Smartwatch Accelerometer Step Counting That Rejects False Positives During Non-Walking Wrist Movement

Wrist-worn step-counting holds potential to improve health management and disease prevention. However, inaccurate step counting is often caused by false positives during non-walking wrist movements, potentially leading to incorrect health assessments, ineffective interventions, and suboptimal patient outcomes. We thus propose a real-time adaptive multi-stage step counting algorithm based on a smartwatch 3-axis accelerometer, integrating non-walking detection to identify false positive step counts during non-walking wrist movements. Sixty-seven subjects wore a smartwatch with a 3-axis accelerometer and performed walking and running trials and eight non-gait trials: eating with forks and chopsticks, drinking, rolling while sleeping, flipping the wrist to check a watch, moving and grasping objects, typing, and using a computer mouse. When evaluated on the proprietary dataset, the proposed model was 93.58% accurate in estimating step counts as compared with 10.09% accuracy from a standard peak detection framework that grossly over-counted steps during non-walking movements. In non-walking detection experiments, the proposed model was almost more accurate and efficient than other four baseline models (p < 0.05), while requiring only 8.9% of the inference time of a single OCSVM. These results highlight the importance of rejecting false positive step counts during non-walking movements from wrist-worn step counters, and our proposed approach holds potential to more accurately estimate step counting in real-life scenarios to improve aerobic exercise assessment and promote sedentary disease prevention.