Automatic Step Detection of Tandem Gait Test in Patients with Vestibular Hypofunction Using Wearable Sensors

The tandem gait test is a common examination in testing individuals with possible neurologic diseases, such as motor neurons or cortex problems, and vestibular disorders. A reliable step detection method is essential to capture these step-based features to assess vestibular hypofunction. However, most studies still relied on manual labeling or a simple threshold-based peak detection approach to obtain step information. These methods often suffer issues in time consuming and reliability. This study proposes an automatic and accurate step detection algorithm for the tandem gait test using a dynamic threshold. This technique could filter noises and adapt to individual step patterns. The proposed algorithm was validated on a dataset including 15 healthy subjects and 62 patients with peripheral vestibular disorders. The results show that using the developed step detection approach with the shank-worn sensor has the best performance, which achieves 99.8%, 100%, 99.8% and 99.9% in accuracy, recall, precision and F1-score, respectively.