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

Computerised accelerometric machine learning techniques and statistical developments for human balance analysis

Balance maintenance is crucial to participating in the activities of daily life. Balance is often considered as the ability to maintain the centre of mass (COM) position within the base of support. Primarily, to maintain balance, reliance is placed on the balance related sensory systems i.e., the visual, proprioceptive and vestibular. Several factors can affect a person’s balance such as neurological diseases, ageing, medication and obesity etc. To gain insight into the balance operations, studies rely on statistical and machine learning techniques. Statistical techniques are used for inferencing while machine learning techniques proved effective for interpretation. The focus of this study was on the issues encountered in human balance analysis such as the quantification of balance by relevant features, the relationships between COM and ground projected body sway, the performance of various sensory systems in balance analysis, and their relationships between the directions of body sway (i.e., mediolateral (ML) and anteriorposterior (AP)). A portable wireless accelerometry device was developed, balance analysis methods based on the inverted pendulum were devised and evaluated for their accuracy and reliability against a setup designed to allow manual balance measurements. Balance data were collected from 23 healthy adult subjects with the mean (standard deviation) of the age, height and weight: 24.5 (4.0) years, 173.6 (6.8) cm, and 72.7 (9.9) kg respectively. The accelerometry device was attached to the subjects at the approximate position of the illac crest, while they performed 30 seconds trials of the four conditions associated with a standard balance test called the modified Clinical Test of Sensory Interaction and Balance (mCTSIB). These required standing on a hard (ground) surface with the eyes open, standing on hard surface with the eyes closed, standing on a compliant surface (sponge, 10 cm thick) with the eyes open and standing on a compliant surface with the eyes closed. Statistical and machine learning techniques such as t-test, Wilcoxon signed-rank test, the Mann-Whitney U test, Analysis of variance (ANOVA), Kruskal-Wallis test, Friedman test, correlation analysis, linear regression, Bland and Altman analysis, principal component analysis (PCA), K-means clustering, and Kohonen neural network (KNN) were employed for interpreting the measurements. The findings showed close agreement between the developed balance analysis methods and the related measurements from the manual setup for balance analysis. The COM was observed to be responsible for differing amount of sway across the subjects and could affect both the angle and ground projected sway. The AP direction was more sensitive to sway than the ML direction. The subjects were observed to depend more on their proprioceptive system to control balance. The proprioceptive system was observed to have a greater impact in controlling the AP velocity of the subjects as compared to their visual system. The proprioceptive system had no impact on the ML velocity. The visual system was responsible for the control of the ML velocity and for reducing the acceleration in both directions. It was concluded that for comparison of postural sway information, subjects with closely related COM positions should be compared, comparison should be carried out in respect to the base of their support. The sway normalisation by dividing with COM position should be performed to reduce the obscuring effect of the COM. Enhancement of the proprioceptive system should be carried out to reduce the AP velocity while enhancement of the visual system should be used to reduce the ML sway and acceleration in ML and AP directions. The velocity in the AP direction should be used to examine the performance of the proprioceptive system while the ML velocity and acceleration should be used for the visual system. The vestibular system characterised sway more in the AP direction, and hence, the AP direction should be used to examine its performance in balance