## 4 Methodology
### Adaptive Position Estimation
#### 4.1.2 Adaptive Step Length Estimation  
The step length estimation model reflects the mapping relationship between the inertial data features within the step segmentation and the step length. This paper constructs a  
Figure 6: Edge detection constraints.  
Figure 7: Step segmentation detection.  
chest-mounted inertial dataset, extracts the corresponding data features within the step segmentation as input to a BP neural network, and trains the adaptive step length estimation model to output the estimated step length for the step segmentation.  
In the experiment, test subjects wore IMU data acquisition units on their chests and performed various activities, including walking, jogging, jumping, crouch walking, crawling, side stepping, climbing stairs, descending stairs, bending, and turning left and right in place. During data collection, the step length of each step was calibrated. Since the IMU placement in existing pedestrian datasets does not match the setup used in this study, or the datasets involve a limited set of motion modes, a new dataset was constructed, and step lengths were calibrated. The calibration method involved placing markers on the inside of the subject's ankles, leaving marks on the ground with each step, and measuring these marks to obtain the step length.  
Using the step segmentation detection algorithm, the collected inertial data was segmented into step segmentations, and feature data was extracted to construct the training dataset for the adaptive step length estimation model. Twelve feature parameters from the step segmentations were selected for dataset construction. These feature parameters include Y and Z axis peaks from the accelerometer, Y and Z axis troughs from the accelerometer, accelerometer frequency band energy, Y and Z axis troughs from the gyroscope, Y axis peaks and troughs from the magnetometer, skewness from the magnetometer, skewness from the gyroscope, and skewness from the accelerometer.  
The BP neural network-based step length estimation model was constructed, as shown in Figure 8. The input layer contains 12 neurons, corresponding to the 12 input data features. The two hidden layers contain 20 and 10 neurons, respectively. The output layer contains 1 neuron, corresponding to the step length.  
To evaluate the accuracy of the adaptive step length estimation model, the chest inertial data and step length measurements during the subjects' movements were collected as a dataset. Specifically, we collected a total of 2520 steps of motion data, covering various locomotion modes such as walking, running, and crouching. For each step, the corresponding step length and segmentation points were labeled.  
To illustrate the model's performance during the training, validation, and testing phases, we present Figure 9, which depicts the error distribution across different instances. Overall, the model demonstrates a fairly concentrated error distribution with most error values close to the zero-error baseline, highlighting high accuracy and robustness.  
To further analyze the performance of our model, we calculated the Root Mean Square Error (RMSE) on the validation set. The computed RMSE is 0.047 m. This result indicates that our adaptive step length estimation model performs well in estimating pedestrian step lengths across various motion states. Such a low RMSE value suggests that the model is not  
Figure 8: Diagram of the adaptive step length estimation model.  
only accurate but also robust in handling different types of human locomotion, including walking, running, and crouching.