A Frequency-aware Augmentation Network for Mental Disorders Assessment from Audio

Depression and Attention Deficit Hyperactivity Disorder (ADHD) stand out as the common mental health challenges today. In affective computing, speech signals serve as effective biomarkers for mental disorder assessment. Current research, relying on labor-intensive hand-crafted features or simplistic time-frequency representations, often overlooks critical details by not accounting for the differential impacts of various frequency bands and temporal fluctuations. Therefore, we propose a frequency-aware augmentation network with dynamic convolution for depression and ADHD assessment. In the proposed method, the spectrogram is used as the input feature and adopts a multi-scale convolution to help the network focus on discriminative frequency bands related to mental disorders. A dynamic convolution is also designed to aggregate multiple convolution kernels dynamically based upon their attentions which are input-independent to capture dynamic information. Finally, a feature augmentation block is proposed to enhance the feature representation ability and make full use of the captured information. Experimental results on AVEC 2014 and self-recorded ADHD dataset prove the robustness of our method, an RMSE of 9.23 was attained for estimating depression severity, along with an accuracy of 89.8\% in detecting ADHD.

ADHD diagnosis based on action characteristics recorded in videos using machine learning

Demand for ADHD diagnosis and treatment is increasing significantly and the existing services are unable to meet the demand in a timely manner. In this work, we introduce a novel action recognition method for ADHD diagnosis by identifying and analysing raw video recordings. Our main contributions include 1) designing and implementing a test focusing on the attention and hyperactivity/impulsivity of participants, recorded through three cameras; 2) implementing a novel machine learning ADHD diagnosis system based on action recognition neural networks for the first time; 3) proposing classification criteria to provide diagnosis results and analysis of ADHD action characteristics.

Skeleton-based action analysis for ADHD diagnosis

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurobehavioral disorder worldwide. While extensive research has focused on machine learning methods for ADHD diagnosis, most research relies on high-cost equipment, e.g., MRI machine and EEG patch. Therefore, low-cost diagnostic methods based on the action characteristics of ADHD are desired. Skeleton-based action recognition has gained attention due to the action-focused nature and robustness. In this work, we propose a novel ADHD diagnosis system with a skeleton-based action recognition framework, utilizing a real multi-modal ADHD dataset and state-of-the-art detection algorithms. Compared to conventional methods, the proposed method shows cost-efficiency and significant performance improvement, making it more accessible for a broad range of initial ADHD diagnoses. Through the experiment results, the proposed method outperforms the conventional methods in accuracy and AUC. Meanwhile, our method is widely applicable for mass screening.