Park, Beomjoon Kim, Minhye Jung, Dawoon Kim, Jinwook Mun, Kyung-Ryoul 2025-04-09T08:31:05Z 2025-04-09T08:31:05Z 2025-04-09 2025-03 2055-2076 https://pubs.kist.re.kr/handle/201004/152232 Objective Gait analysis plays a pivotal role in evaluating walking abilities, with recent advancements in digital health stressing the importance of efficient data collection methods. This study aims to classify nine gait types including one normal and eight abnormal gaits, using sequential network-based models and diverse feature combinations obtained from insole sensors.Methods The dataset was collected using insole sensors from subjects performing 15 m walking with designated gait types. The sensors incorporated pressure sensors and inertial measurement units (IMUs), along with the center of pressure engineered from the pressure readings. A number of deep learning architectures were evaluated for their ability to classify the gait types, focusing on feature sets including temporal parameters, statistical features of pressure signals, center of pressure data, and IMU data. Ablation studies were also conducted to assess the impact of combining features from different modalities.Results Our results demonstrate that models incorporating IMU features outperform those using different combinations of modalities including individual feature sets, with the top-performing models achieving F1-scores of up to 90% in sample-wise classification and 92% in subject-wise classification. Additionally, an ablation study reveals the importance of considering diverse feature modalities, including temporal parameters, statistical features from pressure signals, center of pressure data, and IMU data, for comprehensive gait classification.Conclusion Overall, this study successfully developed deep sequential models that effectively classify nine different gait types, with the ablation study underscoring the potential for integrating features from diverse domains to enhance clinical applications, such as intervention for gait-related disorders. English SAGE Publications Smart insole-based abnormal gait identification: Deep sequential networks and feature ablation study Article 10.1177/20552076251332999 1 Digital Health, v.11 Digital Health 11 Y scie ssci scopus 001456065800001 2-s2.0-105001648215 Health Care Sciences & Services Health Policy & Services Public, Environmental & Occupational Health Medical Informatics Health Care Sciences & Services Public, Environmental & Occupational Health Medical Informatics Article Abnormal gait deep sequential networks gait analysis insole sensors feature selection