A hybrid multi-branch deep learning model for autism spectrum disorder detection using spatiotemporal gait analysis

Early and objective diagnosis of autism spectrum disorders (ASD) remains a pressing issue in modern medical and cognitive informatics, where motor behavior analysis is considered a promising source of digital biomarkers. In particular, kinematic gait characteristics obtained with contactless sensors enable the identification of subtle motor impairments that may not be apparent on visual assessment. In this paper, we propose a hybrid multi-branch deep learning model, HMB-TSC, for the automatic classification of children with ASD based on spatiotemporal gait data extracted from the Kinect v2 camera. An open dataset containing gait recordings of children with ASD and typically developing (TD) peers was used for the experiments. Data preprocessing included outlier filtering, feature normalization, feature expansion, and time-series segmentation using a sliding-window method. The HMB-TSC architecture combines three complementary branches: a Transformer for modeling global temporal dependencies, a CNN-BiLSTM for extracting local spatiotemporal patterns, and a CNN-BiGRU for efficient sequential analysis. The performance evaluation was conducted using the holdout, 5-fold, and 5 × 3 nested validation protocols. In a reproducible released run, HMB-TSC achieved Accuracy = 0.850, AUROC = 0.948, and F1 = 0.880 in holdout validation. Under 5-fold and 5 × 3 nested cross-validation, the model achieved Accuracy = 0.850 ± 0.035, AUROC = 0.940 ± 0.029, and F1 = 0.845 ± 0.036. Compared with the baseline CNN, LSTM, and Transformer models, the proposed model demonstrated strong and stable overall performance across validation protocols, especially in terms of AUROC and balanced F1-score.

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