Gait adaptation in adults with intellectual disability with and without Down syndrome: A Kinect-based neural network approach.

Journal: Technology and health care : official journal of the European Society for Engineering and Medicine
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Abstract

BackgroundRecognizing that walking is a critical marker of independence, fall risk, and overall health in adults with intellectual disability (ID) and that comparative evidence by Down syndrome (DS) status remains limited, this study aimed to quantify spatial, temporal, and kinematic gait differences across flat and compliant surfaces in adults with and without DS and to evaluate the classification performance of Kinect-derived gait data using artificial neural networks (ANNs).MethodsCross-sectional, exploratory study at a state special education center in eastern Türkiye. Sixty-nine participants aged 18-27 years (ID without DS: n = 46; ID with DS: n = 23; mild-moderate levels) completed three 3-m trials at preferred speed on flat (concrete) and compliant (foam) surfaces. Kinect V1 recorded 3D joint trajectories. Step count, step length, step duration, and walking speed were computed via a threshold-based method. ANN models were trained to classify by DS status, gender, and disability level/age.ResultsOn compliant surfaces, step count and walking time increased while walking speed decreased relative to flat surfaces. Performance decrements were more pronounced in participants with DS and those with moderate ID. Women took more steps and walked more slowly than men on both surfaces. ANN models showed high correlation values across tasks (R = 0.96-0.99); however, these findings should be interpreted as exploratory.ConclusionA Kinect-based approach may provide a practical and accessible method for characterizing gait in adults with ID with and without DS; however, the findings should be interpreted with caution due to methodological limitations and the need for further validation. Findings support integrating surface-specific balance and gait training, age- and gender-sensitive strategies, and individualized programs for DS. ANN-based analyses may provide preliminary insights for classification purposes; however, their clinical applicability requires further validation and should be interpreted cautiously.

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