Radar-based, contactless in-home vital sign monitoring can significantly improve the healthcare of the rapidly growing aging population by enabling early detection of clinical events and continuous tracking of physiological states, while maintaining privacy, comfort, and user compliance. We introduce a unique experimental environment, collected for over a year – consisting of populated test homes, equipped with arrays of continuous-wave radar sensors placed on ceilings and walls, with occupants engaged in everyday activities within the homes. We applied and compared multiple analytical methods to extract heart rate from the radar data, using combinations of normalized cross-correlation (NXC), machine learning with individual sensors, and sensor fusion. Our results show that sensor fusion leads to the lowest mean absolute errors, reduced by over 30% from single-sensor machine learning models and over 80% from NXC. We additionally observed that a dense sensor array design enhances signal quality and reduces noise by more than 15%, providing more accurate heart rate monitoring even during dynamic activities, such as jumping and walking. These results highlight the effectiveness of sensor fusion and dense array designs in real-world settings. Our novel experimental platform and analytical results represent a meaningful step toward scalable application of radar technology in real residential settings for non-contact health monitoring.

We present ViTaM-D, a novel visual-tactile framework for reconstructing dynamic hand-object interaction with distributed tactile sensing to enhance contact modeling. Existing methods, relying solely on visual inputs, often fail to capture occluded interactions and object deformation. To address this, we introduce DF-Field, a distributed force-aware contact representation leveraging kinetic and potential energy in hand-object interactions. ViTaM-D first reconstructs interactions using a visual network with contact constraint, then refines contact details through force-aware optimization, improving object deformation modeling. To evaluate deformable object reconstruction, we introduce the HOT dataset, featuring 600 hand-object interaction sequences in a high-precision simulation environment. Experiments on DexYCB and HOT datasets show that ViTaM-D outperforms state-of-the-art methods in reconstruction accuracy for both rigid and deformable objects. DF-Field also proves more effective in refining hand poses and enhancing contact modeling than previous refinement methods. The code, models, and datasets are available at https://sites.google.com/view/vitam-d/.

Large language models (LLMs) are increasingly essential in processing natural languages, yet their application is frequently compromised by biases and inaccuracies originating in their training data.In this study, we introduce \textbf{Cross-Care}, the first benchmark framework dedicated to assessing biases and real world knowledge in LLMs, specifically focusing on the representation of disease prevalence across diverse demographic groups.We systematically evaluate how demographic biases embedded in pre-training corpora like $ThePile$ influence the outputs of LLMs.We expose and quantify discrepancies by juxtaposing these biases against actual disease prevalences in various U.S. demographic groups.Our results highlight substantial misalignment between LLM representation of disease prevalence and real disease prevalence rates across demographic subgroups, indicating a pronounced risk of bias propagation and a lack of real-world grounding for medical applications of LLMs.Furthermore, we observe that various alignment methods minimally resolve inconsistencies in the models' representation of disease prevalence across different languages.For further exploration and analysis, we make all data and a data visualization tool available at: \url{www.crosscare.net}.

This work presents the first quantitative ultrasonic sound speed images of ex vivo limb cross-sections containing both soft tissue and bone using Full Waveform Inversion (FWI) with level set (LS) and travel time regularization. The estimated bulk sound speed of bone and soft tissue are within 10% and 1%, respectively, of ground truth estimates. The sound speed imagery shows muscle, connective tissue and bone features. Typically, ultrasound tomography (UST) using FWI is applied to imaging breast tissue properties (e.g. sound speed and density) that correlate with cancer. With further development, UST systems have the potential to deliver volumetric operator independent tissue property images of limbs with non-ionizing and portable hardware platforms. This work addresses the algorithmic challenges of imaging the sound speed of bone and soft tissue by combining FWI with LS regularization and travel time methods to recover soft tissue and bone sound speed with improved accuracy and reduced soft tissue artifacts when compared to conventional FWI. The value of leveraging LS and travel time methods is realized by evidence of improved bone geometry estimates as well as promising convergence properties and reduced risk of final model errors due to un-modeled shear wave propagation. Ex vivo bulk measurements of sound speed and MRI cross-sections validates the final inversion results.

A common method for image alignment in computer vision is finding the maximum consensus transformation for a set of features in the images. This is commonly done using randomized methods such as RANSAC. While relatively robust when strong features are involved, these methods do not deal well with ambiguous features where maximum likelihood does not provide the best match between the images, a common case with modalities such as medical ultrasound, thermal imaging and cross modality registration. They also do not inherently allow for the application of external knowledge regarding possible configurations to aid in the registration. In this paper we present a novel statistical framework for maximum consensus image alignment which is both robust in the presence of weak features (features not providing one-to-one matches) while at the same time providing an inherent natural ability for integrating external knowledge. Our methods is able to collect information not only from finding good matches, but also from improbable and partially ambiguous matches. We demonstrate our framework in the context of medical ultrasound image registration. In our test cases, our method succeeded where other state of the art methods we compared to failed to provide satisfactory results with over 17% of the samples.

A hand-held force-controlled ultrasound probe has been developed for medical imaging applications. The probe-patient contact force can be held constant to improve image stability, swept through a range, or cycled. The mechanical portion of the device consists of a ball screw linear actuator driven by a servo motor, along with a load cell, accelerometer, and limit switches. The performance of the system was assessed in terms of the frequency response to simulated sonographer hand motion and in hand-held image feature tracking during simulated patient motion. The system was found to attenuate contact force variation by 97% at 0.1 Hz, 83% at 1 Hz, and 33% 10 Hz, a range that spans the typical human hand tremor frequency spectrum. In studies with 15 human operators, the device applied the target contact force with ten times less variation than in conventional ultrasound imaging. An ergonomic, human-in-the-loop, imaging-workflow enhancing control scheme, which combines both force- and position-control, permits smooth making and breaking of probe-patient contact, and helps the operator keep the probe centered within its range of motion. By controlling ultrasound probe contact force and consequently the amount of tissue deformation, the system enhances the repeatability, usability, and diagnostic capabilities of ultrasound imaging.

An ergonomic, handheld, force-controlled ultrasound probe has been developed for medical imaging applications. The device, which consists of an ultrasound probe mounted to a backlash-free ball screw actuator and driven by a compact servo motor, maintains a prescribed contact force between the ultrasound probe and patient's body. A control system which combines both a position and a force controller enables ergonomic operation by keeping the actuator centered within its range of motion and permits the repeated making and breaking of probe-patient contact. By controlling ultrasound probe contact force and consequently the amount of tissue deformation, the system enhances the repeatability, usability, and diagnostic capabilities of ultrasound imaging.