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, mo...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings ... Asia-Pacific Signal and Information Processing Association Annual Summit and Conference APSIPA ASC ... (Online) s. 1550 - 1555
Hlavní autoři: Huang, Yi-Ju, Liu, Chien-Pin, Ting, Kuan-Chung, Hsieh, Chia-Yeh, Liu, Kai-Chun, Chan, Chia-Tai
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: Asia-Pacific of Signal and Information Processing Association (APSIPA) 07.11.2022
Témata:
Filtering algorithms
Heuristic algorithms
Information filters
Labeling
Manuals
Neurons
Reliability
ISSN:2640-0103
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
ISSN:2640-0103
DOI:10.23919/APSIPAASC55919.2022.9979880