Wearable medical sensors (WMSs) are revolutionizing smart healthcare by enabling continuous, real-time monitoring of user physiological signals, especially in the field of consumer healthcare. The integration of WMSs and modern machine learning enables unprecedented solutions to efficient early-stage disease detection. Despite the success of Transformers in various fields, their application to sensitive domains, such as smart healthcare, remains underexplored due to limited data accessibility and privacy concerns. A key challenge in disease detection using Transformers with WMS data is the difficulty of obtaining sufficient labeled training data from individuals suffering from a specific disease, as this requires labeling by experts and extensive time and effort devoted to data collection and data privacy regulation compliance. The phrase “foundation models” conjures up an image of a large language model trained on text corpora. However, foundation models need not be large, nor necessarily trained on text. In this work, we propose a foundation model trained on WMS data under a continual fine-tuning framework called COMFORT. COMFORT introduces a novel approach for pre-training a Transformer-based foundation model on a large dataset of physiological signals exclusively collected from healthy individuals with commercially available WMSs. We adopt a masked data modeling (MDM) objective to pre-train this health foundation model. MDM is inspired by the mask language modeling approach employed in self-supervised training of language models. Our WMS-based foundation model can then be rapidly adapted to multiple disease detection tasks through various parameter-efficient fine-tuning (PEFT) methods, such as Low-Rank Adaptation and its variants. COMFORT continually stores the low-rank decomposition matrices and classifiers obtained using the PEFT algorithms to construct a library for multi-disease detection. This library enables scalable and memory-efficient disease detection on edge devices. Our experimental results demonstrate that COMFORT achieves highly competitive performance while reducing memory overhead by up to 52% relative to conventional methods. Thus, COMFORT paves the way for personalized and proactive solutions to efficient and effective early-stage disease detection.

Modern advances in machine learning (ML) and wearable medical sensors (WMSs) in edge devices have enabled ML-driven disease detection for smart healthcare. Conventional ML-driven methods for disease detection rely on customizing individual models for each disease and its corresponding WMS data. However, such methods lack adaptability to distribution shifts and new task classification classes. In addition, they need to be rearchitected and retrained from scratch for each new disease. Moreover, installing multiple ML models in an edge device consumes excessive memory, drains the battery faster, and complicates the detection process. To address these challenges, we propose DOCTOR, a multi-disease detection continual learning (CL) framework based on WMSs. It employs a multi-headed deep neural network (DNN) and a replay-style CL algorithm. The CL algorithm enables the framework to continually learn new missions in which different data distributions, classification classes, and disease detection tasks are introduced sequentially. It counteracts catastrophic forgetting with either a data preservation (DP) method or a synthetic data generation (SDG) module. The DP method preserves the most informative subset of real training data from previous missions for exemplar replay. The SDG module models the probability distribution of the real training data and generates synthetic data for generative replay while retaining data privacy. The multi-headed DNN enables DOCTOR to detect multiple diseases simultaneously based on user WMS data. We demonstrate DOCTOR’s efficacy in maintaining high disease classification accuracy with a single DNN model in various CL experiments. In complex scenarios, DOCTOR achieves 1.43× better average test accuracy, 1.25× better F1-score, and 0.41 higher backward transfer than the naïve fine-tuning framework, with a small model size of less than 350 KB.

Recently, automated co-design of machine learning (ML) models and accelerator architectures has attracted significant attention from both the industry and academia. However, most co-design frameworks either explore a limited search space or employ suboptimal exploration techniques for simultaneous design decision investigations of the ML model and the accelerator. Furthermore, training the ML model and simulating the accelerator performance is computationally expensive. To address these limitations, this work proposes a novel neural architecture and hardware accelerator co-design framework, called CODEBench. It comprises two new benchmarking sub-frameworks, CNNBench and AccelBench, which explore expanded design spaces of convolutional neural networks (CNNs) and CNN accelerators. CNNBench leverages an advanced search technique, Bayesian Optimization using Second-order Gradients and Heteroscedastic Surrogate Model for Neural Architecture Search, to efficiently train a neural heteroscedastic surrogate model to converge to an optimal CNN architecture by employing second-order gradients. AccelBench performs cycle-accurate simulations for diverse accelerator architectures in a vast design space. With the proposed co-design method, called Bayesian Optimization using Second-order Gradients and Heteroscedastic Surrogate Model for Co-Design of CNNs and Accelerators, our best CNN–accelerator pair achieves 1.4% higher accuracy on the CIFAR-10 dataset compared to the state-of-the-art pair while enabling 59.1% lower latency and 60.8% lower energy consumption. On the ImageNet dataset, it achieves 3.7% higher Top1 accuracy at 43.8% lower latency and 11.2% lower energy consumption. CODEBench outperforms the state-of-the-art framework, i.e., Auto-NBA, by achieving 1.5% higher accuracy and 34.7× higher throughput while enabling 11.0× lower energy-delay product and 4.0× lower chip area on CIFAR-10.