A real-time gait monitoring system that incorporates an immediate and periodical assessment of gait asymmetry is described. This system was designed for gait analysis and rehabilitation of patients with pathologic gait. It employs wireless gyroscopes to measure the angular rate of the thigh and shank in real time. Cross-correlation of the lower extremity (Cc(norm)), and normalized Symmetry Index (SI(norm)) are implemented as new approaches to periodically determine the gait asymmetry in each gait cycle. Cc(norm) evaluates the signal patterns measured by wireless gyroscopes in each gait cycle. SI(norm) determines the movement differences between the left and right limb. An experimental study was conducted to examine the viability of these methods. Artificial asymmetrical gait was simulated by placing a load on one side of the limbs. Results showed that there were significant differences between the normal gait and asymmetrical gait (p < 0.01). They also indicated that the system worked well in periodically assessing the gait asymmetry.
Injury to a lower limb may disrupt natural walking and cause asymmetrical gait, therefore assessing the gait asymmetry has become one of the important procedures in gait analysis. This paper proposes the use of wireless gyroscopes as a new instrument to determine gait asymmetry. It also introduces two novel approaches: normalized cross-correlations (Cc(norm)) and Normalized Symmetry Index (SI(norm)). Cc(norm) evaluates the waveform patterns generated by the lower limb in each gait cycle. SI(norm) provides indications on the timing and magnitude of the bilateral differences between the limbs while addressing the drawbacks of the conventional methods. One-way ANOVA test reveals that Cc(norm) can be considered as single value indicator that determines the gait asymmetry (p<0.01). The experiment results showed that SI(norm) in asymmetrical gait were different from normal gait. SI(norm) in asymmetrical gait were found to be approximately 20% greater than SI(norm) in normal gait during pre-swing and initial swing.
In this paper, a gait event detection algorithm is presented that uses computer intelligence (fuzzy logic) to identify seven gait phases in walking gait. Two inertial measurement units and four force-sensitive resistors were used to obtain knee angle and foot pressure patterns, respectively. Fuzzy logic is used to address the complexity in distinguishing gait phases based on discrete events. A novel application of the seven-dimensional vector analysis method to estimate the amount of abnormalities detected was also investigated based on the two gait parameters. Experiments were carried out to validate the application of the two proposed algorithms to provide accurate feedback in rehabilitation. The algorithm responses were tested for two cases, normal and abnormal gait. The large amount of data required for reliable gait-phase detection necessitate the utilisation of computer methods to store and manage the data. Therefore, a database management system and an interactive graphical user interface were developed for the utilisation of the overall system in a clinical environment.
An intelligent gait-phase detection algorithm based on kinematic and kinetic parameters is presented in this paper. The gait parameters do not vary distinctly for each gait phase; therefore, it is complex to differentiate gait phases with respect to a threshold value. To overcome this intricacy, the concept of fuzzy logic was applied to detect gait phases with respect to fuzzy membership values. A real-time data-acquisition system was developed consisting of four force-sensitive resistors and two inertial sensors to obtain foot-pressure patterns and knee flexion/extension angle, respectively. The detected gait phases could be further analyzed to identify abnormality occurrences, and hence, is applicable to determine accurate timing for feedback. The large amount of data required for quality gait analysis necessitates the utilization of information technology to store, manage, and extract required information. Therefore, a software application was developed for real-time acquisition of sensor data, data processing, database management, and a user-friendly graphical-user interface as a tool to simplify the task of clinicians. The experiments carried out to validate the proposed system are presented along with the results analysis for normal and pathological walking patterns.
A method for assessing balance, which was sensitive to changes in the postural control 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 measuring destabilized postural perturbations in dynamic and static postural conditions. Besides providing real-time qualitative assessment, the platform quantifies the postural control of the subjects. This is done by evaluating the weighted center of applied pressure distribution over time. The objective of this research was to establish the feasibility of using the force-sensing platform to test and gauge the postural control of individuals. Tests were conducted in 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 sway experienced by the body when subject's states and conditions changed.
A force-sensing platform (FSP), sensitive to changes of the postural control system was designed. The platform measured effects of postural perturbations in static and dynamic conditions. This paper describes the implementation of an FSP using force-sensing resistors as sensing elements. Real-time qualitative assessment utilized a rainbow color scale to identify areas with high force concentration. Postprocessing of the logged data provided end-users with quantitative measures of postural control. The objective of this research was to establish the feasibility of using an FSP to test and gauge human postural control. Tests were conducted in eye open and eye close states. Readings obtained were tested for repeatability using a one-way analysis of variance test. The platform gauged postural sway by measuring the area of distribution for the weighted center of applied pressure at the foot. A fuzzy clustering algorithm was applied to identify regions of the foot with repetitive pressure concentration. Potential application of the platform in a clinical setting includes monitoring rehabilitation progress of stability dysfunction. The platform functions as a qualitative tool for initial, on-the-spot assessment, and quantitative measure for postacquisition assessment on balance abilities.