An Accelerometer Based Gait Analysis System to Detect Gait Abnormalities in Cerebralspinal Meningitis Patients

This paper proposed a gait analysis system to detect abnormal gaits based on each gait cycle. The proposed system took advantage of a tri-axial accelerometer to collect the gait signals in three dimensions. The collected signals were divided into four intervals for each gait cycle, including the step, swing, stance phase, and stride. The time domain and time-frequency domain features were generated for each interval. Later, Fisher score was calculated to determine discrimination ability for each feature. Support Vector Machine would be trained for classification of normal and abnormal gaits based on selected features with the highest Fisher scores. Cerebralspinal Meningitis (CSM) patients with/without spinal cord edema were used as samples to conduct the experiments. The results demonstrated that the proposed gait analysis system could provide 90% accuracy. The feature subset with the best accuracy includes kurtosis, crest factor, and mean of lateral acceleration data in stride interval. It implied the force to make the body left and right in stride interval is an critical indicator for diagnosis of spinal cord edema. The proposed gait analysis system could further be extended to more symptoms if other sets of training samples are available in the future.