Negative biofeedback for enhancing proprioception training on wobble boards

Biofeedback has been identified to improve postural control and stability.A biofeedback system communicates with the humans’ Central Nervous Systemthrough many available modalities, such as vibrotactile. The vibrotactile nature offeedback is presented in a simple and realistic manner, making the presentationof signals safe and easy to decipher. This work presents a wobble board training routine for rehabilitation combined with real-time biofeedback. The biofeedback was stimulated using a fuzzy inference system. The fuzzy system had two inputs and one output.Measurements to test this rehabilitation approach was taken in Eyes Open and Eyes Close states, with and without biofeedback while subjects stood on the wobble board. An independent T-test was conducted on the readings obtained to test for statistical significance. The goal of this work was to determine the feasibilityof implementing a negative close-loop biofeedback system to assist in proprioceptortraining utilizing wobble boards

Force sensing resistors for monitoring proprioception response in rehabilitation routines

During rehabilitation routines for postural control, clinician use proprioception training involving wobble boards to help strengthen the proprioception. Wobble board routines are carried out for at least six weeks, subjects are required to perform certain motions on the boards which are targeted to improve proprioception. Subjects perform this tasks without (or with minimal) real-time feedback. A real-time system to monitor proprioception training, using a wobble board, was designed and tested. This work presents a force sensing platform, equipped with soft-computing methods to measure effects of destabilizing postural perturbations. Experiments were conducted to verify the system's ability to monitor and gauge subject's postural control via proprioception. The experimental set-up was observed at a frequency of three times a week for a duration of six weeks. Fuzzy clustering and area of sway analysis was used to determine the effects of training on subjects' postural control in Eyes Open (EO) and Eyes Close (EC) conditions. All data was tabulated and compared using one-way ANOVA to determine its statistical significance, with a false rejection ratio α = 0.05. The results of the experiment supported the suitability of the system for clinical applications pertaining to postural control improvements

Assistive vibrotactile biofeedback system for postural control on perturbed surface.

Postural control is an important aspect of human locomotion and stance. When inputs to theCentral Nervous System (CNS), consisting of the vestibular, somatosensory, and visual senses,degrade or become dysfunctional, the postural control is a®ected. Biofeedback has beenestablished as a potential intervention method to assist individuals improve postural control, byaugmenting or complementing signals to the CNS. This paper presents an approach to helpachieve better postural control using vibrotactile biofeedback. Tests to monitor postural con-trol, in eyes open and eyes closed states, on a wobble board were introduced to assess theviability of the designed system in providing accurate real-time biofeedback responses. Posturalcontrol was gauged by measuring the angular displacement of perturbations experienced.Perturbations along the anterior and posterior direction are used to determine the level ofprovided vibrotactile biofeedback. The feedback informs subjects the severity of perturbationand direction of imbalance. Signi¯cant improvement (p-value < 0:05) in postural control whileon perturbed surface was detected when the designed biofeedback system was used. Thewearable system was found to be e®ective in improving postural control of the subjects and canbe expanded for rehabilitation, conditioning, and strengthening applications dealing withhuman postural control

Determining level of postural control in young adults using force-sensing resistors

A force-sensing platform (FSP), sensitive to changesof the postural control system was designed. The platform measuredeffects of postural perturbations in static and dynamic conditions.This paper describes the implementation of an FSP usingforce-sensing resistors as sensing elements. Real-time qualitativeassessment utilized a rainbow color scale to identify areas withhigh force concentration. Postprocessing of the logged data providedend-users with quantitative measures of postural control.The objective of this research was to establish the feasibility of usingan FSP to test and gauge human postural control. Tests wereconducted in eye open and eye close states. Readings obtained weretested for repeatability using a one-way analysis of variance test.The platform gauged postural sway by measuring the area of distributionfor the weighted center of applied pressure at the foot.A fuzzy clustering algorithm was applied to identify regions of thefoot with repetitive pressure concentration. Potential applicationof the platform in a clinical setting includes monitoring rehabilitationprogress of stability dysfunction. The platform functions as aqualitative tool for initial, on-the-spot assessment, and quantitativemeasure for postacquisition assessment on balance abilities

Real-time stability measurement system for postural control

A method for assessing balance, which was sensitive to changes in the posturalcontrol system is presented. This paper describes the implementation of a force-sensing platform,with force sensing resistors as the sensing element. The platform is capable of measuringdestabilized postural perturbations in dynamic and static postural conditions. Besidesproviding real-time qualitative assessment, the platform quantifies the postural control ofthe subjects. This is done by evaluating the weighted center of applied pressure distributionover time. The objective of this research was to establish the feasibility of using the forcesensingplatform to test and gauge the postural control of individuals. Tests were conductedin Eye Open and Eye Close states on Flat Ground (static condition) and the balance trainer(dynamic condition). It was observed that the designed platform was able to gauge the swayexperienced by the body when subject’s states and conditions changed

A neural model for processor-throughput using hardware parameters and software's dynamic behavior

Design space exploration of a processor system, prior to its hardware implementation, usually involves cycle-accurate simulations. The simulations provide a good measure of performance but require long periods of time even when a small set of design variations are assessed. An alternative is to use empirically-developed models which are much faster than actual simulations. In this paper, we have proposed an NN model for processor performance (IPC) prediction. The model uses a larger set of input parameters (especially the software parameters) than the prior models. For dimension reduction, we found PCA to be a more useful technique than correlation and graphical analysis. For the purpose of training the NNs, we used the data from a large number of simulations of industry-standard SPEC CPU 2000 and SPEC CPU 2006 benchmark suites In order to collect the NN training data in a reasonable period of time, we utilized two well-known techniques, namely, benchmark-subsetting and SPs