Tariq Adnan

Tariq Adnan, Md Saiful Islam, Wasifur Rahman et al.

We present an efficient and accessible PD screening method by leveraging AI-driven models enabled by the largest video dataset of facial expressions from 1,059 unique participants. This dataset includes 256 individuals with PD, 165 clinically diagnosed, and 91 self-reported. Participants used webcams to record themselves mimicking three facial expressions (smile, disgust, and surprise) from diverse sources encompassing their homes across multiple countries, a US clinic, and a PD wellness center in the US. Facial landmarks are automatically tracked from the recordings to extract features related to hypomimia, a prominent PD symptom characterized by reduced facial expressions. Machine learning algorithms are trained on these features to distinguish between individuals with and without PD. The model was tested for generalizability on external (unseen during training) test videos collected from a US clinic and Bangladesh. An ensemble of machine learning models trained on smile videos achieved an accuracy of 87.9+-0.1% (95% Confidence Interval) with an AUROC of 89.3+-0.3% as evaluated on held-out data (using k-fold cross-validation). In external test settings, the ensemble model achieved 79.8+-0.6% accuracy with 81.9+-0.3% AUROC on the clinical test set and 84.9+-0.4% accuracy with 81.2+-0.6% AUROC on participants from Bangladesh. In every setting, the model was free from detectable bias across sex and ethnic subgroups, except in the cohorts from Bangladesh, where the model performed significantly better for female participants than males. Smiling videos can effectively differentiate between individuals with and without PD, offering a potentially easy, accessible, and cost-efficient way to screen for PD, especially when a clinical diagnosis is difficult to access.

Md Saiful Islam, Ekram Hossain, Abdelrahman Abdelkader et al.

Video-based assessments offer a scalable pathway for remote Parkinson's disease (PD) screening. While traditional approaches rely on handcrafted features mimicking clinical scales, recent advances in video foundation models (VFMs) enable representation learning without task-specific customization. However, the comparative effectiveness of different VFM architectures across diverse clinical tasks remains poorly understood. We present a large-scale systematic study using a novel video dataset from 1,888 participants (727 with PD), comprising 32,847 videos across 16 standardized clinical tasks. We evaluate seven state-of-the-art VFMs -- including VideoPrism, V-JEPA, ViViT, and VideoMAE -- to determine their robustness in clinical screening. By evaluating frozen embeddings with a linear classification head, we demonstrate that task saliency is highly model-dependent: VideoPrism excels in capturing visual speech kinematics (no audio) and facial expressivity, while V-JEPA proves superior for upper-limb motor tasks. Notably, TimeSformer remains highly competitive for rhythmic tasks like finger tapping. Our experiments yield AUCs of 76.4 - 85.3% and accuracies of 71.5 - 80.6%. While high specificity (up to 90.3%) suggests strong potential for ruling out healthy individuals, the lower sensitivity (43.2 - 57.3%) highlights the need for task-aware calibration and integration of multiple tasks and modalities. Overall, this work establishes a rigorous baseline for VFM-based PD screening and provides a roadmap for selecting suitable tasks and architectures in remote neurological monitoring. Code and anonymized structured data are publicly available: https://anonymous.4open.science/r/parkinson\_video\_benchmarking-A2C5

Tariq Adnan, Abdelrahman Abdelkader, Zipei Liu et al.

We present a framework to recognize Parkinson's disease (PD) through an English pangram utterance speech collected using a web application from diverse recording settings and environments, including participants' homes. Our dataset includes a global cohort of 1306 participants, including 392 diagnosed with PD. Leveraging the diversity of the dataset, spanning various demographic properties (such as age, sex, and ethnicity), we used deep learning embeddings derived from semi-supervised models such as Wav2Vec 2.0, WavLM, and ImageBind representing the speech dynamics associated with PD. Our novel fusion model for PD classification, which aligns different speech embeddings into a cohesive feature space, demonstrated superior performance over standard concatenation-based fusion models and other baselines (including models built on traditional acoustic features). In a randomized data split configuration, the model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 88.94% and an accuracy of 85.65%. Rigorous statistical analysis confirmed that our model performs equitably across various demographic subgroups in terms of sex, ethnicity, and age, and remains robust regardless of disease duration. Furthermore, our model, when tested on two entirely unseen test datasets collected from clinical settings and from a PD care center, maintained AUROC scores of 82.12% and 78.44%, respectively. This affirms the model's robustness and it's potential to enhance accessibility and health equity in real-world applications.

Md Saiful Islam, Tariq Adnan, Jan Freyberg et al.

Limited accessibility to neurological care leads to underdiagnosed Parkinson's Disease (PD), preventing early intervention. Existing AI-based PD detection methods primarily focus on unimodal analysis of motor or speech tasks, overlooking the multifaceted nature of the disease. To address this, we introduce a large-scale, multi-task video dataset consisting of 1102 sessions (each containing videos of finger tapping, facial expression, and speech tasks captured via webcam) from 845 participants (272 with PD). We propose a novel Uncertainty-calibrated Fusion Network (UFNet) that leverages this multimodal data to enhance diagnostic accuracy. UFNet employs independent task-specific networks, trained with Monte Carlo Dropout for uncertainty quantification, followed by self-attended fusion of features, with attention weights dynamically adjusted based on task-specific uncertainties. To ensure patient-centered evaluation, the participants were randomly split into three sets: 60% for training, 20% for model selection, and 20% for final performance evaluation. UFNet significantly outperformed single-task models in terms of accuracy, area under the ROC curve (AUROC), and sensitivity while maintaining non-inferior specificity. Withholding uncertain predictions further boosted the performance, achieving 88.0+-0.3%$ accuracy, 93.0+-0.2% AUROC, 79.3+-0.9% sensitivity, and 92.6+-0.3% specificity, at the expense of not being able to predict for 2.3+-0.3% data (+- denotes 95% confidence interval). Further analysis suggests that the trained model does not exhibit any detectable bias across sex and ethnic subgroups and is most effective for individuals aged between 50 and 80. Requiring only a webcam and microphone, our approach facilitates accessible home-based PD screening, especially in regions with limited healthcare resources.

Masum Hasan, Cengiz Ozel, Nina Long et al.

Hand pose estimation plays a vital role in capturing subtle nonverbal cues essential for understanding human affect. However, collecting diverse, expressive real-world data remains challenging due to labor-intensive manual annotation that often underrepresents demographic diversity and natural expressions. To address this issue, we introduce a cost-effective approach to generating synthetic data using high-fidelity 3D hand models and a wide range of affective hand poses. Our method includes varied skin tones, genders, dynamic environments, realistic lighting conditions, and diverse naturally occurring gesture animations. The resulting dataset, Hi5, contains 583,000 pose-annotated images, carefully balanced to reflect natural diversity and emotional expressiveness. Models trained exclusively on Hi5 achieve performance comparable to human-annotated datasets, exhibiting superior robustness to occlusions and consistent accuracy across diverse skin tones -- which is crucial for reliably recognizing expressive gestures in affective computing applications. Our results demonstrate that synthetic data effectively addresses critical limitations of existing datasets, enabling more inclusive, expressive, and reliable gesture recognition systems while achieving competitive performance in pose estimation benchmarks. The Hi5 dataset, data synthesis pipeline, source code, and game engine project are publicly released to support further research in synthetic hand-gesture applications.