Instantaneous Segmental Energy Symmetry Index as Gait Compensation Indicator in Asymmetrical Walking

Purpose: Human body constantly adapts to optimise the energy expenditure. A better understanding of the mechanical energetic costs in lower extremities helps identify the compensatory mechanism adopted in asymmetrical gait. This paper proposes the use of instantaneous segmental energy and normalised symmetry index (SInorm) to examine asymmetrical gait. This approach can provide better overview of gait quality allowing identification of change in segmental energy during different gait phases and contribution of each segment in compensating abnormal walking. Method: An experimental study was carried out to validate this method. Twenty healthy subjects were recruited. Asymmetrical gait was simulated by restricting knee motion during walking using a knee brace. Mechanical energy was determined for each segment of the left and right limbs. Normalised Symmetry Index (SInorm) was then calculated to examine bilateral differences in segmental energy during stance phase and swing phase. Statistical analysis using ANOVA and Tukey-Kramer multiple comparison test to identify asymmetry of the segmental energy (p-value < 0.05). Result: Significant asymmetry of segmental energy occurred during swing phase. Greater asymmetry was observed in kinetic energy than in potential energy. The affected limb segments produced lower kinetic energy than the normal limb. At asymmetrical state, potential energy of the affected limb’s foot and thigh were lower than that of the normal segments while the inverse was true for thigh segment. Conclusion: These results suggested that in asymmetrical gait, a form of compensatory mechanism is adopted to walk. This can be observed in the change of instantaneous segmental energy during walking

Data for: Artificial Neural Network Based Ankle Joint Angle Estimation Using Instrumented Foot Insoles

The shared data consists of Ground Reaction Forces (GRF) and Ankle Angles used in this work. GRFs are in Newtons (N) and Ankle angles are in degrees (°). The GRFs are shared as GRF left and GRF right denoting the left and right gait cycles (GRF left, GRF right). Similarly, ankle angles are shared as ankle angle left and ankle angle right (ankle angle left, ankle angle right). There are 10 samples of data for each data type (ankle angle left, ankle angle right, GRF left, GRF right). Each sample denote data from stance gait cycle. In the shared excel file, data is organised in 4 sheets. Each sheet denotes the below;Sheet 1 (ankle_angle_left)- consists of 10 samples of ankle angle extracted from left legSheet 2 (ankle_angle_right)- consists of 10 samples of ankle angle extracted from right legSheet 3 (GRF_left)- consists of 10 samples of GRFs extracted from left legSheet 4 (GRF_right)- consists of 10 samples of GRFs extracted from right legWithin each sheet, column 1 to 10 denotes data from Sample 1 to Sample 10 respectively

Data for: Artificial Neural Network Based Ankle Joint Angle Estimation Using Instrumented Foot Insoles

The shared data consists of Ground Reaction Forces (GRF) and Ankle Angles used in this work. GRFs are in Newtons (N) and Ankle angles are in degrees (°). The GRFs are shared as GRF left and GRF right denoting the left and right gait cycles (GRF left, GRF right). Similarly, ankle angles are shared as ankle angle left and ankle angle right (ankle angle left, ankle angle right). There are 10 samples of data for each data type (ankle angle left, ankle angle right, GRF left, GRF right). Each sample denote data from stance gait cycle. In the shared excel file, data is organised in 4 sheets. Each sheet denotes the below;Sheet 1 (ankle_angle_left)- consists of 10 samples of ankle angle extracted from left legSheet 2 (ankle_angle_right)- consists of 10 samples of ankle angle extracted from right legSheet 3 (GRF_left)- consists of 10 samples of GRFs extracted from left legSheet 4 (GRF_right)- consists of 10 samples of GRFs extracted from right legWithin each sheet, column 1 to 10 denotes data from Sample 1 to Sample 10 respectively.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

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

Malaysia traffic sign recognition with convolutional neural network

Traffic sign recognition system is an important subsystem in advanced driver assistance systems (ADAS) that assisting a driver to detect a critical driving scenario and subsequently making an immediate decision. Recently, deep architecture neural network is popular because it adapts well in various kind of scenarios, even those which were not used during training. Therefore, a deep architecture neural network is implemented to perform traffic sign classification in order to improve the traffic sign recognition rate. A comparative study for a deep and shallow architecture neural network is presented in this paper. Deep and shallow architecture neural network refer to convolutional neural network (CNN) and radial basis function neural network (RBFNN) respectively. In the simulation result, two types of training modes had been compared i.e. incremental training and batch training. Experimental results show that incremental training mode trains faster than batch training mode. The performance of the convolutional neural network is evaluated with the Malaysian traffic sign database and achieves 99% of the recognition rate