.5%, COP: 48.7-69.2%). Additionally, positive correlations between balance measures were primarily found at diagnosis (r=0.33-0.53), while only three correlations were maintained at return-to-play initiation (r=0.34-0.39).
BESS scores successfully identified poor balance performance at diagnosis when symptoms were most pronounced, but failed to accurately depict performance once balance impairment, indicated by COP-based measures, became less apparent. Further work is needed to implement more advanced balance assessments into clinical environments.
The aim of the study was to design an algorithm of selecting the balance assessment tool in patients after stroke, which could be used in a subacute rehabilitation setting. A retrospective study was ...carried out to analyse results of standardized balance measurements in three groups of stroke patients classified by Functional Ambulation Category (FAC) (FAC 1 or 2, non-functional ambulation; FAC 3 or 4, ambulatory dependent; FAC 5 or 6, ambulatory independent). Balance functions were evaluated in 62 out of 70 patients (88.6%) at admission and discharge with at least with one standardized assessment tool. In 21 patients (30%), two or more assessment tools were used. From admission to discharge significant changes in balance functions in the non-functional ambulatory group were detected by Postural Assessment Scale for Stroke (PASS) (P = 0.003), in the ambulatory dependent group with PASS (P = 0.025) and Berg Balance Scale (BBS) (P = 0.009) and in the ambulatory independent group with the Timed Up and Go Test (P = 0.002) and Functional Gait Assessment (P = 0.029). In a post-stroke rehabilitation most commonly used BBS and PASS are sensitive enough in non-functional ambulatory and ambulatory dependent patients, though they do not reflect the overall balance function. In ambulatory independent patients, significant changes in balance functions can be detected only with the assessment tools that include the measurements of dynamic balance. Based on the findings, the algorithm for the selection of balance assessment tools in post-stroke rehabilitation setting was formulated according to FAC.
Objective: Postural control naturally declines with age, leading to an increased risk of falling. Within clinical settings, the deployment of balance assessments has become commonplace, facilitating ...the identification of postural instability and targeted interventions to forestall falls among older adults. Some studies have ventured beyond the controlled laboratory, leaving, however, a gap in our understanding of balance in real-world scenarios. Methods: Previously reported algorithms were used to build a finite-state machine (FSM) with four states: walking, turning, sitting, and standing. The FSM was validated against video annotations (gold standard) in an independent dataset with data collected on 20 older adults. Later, the FSM was applied to data from 168 community-dwelling older people in the InCHIANTI cohort who were evaluated both in the laboratory and then remotely in real-world conditions for a week. A 70/30 data split with recursive feature selection and resampling techniques was used to train and test four machine-learning models. Results: In identifying fallers, duration, distance, and mean frequency computed during standing in real-world settings revealed significant relationships with fall risk. Also, the best-performing model (Lasso Regression) built on real-world balance features had a higher area under the curve (AUC, 0.76) than one built on lab-based assessments (0.57). Conclusion: Real-world balance features differ considerably from laboratory balance assessments (Romberg test) and have a higher predictive capacity for identifying patients at high risk of falling. Significance: These findings highlight the need to move beyond traditional laboratory-based balance measures and develop more sensitive and accurate methods for predicting falls.
Virtual reality (VR) is a well-established technology in medicine. Head-mounted displays (HMDs) have made VR more accessible in many branches of medical research. However, its application in balance ...evaluation has been vague, and comprehensive literature on possible applications of VR in posture measurement is scarce. The aim of this review is to conduct a literature search on the application of immersive VR delivered using a head-mounted display in posturographic measurements. A systematic search of two databases, PubMed and Scopus, using the keywords “virtual reality” and “posturography,” was performed following PRISMA guidelines for systematic reviews. Initial search results returned 89 non-duplicate records. Two reviewers independently screened the abstracts. Sixteen papers fulfilled the inclusion criteria and none of the exclusion criteria and were selected for complete text retrieval. An additional 16 records were identified from citation searching. Ultimately, 21 studies were included in this review. virtual reality is often used as additional visual stimuli in static and dynamic posturography evaluation. Only one study has attempted to evaluate a VR environment in a head-mounted display as an independent method in the assessment of posture. Further research should be conducted to assess HMD VR as a standalone posturography replacement.
•A robotic device with applications in balance assessment was designed and fabricated.•It provides controlled mechanical perturbations with appropriate dynamics for balance assessment.•The ...functionality of the device was evaluated.•The sensitivity of the CoP indices increases when the mechanical perturbations are present.
Balance impairment is critical for many patient groups such as those with neural and musculoskeletal disorders and also the elderly. Accurate and objective assessment of balance performance has led to the development of several indices based on the measurement of the center of pressure. In this study, a robotic device was designed and fabricated to provide controlled and repeatable mechanical perturbations to the standing platform of the user. The device uses servo-controlled actuators and two parallel mechanisms to provide independent rotations in mediolateral and anterior-posterior directions. The device also provides visual feedback of the center of pressure position to the user. Functional tests were run and showed that the device is able to provide an appropriate dynamics (time constant of 0.19 s and bandwidth of 0.85 Hz) for the two motions. The efficacy of the device on the balance assessment was then evaluated experimentally. Ten healthy subjects performed a balance task with and without perturbations and seven center of pressure indices were measured. It was shown that the sensitivity of the indices to the user's performance was statistically increased in all indices particularly in anterior/posterior direction when the mechanical perturbations were present.
In this study, a wearable inertial measurement unit system was introduced to assess patients via the Berg balance scale (BBS), a clinical test for balance assessment. For this purpose, an automatic ...scoring algorithm was developed. The principal aim of this study is to improve the performance of the machine-learning-based method by introducing a deep-learning algorithm. A one-dimensional (1D) convolutional neural network (CNN) and a gated recurrent unit (GRU) that shows good performance in multivariate time-series data were used as model components to find the optimal ensemble model. Various structures were tested, and a stacking ensemble model with a simple meta-learner after two 1D-CNN heads and one GRU head showed the best performance. Additionally, model performance was enhanced by improving the dataset via preprocessing. The data were down sampled, an appropriate sampling rate was found, and the training and evaluation times of the model were improved. Using an augmentation process, the data imbalance problem was solved, and model accuracy was improved. The maximum accuracy of 14 BBS tasks using the model was 98.4%, which is superior to the results of previous studies.
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