FOCUS: A Foundation Model for Wearable A-Mode Ultrasound

Wearable A-mode ultrasound (AUS) enables continuous, non-invasive monitoring of deep tissue, and advances in conformable transducers and low-power electronics have enabled increasingly large and diverse wearable datasets. Because wearable probes communicate over bandwidth-limited wireless links that cannot stream raw echo data continuously, processing must largely run on the device, calling for compact models that can be adapted to each user with limited labeled data. However, learning approaches for wearable AUS are developed for individual tasks, subjects, or acquisition systems and trained from scratch on small datasets, and no foundation model (FM) exists for this setting. We introduce FOCUS, a compact FM for wearable AUS (6.3 M encoder parameters), pre-trained on 24.7 million unlabeled A-mode scans from five wearable and benchtop acquisition systems and multiple body locations, including the neck, upper and lower limbs. A frequency-aware multi-tokenizer maps raw signals acquired at different sampling frequencies to patches spanning an approximately constant physical depth. We evaluate FOCUS on eight subject-specific downstream tasks from five wearable datasets, spanning hand-gesture, forearm-position, and gait-phase classification, as well as upper- and lower-limb kinematic regression. Pre-training improves mean performance over the same architecture trained from scratch on all eight tasks, and fine-tuning with only 40% of the labeled training data outperforms training from scratch with the complete labeled dataset on all eight. FOCUS also outperforms the previously reported results on seven of the eight benchmarks. Across three model capacities (0.8 M, 6.3 M, and 85 M parameters), downstream performance generally improves with model size, and larger models benefit more consistently from additional pre-training data. FOCUS requires 669–856 MMACs per channel update under sequential inference, a compute budget compatible with future on-device deployment for wearable AUS.