Optimizing Rare Disease Gait Classification through Data Balancing and Generative AI: Insights from Hereditary Cerebellar Ataxia

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 11; p. 3613
• Main Authors: Trabassi, Dante, Castiglia, Stefano Filippo, Bini, Fabiano, Marinozzi, Franco, Ajoudani, Arash, Lorenzini, Marta, Chini, Giorgia, Varrecchia, Tiwana, Ranavolo, Alberto, De Icco, Roberto, Casali, Carlo, Serrao, Mariano
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.06.2024; MDPI
• Subjects: Adult; Aged; Algorithms; Artificial Intelligence; Ataxia; Cerebellar ataxia; Cerebellar Ataxia - diagnosis; Cerebellar Ataxia - genetics; Cerebellar Ataxia - physiopathology; Classification; data augmentation; data balancing; Data collection; Datasets; Diseases; Female; Fourier transforms; Gait; Gait - physiology; gait analysis; Gait Analysis - methods; generative artificial intelligence; Humans; Kinematics; Male; Medical research; Medicine, Experimental; Middle Aged; Range of motion; Rare Diseases; Software; Walking; Italy; inertial measurement unit; rare diseases; cerebellar ataxia; data augmentation; conditional tabular generative artificial network; data balancing; synthetic minority oversampling technique; generative artificial network; generative artificial intelligence; gait analysis

The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to assess the effectiveness of data balancing and generat...