In Alzheimer's Disease and Related Dementia (ADRD) prediction, Electronic Health Records (EHRs) provide rich but fragmented information. Without a coherent clinical narrative, models tend to rely on a few dominant signals (e.g., age-related patterns) rather than capturing the underlying clinical mechanisms. Such reliance becomes problematic in younger-onset cases, where these signals are less informative. To address this challenge, we propose RaiLED-AD, a dual-encoder teacher-student framework where the student learns from serialized EHR data and the teacher leverages LLM-generated narratives that capture temporal and relational patterns. A hybrid objective with soft-label supervision and hierarchical contrastive alignment transfers these reasoning signals to the student, which operates independently at inference. On a real-world EHR cohort, RaiLED-AD consistently improves ADRD prediction over baselines and achieves substantial gains in the challenging younger-onset subgroup (index age$<65$). These results highlight the potential of integrating LLM-derived reasoning signals with structured EHR models for early-stage ADRD risk prediction.

This article presents EARFace , a system that shows the feasibility of tracking facial landmarks for 3D facial reconstruction using in-ear acoustic sensors embedded within smart earphones. This enables a number of applications in the areas of facial expression tracking, user interfaces, AR/VR applications, affective computing, and accessibility, among others. Although conventional vision-based solutions break down under poor lighting and occlusions, and also suffer from privacy concerns, earphone platforms are robust to ambient conditions while being privacy-preserving. In contrast to prior work on earable platforms that perform outer-ear sensing for facial motion tracking, EARFace shows the feasibility of completely in-ear sensing with a natural earphone form factor, thus enhancing the comfort levels of wearing. The core intuition exploited by EARFace is that the shape of the ear canal changes due to the movement of facial muscles during facial motion. EARFace tracks the changes in shape of the ear canal by measuring ultrasonic channel frequency response of the inner ear, ultimately resulting in tracking of the facial motion. A transformer-based machine learning model is designed to exploit spectral and temporal relationships in the ultrasonic channel frequency response data to predict the facial landmarks of the user with an accuracy of 1.83 mm. Using these predicted landmarks, a 3D graphical model of the face that replicates the precise facial motion of the user is then reconstructed. Domain adaptation is further performed by adapting the weights of layers using a group-wise and differential learning rate. This decreases the training overhead in EARFace . The transformer-based machine learning model runs on smart phone devices with a processing latency of 13 ms and an overall low power consumption profile. Finally, usability studies indicate higher levels of comforts of wearing EARFace ’s earphone platform in comparison with alternative form factors.

Ubiquitous finger motion tracking enables a number of exciting applications in augmented reality, sports analytics, rehabilitation-healthcare, haptics, etc. This article presents NeuroPose, a system that shows the feasibility of 3-D finger motion tracking using a platform of wearable electromyography (EMG) sensors. EMG sensors can sense electrical potential from muscles due to finger activation, thus offering rich information for fine-grained finger motion sensing. However, converting the sensor information to 3-D finger poses is non trivial since signals from multiple fingers superimpose at the sensor in complex patterns. Toward solving this problem, NeuroPose fuses information from anatomical constraints of finger motion with machine learning architectures on recurrent neural networks (RNNs), encoder–decoder networks, and ResNets to extract 3-D finger motion from noisy EMG data. The generated motion pattern is temporally smooth as well as anatomically consistent. Furthermore, a transfer learning algorithm is leveraged to adapt a pretrained model on one user to a new user with minimal training overhead. A systematic study with 12 users demonstrates a median error of 6.24° and a 90%-ile error of 18.33° in tracking 3-D finger joint angles. The accuracy is robust to natural variation in sensor mounting positions as well as changes in wrist positions of the user. In addition, this article validates the feasibility of mirrored bilateral training approach with applications in prosthetic devices. Finally, NeuroPose is comprehensively evaluated on both low-end and recent smartphones with a processing latency of 0.019 s and low energy overhead.

Ubiquitous finger motion tracking enables a number of exciting applications in augmented reality, sports analytics, rehabilitation-healthcare, haptics etc. This paper presents NeuroPose, a system that shows the feasibility of 3D finger motion tracking using a platform of wearable ElectroMyoGraphy (EMG) sensors. EMG sensors can sense electrical potential from muscles due to finger activation, thus offering rich information for fine-grained finger motion sensing. However converting the sensor information to 3D finger poses is non trivial since signals from multiple fingers superimpose at the sensor in complex patterns. Towards solving this problem, NeuroPose fuses information from anatomical constraints of finger motion with machine learning architectures on Recurrent Neural Networks (RNN), Encoder-Decoder Networks, and ResNets to extract 3D finger motion from noisy EMG data. The generated motion pattern is temporally smooth as well as anatomically consistent. Furthermore, a transfer learning algorithm is leveraged to adapt a pretrained model on one user to a new user with minimal training overhead. A systematic study with 12 users demonstrates a median error of 6.24° and a 90%-ile error of 18.33° in tracking 3D finger joint angles. The accuracy is robust to natural variation in sensor mounting positions as well as changes in wrist positions of the user. NeuroPose is implemented on a smartphone with a processing latency of 0.101s, and a low energy overhead.