Development of a Novel Step Detection Algorithm for Gait Evaluation of Patients with Hemiplegia Based on Trunk Accelerometer

In this study, we have developed a novel step detection algorithm for gait evaluation of patients with hemiplegia based on trunk accelerometry device. For this, we have used a bandpass filter and a least square acceleration (LSA) filter which is characterized by emphasizing the peak or valley point of the acceleration signals for each 3-axis accelerometer signals. To evaluate the algorithm, the detected steps by developed algorithm and real steps by the motion analysis system were compared. As a result, we could obtain the sensitivity of 96.44%, the specificity of 99.94% and the accuracy of 99.90% for the patients' data sets and the sensitivity of 100%, the specificity of 99.93% and the accuracy of 99.93% for the normal data sets. In conclusion, the developed algorithm is useful for the step detection for patients with hemiplegia as well as normal subjects.