The performance of a machine learning model in predicting accelerometer-derived walking speed.

Background: Obtaining long-term measurements of walking speed in large-scale studies remains challenging. The aim of this study was to develop and evaluate the performance of a machine learning classifier in predicting slow (≤4 km/h), moderate (4.1-5.4 km/h), and brisk (≥5.5 km/h) walking speeds in adults based on dual and single accelerometer set-ups. Methods: Twenty-four adults (mean age [SD, range] 36.1 [11.9, 23-62] years) participated in the study. Two tri-axial accelerometers positioned on the thigh and low back were used to record body movements. A measuring wheel with a speedometer along with video recording were used to define and record consecutive 5-min periods with the three walking speeds and jogging during conditions resembling free-living. In addition, we included a 5-min period with gradual increase and decrease in walking speed from slow to brisk and vice versa. The video recordings were labelled and used as ground truth for training an eXtreme Gradient Boosting (XGBoost) machine learning classifier. Windows of 1, 3, and 5 s duration were used to train the classifier. The performance of the classifier was evaluated by leave-one-out cross-validation. Results: Total recording time was ∼600 min (∼25 min per participant). Performance metrics for predicting walking speeds (i.e., slow, moderate, brisk) and jogging were largely similar for the dual and single accelerometer set-ups as well as for the different window lengths. The highest overall accuracy was 91 % (SD 11 %, range 59-98 % for individual participants) using a dual accelerometer set-up and a 5-s window, whereas the lowest overall accuracy was 88 % (SD 11 %, range 51-96 % for individual participants) using a single thigh accelerometer set-up and a 1-s window. Conclusions: A machine learning classifier can be used to accurately predict slow, moderate, and brisk walking speeds based on both a dual and single accelerometer set-up on the thigh and/or low back.