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Wearable sensors are de facto revolutionizing the assessment of standing balance. The aim of this work is to review the state-of-the-art literature that adopts this new posturographic paradigm, i.e., to analyse human postural sway through inertial sensors directly worn on the subject body. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 73 full-text articles, selecting 47 high-quality contributions. A good inter-rater reliability was obtained (Cohen’s kappa = 0.79). This selection of papers was used to summarize the available knowledge on the types of sensors used and their positioning, the data acquisition protocols and the main applications in this field (e.g., “active aging”, biofeedback-based rehabilitation for fall prevention, and the management of Parkinson’s disease and other balance-related pathologies), as well as the most adopted outcome measures. A critical discussion on the validation of wearable systems against gold standards is also presented.

Maintaining a stable upright posture is essential for performing activities of daily living, and impaired standing balance may impact an individual’s quality of life. Therefore, accurate and sensitive methods for assessing static balance are crucial for identifying balance impairments, understanding the underlying mechanisms of the balance deficiencies, and developing targeted interventions to improve standing balance and prevent falls. This review paper first explores the methods to quantify standing balance. Then, it reviews traditional posturography and recent advancements in using wearable inertial measurement units (IMUs) to assess static balance in two populations: older adults and those with incomplete spinal cord injury (iSCI). The inclusion of these two groups is supported by their large representation among individuals with balance impairments. Also, each group exhibits distinct aspects in balance assessment due to diverse underlying causes associated with aging and neurological impairment. Given the high vulnerability of both demographics to balance impairments and falls, the significance of targeted interventions to improve standing balance and mitigate fall risk becomes apparent. Overall, this review highlights the importance of static balance assessment and the potential of emerging methods and technologies to improve our understanding of postural control in different populations.

Background: Objective assessment of postural control is central to the clinical evaluation of vestibular disorders. Although force-platform-based posturography is considered the gold standard, its use may be limited by cost and infrastructural requirements. Wearable inertial measurement units (IMUs) represent a promising alternative; however, their clinical validation should account for intrinsic differences in measurement paradigms rather than strict metric equivalence. Objective: To preliminarily evaluate the within-session reliability of a wearable IMU-based medical device for balance assessment (Gravity), and its agreement with established static (SBP) and computerised dynamic posturographic systems (CDP) in healthy subjects. Methods: Sixty-three healthy adults were enrolled in two independent method comparison studies: a wearable IMU-based balance system versus a static stabilometric platform (GRAVITY vs. SVEP; n = 42) and a wearable IMU-based balance system versus computerised dynamic posturography (Gravity vs. EquiTest; n = 21). Gravity measurements were obtained simultaneously with reference systems across standardised sensory conditions. Within-session reliability and method agreement were assessed. Results: Within-session reliability of Gravity was outcome-dependent. Length-based components demonstrated higher repeatability (ICC (single) = 0.25–0.35; ICC (average) = 0.41–0.52), with narrower limits of agreement (LoA = ±9–12%) and lower measurement error (SEM = 3.3–4.3%). In comparison with SBP, length-based measures exhibited narrower limits (LoA = ±12–17) and more consistent relationships. Comparison with CDP revealed moderate agreement for composite and preferential scores (LoA: −2.20–7.07; −5.54–8.12). Conclusions: Gravity sensor may represent a clinically meaningful, outcome-dependent performance, with superior reliability and comparability for length-based postural measures compared with area-based measures. The device could provide balance assessments compatible with both static and dynamic posturographic systems, accounting for physiological variability. These findings support the potential clinical use of wearable IMU-based posturography, particularly in settings where conventional force-platform systems are not readily available, and warrant further validation in larger, more clinically diverse populations.

Purpose Balance performance can be measured with a computerized dynamic posturography (CDP). Frequently used CDP protocols include the sensory organization test (SOT), motor control test (MCT) and adaptation test (ADT). The primary aim of this study was to produce normative data using the Bertec ® Balance Advantage CDP on a cohort of healthy adults aged 20–79 years for the SOT, MCT and ADT. The secondary aim was to determine age- and gender-related differences. Methods The study population of 120 participants were divided into age groups of 20–29, 30–39 years, etc., with an equal number of men and women in each group. Using the Bertec ® CDP, the SOT, MCT and ADT protocols were performed. Results SOT found that the 70–79 age group and men scored lower equilibrium scores (ES) on SOT4, and SOT6. Women had higher ES on SOT1. For the MCT, backward translation . latencies were higher for the 60 s and 70 s age groups. The 60 s age group had higher forward translation latencies. Women had lower latencies on all MCTs. For the ADT toes up test, the 70–79 age group scored higher sway energy. On the toes down test, higher sway energy was registered for the 50–79 age groups. Women had lower sway energy for both ADT’s. Conclusion We have produced normative data for healthy adults aged 20–79 years for the SOT, MCT and ADT protocols using the Bertec® Balance Advantage CDP. Balance performance differences might be due to decline in visual acuity, presbyvestibulopathy and differences in body composition.

Purpose Computerized posturography is the gold standard for balance assessment. Because of the great cost and dimensions of commercial equipments, low-cost and portable devices have been developed and validated, such as RombergLab, a software in open source term which works connected with a low-cost force platform. The objective of this study was to obtain normative posturography data using this software. Methods A multicentric prospective and descriptive study, with 350 healthy participants, was designed. Static postural stability (measured using the modified clinical test of sensory interaction on balance) was evaluated using the software connected to the force platform. Using the confidence ellipse area (CEA) in each condition, global equilibrium score (GES) was calculated and adjusted for significant variable factors using cluster analysis. Results Mean (SD) GES was 0.72 (0.22). Age ( p  < 0.01), height ( p  < 0.01) and recruitment center ( p  < 0.05) were found as influence factors for GES. Cluster analysis obtained 16 groups stratified by age and height. GES decreases with age and height ( p  < 0.005). No significant interaction of age nor height was found with GES in these clusters ( p  > 0.05). After correction for height and age, GES was no longer influenced by the recruitment center ( p  > 0.05). Conclusions With the introduction of the global equilibrium score values of the present study into the software, we consider RombergLab v1.3 a reference posturography tool for healthy individuals. Further studies are needed for validating it as a suitable instrumented test for screening between healthy and pathologic subjects and its reliability over time for the follow-up of patients.

Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined.

Background Approximate entropy (ApEn) and sample entropy (SampEn) have been previously used to quantify the regularity in centre of pressure (COP) time-series in different experimental groups and/or conditions. ApEn and SampEn are very sensitive to their input parameters: m (subseries length), r (tolerance) and N (data length). Yet, the effects of changing those parameters have been scarcely investigated in the analysis of COP time-series. This study aimed to investigate the effects of changing parameters m , r and N on ApEn and SampEn values in COP time-series, as well as the ability of these entropy measures to discriminate between groups. Methods A public dataset of COP time-series was used. ApEn and SampEn were calculated for m  = {2, 3, 4, 5}, r  = {0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5} and N  = {600, 1200} (30 and 60 s, respectively). Subjects were stratified in young adults (age < 60, n  = 85), and older adults (age ≥ 60) with ( n  = 18) and without ( n  = 56) falls in the last year. The effects of changing parameters m , r and N on ApEn and SampEn were investigated with a three-way ANOVA. The ability of ApEn and SampEn to discriminate between groups was investigated with a mixed ANOVA (within-subject factors: m , r and N ; between-subject factor: group). Specific combinations of m , r and N producing significant differences between groups were identified using the Tukey’s honest significant difference procedure. Results A significant three-way interaction between m , r and N confirmed the sensitivity of ApEn and SampEn to the input parameters. SampEn showed a higher consistency and ability to discriminate between groups than ApEn. Significant differences between groups were mostly observed in longer ( N  = 1200) COP time-series in the anterior-posterior direction. Those differences were observed for specific combinations of m and r , highlighting the importance of an adequate selection of input parameters. Conclusions Future studies should favour SampEn over ApEn and longer time-series (≥ 60 s) over shorter ones (e.g. 30 s). The use of parameter combinations such as SampEn (m = {4, 5}, r = {0.25, 0.3, 0.35}) is recommended.

ObjectivesThis study aimed to compare lower-limb muscle strength and postural sway between individuals with KOA and healthy controls, and to examine the association between strength parameters and postural stability using clinically feasible assessment methods.MethodsA cross-sectional analysis was conducted among 90 participants (45 KOA, 45 matched controls). The participants were older adults aged 50–75 years, and 40.0% of the KOA group and 42.22% of the control group were male. Participants with knee osteoarthritis were classified as having moderate disease severity based on Kellgren–Lawrence grades II–III. Strength and balance assessments were conducted in a counterbalanced order, with a standardized 10-min rest interval between testing sessions to minimize fatigue effects. Isometric quadriceps and hamstring strength were measured using handheld dynamometry. Postural sway metrics—sway area, sway velocity, and single-leg stance time—were assessed via static posturography. Limb symmetry index (LSI) and quadriceps-to-hamstrings (Q: H) ratio were calculated. Pearson correlation and multiple linear regression analyses examined associations between strength and postural control.ResultsKOA participants showed significantly greater sway area and velocity, reduced stance time, and lower quadriceps and hamstrings strength compared to controls (all p < 0.001). Quadriceps strength (β = −0.024, p < 0.001) and LSI (β = −0.062, p = 0.001) were independent predictors of sway area under eyes-closed conditions. LSI and quadriceps strength were strongly correlated with sway parameters and stance performance.ConclusionLower limb strength deficits and inter-limb asymmetry significantly contribute to postural instability in individuals with KOA. Objective, clinically feasible tools such as handheld dynamometry and posturography can support evaluation and inform rehabilitation strategies targeting strength and balance.

BackgroundVestibular rehabilitation (VR), specifically, VR with dynamic computerized posturography (CDP) has proven to be useful to improve balance and reduce the risk of falling in old patients. Its major handicap is probably its cost, which has hindered its generalisation. One solution to reduce this cost is performing VR with mobile posturography systems, which allow assessment of stability at the center of body mass in daily-life conditions. Also, rehabilitation with vibrotactile neurofeedback training could be used in dynamic tasks.ObjectiveTo assess whether two different protocols of vestibular rehabilitation (using CDP and the Vertiguard system) show significant differences in the improvement of balance among older persons with imbalanceMethodsA clinical trial comparing VR with CDP exercises and VR with mobile posturography (Vertiguard) exercises, was designed. The participants were people over 65 years, with imbalance. The composite (average balance) in the sensory organization test (SOT) of the CDP was the main outcome measure; it was compared before and 3 weeks after VR, and between both intervention groups.Results40 patients were included in the study (19 in the CDP-VR group and 21 in the Vertiguard-VR group). Average balance was significantly improved in both intervention groups (51% pre-VR vs 60% post-VR, p = 0.002, CDP-VR group; 49% pre-VR vs 57% post-VR, p = 0.008, Vertiguard-VR group); no significant differences in this improvement were found comparing both groups (p = 0.580).Discussion and conclusionsVR using mobile posturography is useful to improve stability in old people with instability, showing similar improvement rates to those of VR using CDP.Unique identifierNCT03034655 www.clinicaltrials.gov Registered on 25 January 2017.

Dynamic posturography combined with wearable sensors has high sensitivity in recognizing subclinical balance abnormalities in patients with Parkinson’s disease (PD). However, this approach is burdened by a high analytical load for motion analysis, potentially limiting a routine application in clinical practice. In this study, we used machine learning to distinguish PD patients from controls, as well as patients under and not under dopaminergic therapy (i.e., ON and OFF states), based on kinematic measures recorded during dynamic posturography through portable sensors. We compared 52 different classifiers derived from Decision Tree, K-Nearest Neighbor, Support Vector Machine and Artificial Neural Network with different kernel functions to automatically analyze reactive postural responses to yaw perturbations recorded through IMUs in 20 PD patients and 15 healthy subjects. To identify the most efficient machine learning algorithm, we applied three threshold-based selection criteria (i.e., accuracy, recall and precision) and one evaluation criterion (i.e., goodness index). Twenty-one out of 52 classifiers passed the three selection criteria based on a threshold of 80%. Among these, only nine classifiers were considered “optimum” in distinguishing PD patients from healthy subjects according to a goodness index ≤ 0.25. The Fine K-Nearest Neighbor was the best-performing algorithm in the automatic classification of PD patients and healthy subjects, irrespective of therapeutic condition. By contrast, none of the classifiers passed the three threshold-based selection criteria in the comparison of patients in ON and OFF states. Overall, machine learning is a suitable solution for the early identification of balance disorders in PD through the automatic analysis of kinematic data from dynamic posturography.