Gait assessment is critical for diagnosing and monitoring neurological disorders, yet current clinical standards remain largely subjective and qualitative. Recent advances in AI have enabled more quantitative and accessible gait analysis using widely available sensors such as smartphone cameras.
However, most existing AI models are designed for specific patient populations and sensor configurations, primarily due to the scarcity of diverse clinical datasets—a constraint often driven by privacy concerns. As a result, these models tend to underperform when applied to populations or settings not well represented in the training data, limiting their broader clinical applicability.
In a study published in Nature Communications, researchers from IBM Research, the Cleveland Clinic, and the University of Tsukuba propose a novel framework to overcome this limitation. Their approach involves generating synthetic gait data using generative AI trained on physics-based musculoskeletal simulations.
Hi-tech surveillance technologies are a double-edged sword. On the one hand, you want sophisticated devices to detect suspicious behavior and alert authorities. But on the other, there is the need to protect individual privacy. Balancing public safety and personal freedoms is an ongoing challenge for innovators and policymakers.
This debate is set to reignite with news that researchers at La Sapienza University in Rome have developed a system that can identify individuals just by the way they disrupt Wi-Fi signals.
The scientists have dubbed this new technology “WhoFi.” Unlike traditional biometric systems such as fingerprint scanners and facial recognition, it doesn’t require direct physical contact or visual feeds. WhoFi can also track individuals in a larger area than a fixed-position camera, provided there is a Wi-Fi network.
Clearing your cookies is not enough to protect your privacy online. New research led by Texas A&M University has found that websites are covertly using browser fingerprinting—a method to uniquely identify a web browser—to track people across browser sessions and sites.
Facial morphology is a distinctive biometric marker, offering invaluable insights into personal identity, especially in forensic science. In the context of high-throughput sequencing, the reconstruction of 3D human facial images from DNA is becoming a revolutionary approach for identifying individuals based on unknown biological specimens. Inspired by artificial intelligence techniques in text-to-image synthesis, it proposes Difface, a multi-modality model designed to reconstruct 3D facial images only from DNA. Specifically, Difface first utilizes a transformer and a spiral convolution network to map high-dimensional Single Nucleotide Polymorphisms and 3D facial images to the same low-dimensional features, respectively, while establishing the association between both modalities in the latent features in a contrastive manner; and then incorporates a diffusion model to reconstruct facial structures from the characteristics of SNPs. Applying Difface to the Han Chinese database with 9,674 paired SNP phenotypes and 3D facial images demonstrates excellent performance in DNA-to-3D image alignment and reconstruction and characterizes the individual genomics. Also, including phenotype information in Difface further improves the quality of 3D reconstruction, i.e. Difface can generate 3D facial images of individuals solely from their DNA data, projecting their appearance at various future ages. This work represents pioneer research in de novo generating human facial images from individual genomics information.
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This study has introduced Difface, a de novo multi-modality model to reconstruct 3D facial images from DNA with remarkable precision, by a generative diffusion process and a contrastive learning scheme. Through comprehensive analysis and SNP-FACE matching tasks, Difface demonstrated superior performance in generating accurate facial reconstructions from genetic data. In particularly, Difface could generate/predict 3D facial images of individuals solely from their DNA data at various future ages. Notably, the model’s integration of transformer networks with spiral convolution and diffusion networks has set a new benchmark in the fidelity of generated images to their real images, as evidenced by its outstanding accuracy in critical facial landmarks and diverse facial feature reproduction.
Difface’s novel approach, combining advanced neural network architectures, significantly outperforms existing models in genetic-to-phenotypic facial reconstruction. This superiority is attributed to its unique contrastive learning method of aligning high-dimensional SNP data with 3D facial point clouds in a unified low-dimensional feature space, a process further enhanced by adopting diffusion networks for phenotypic characteristic generation. Such advancements contribute to the model’s exceptional precision and ability to capture the subtle genetic variations influencing facial morphology, a feat less pronounced in previous methodologies.
Despite Difface’s demonstrated strengths, there remain directions for improvement. Addressing these limitations will require a focused effort to increase the model’s robustness and adaptability to diverse datasets. Future research should aim to incorporating variables like age and BMI would allow Difface to simulate age-related changes, enabling the generation of facial images at different life stages an application that holds significant potential in both forensic science and medical diagnostics. Similarly, BMI could help the model account for variations in body composition, improving its ability to generate accurate facial reconstructions across a range of body types.
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In an era where data privacy concerns loom large, a new approach in artificial intelligence (AI) could reshape how sensitive information is processed.
Researchers Austin Ebel and Karthik Garimella, Ph.D. students, and Assistant Professor of Electrical and Computer Engineering Brandon Reagen have introduced Orion, a novel framework that brings fully homomorphic encryption (FHE) to deep learning—allowing AI models to practically and efficiently operate directly on encrypted data without needing to decrypt it first.
