Stride-length Estimation Method for Indoor Navigation Assisted by SEMG Signals

To solve the issues of low estimation accuracy and limited applicability of the existing stride length estimation (SLE) methods in indoor personnel positioning, a novel mixed stride length estimation (MSLE) model is proposed, combining the adaptive Harris hawk optimization (AHHO)-backpropagation neural network (BPNN) and the inverted pendulum model. This model accurately estimates pedestrian stride-length by analyzing and extracting features from the accelerometer, gyroscope data, and surface electromyography (SEMG) signals. The collected sensor signals are preprocessed using the second-generation wavelet algorithm. The peak detection algorithm is employed for stride counting, and based on this, a SLE algorithm is proposed using an AHHO-BPNN model. Subsequently, a MSLE model is developed by fitting it with a three-dimensional linear inverted pendulum model (3D-LIPM). The resulting model is then tested for individual indoor SLE. The experimental results indicate that the MSLE model can accurately estimate indoor stride lengths under different walking speeds. Compared with traditional models, it has lower SLE errors, meeting the requirements of personal indoor positioning. Therefore, this model has great potential for applications in fields such as rehabilitation medicine and remote monitoring.