Association between central sensitization and gait in chronic low back pain:Insights from a machine learning approach

BACKGROUND: Central sensitization (CS) is often present in patients with chronic low back pain (CLBP). Gait impairments due to CLBP have been extensively reported; however, the association between CS and gait is unknown. The present study examined the association between CS and CLBP on gait during activities of daily living. METHOD: Forty-two patients with CLBP were included. CS was assessed through the Central Sensitization Inventory (CSI), and patients were divided in a low and high CS group (23 CLBP- and 19 CLBP+, respectively). Patients wore a tri-axial accelerometer device for one week. From the acceleration signals, gait cycles were extracted and 36 gait outcomes representing quantitative and qualitative characteristics of gait were calculated. A Random Forest was trained to classify CLBP- and CLBP + based on the gait outcomes. The maximum Youden index was computed to measure the diagnostic test's ability and SHapley Additive exPlanations (SHAP) indexed the gait outcomes' importance to the classification model. RESULTS: The Random Forest accurately (84.4%) classified the CLBP- and CLBP+. Youden index was 0.65, and SHAP revealed that the gait outcomes' important to the classification model were related to gait smoothness, stride frequency variability, stride length variability, stride regularity, predictability, and stability. CONCLUSIONS: CLBP- and CLBP + patients had different motor control strategies. Patients in the CLBP- group presented with a more "loose control", with higher gait smoothness and stability, while CLBP + patients presented with a "tight control", with a more regular, less variable, and more predictable gait pattern

Vector Graph Assisted Pedestrian Dead Reckoning Using an Unconstrained Smartphone

The paper presents a hybrid indoor positioning solution based on a pedestrian dead reckoning (PDR) approach using built-in sensors on a smartphone. To address the challenges of flexible and complex contexts of carrying a phone while walking, a robust step detection algorithm based on motion-awareness has been proposed. Given the fact that step length is influenced by different motion states, an adaptive step length estimation algorithm based on motion recognition is developed. Heading estimation is carried out by an attitude acquisition algorithm, which contains a two-phase filter to mitigate the distortion of magnetic anomalies. In order to estimate the heading for an unconstrained smartphone, principal component analysis (PCA) of acceleration is applied to determine the offset between the orientation of smartphone and the actual heading of a pedestrian. Moreover, a particle filter with vector graph assisted particle weighting is introduced to correct the deviation in step length and heading estimation. Extensive field tests, including four contexts of carrying a phone, have been conducted in an office building to verify the performance of the proposed algorithm. Test results show that the proposed algorithm can achieve sub-meter mean error in all contexts