REVIEW 16 cited by
Wearable Accelerometer Foundation Models for Health via Knowledge Distillation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Wearable Accelerometer Foundation Models for Health via Knowledge Distillation
read the original abstract
Modern wearable devices can conveniently record various biosignals in the many different environments of daily living, enabling a rich view of individual health. However, not all biosignals are the same: high-fidelity biosignals, such as photoplethysmogram (PPG), contain more physiological information, but require optical sensors with a high power footprint. Alternatively, a lower-fidelity biosignal such as accelerometry has a significantly smaller power footprint and is available in almost any wearable device. While accelerometry is widely used for activity recognition and fitness, it is less explored for health biomarkers and diagnosis. Here, we show that an accelerometry foundation model can predict a wide variety of health targets. To achieve improved performance, we distill representational knowledge from PPG encoders to accelerometery encoders using 20 million minutes of unlabeled data, collected from ~172K participants in the Apple Heart and Movement Study under informed consent. We observe strong cross-modal alignment on unseen data, e.g., 99.2% top-1 accuracy for retrieving PPG embeddings from accelerometry embeddings. We show that distilled accelerometry encoders have significantly more informative representations compared to self-supervised or supervised encoders trained directly on accelerometry data, observed by at least 23%-49% improved performance for predicting heart rate and heart rate variability. We also show that distilled accelerometry encoders are readily predictive of a wide array of downstream health targets, i.e., they are generalist foundation models. We believe accelerometry foundation models for health may unlock new opportunities for developing digital biomarkers from any wearable device.
Forward citations
Cited by 16 Pith papers
-
BCG-FM: A Foundation Model for Ambient Cardiac Health Sensing
BCG-FM, the first foundation model for ambient BCG, achieves 3.26-year MAE on biological age estimation and discriminates 15 health conditions using frozen embeddings from participant-level contrastive pretraining on ...
-
Prediction of Challenging Behaviors Associated with Profound Autism in a Classroom Setting Using Wearable Sensors
Wearable accelerometry, EDA, and skin temperature data from 9 students with profound autism in real classrooms enables prediction of challenging behavior episodes up to 10 minutes in advance with AUC-ROC of 0.78 using...
-
OpenWatch: A Multimodal Benchmark for Hand Gesture Recognition on Smartwatches
OpenWatch provides the first open multimodal smartwatch gesture dataset and benchmark, with MixToken and NormWear-Lora methods reaching 90% F1-score using 223k parameters versus 66% for 136M-parameter foundation models.
-
Expanders Meet Reed-Muller: Easy Instances of Noisy k-XOR
Explicit near-optimal expanders exist for which noisy k-XOR is polynomial-time solvable, falsifying conjectures that expansion implies hardness.
-
OpenMHC: Accelerating the Science of Wearable Foundation Models
OpenMHC contributes the largest open-access consumer wearable dataset to date (67M hours, 11,894 participants), a standardized three-track benchmark, and the first open implementations of Apple WBM and Google LSM-2.
-
A robust PPG foundation model using multimodal physiological supervision
A PPG foundation model pretrained via multimodal ECG/respiratory contrastive sample selection on ICU data improves performance on 14 of 15 downstream tasks including field-like data while using 3x fewer subjects.
-
AURORA: Contextual Orthogonalization for Geometric Representation Learning in Healthcare Foundation Models
AURORA is a representation learning framework that uses contextual orthogonalization and relational alignment to create disentangled, geometrically interpretable latent spaces in healthcare foundation models.
-
Prediction of Challenging Behaviors Associated with Profound Autism in a Classroom Setting Using Wearable Sensors
Wearable accelerometry, EDA, and temperature data from 9 students with profound autism, processed with fine-tuned foundation models, enables prediction of challenging behavior episodes up to 10 minutes in advance at A...
-
WavesFM: Hierarchical Representation Learning for Longitudinal Wearable Sensor Waveforms
WavesFM uses hierarchical SSL to pretrain a segment encoder on short waveforms followed by a temporal encoder on multi-day sequences, outperforming prior methods on 58 tasks after training on over 12 million hours of ...
-
Event Fields: Learning Latent Event Structure for Waveform Foundation Models
Event-centric waveform foundation models are learned via self-supervised consistency on latent event structures and interactions, yielding improved performance and label efficiency over sequence-based baselines on phy...
-
Emergent Symbolic Structure in Health Foundation Models: Extraction, Alignment, and Cross-Modal Transfer
Health foundation model embeddings contain an interpretable symbolic organization shared across modalities that supports cross-domain transfer without joint training.
-
Sonata: A Hybrid World Model for Inertial Kinematics under Clinical Data Scarcity
Sonata is a small hybrid world model pre-trained to predict future IMU states that outperforms autoregressive baselines on clinical discrimination, fall-risk prediction, and cross-cohort transfer while fitting on-devi...
-
Uncertainty-Aware Foundation Models for Clinical Data
The work introduces uncertainty-aware foundation models for clinical data by learning set-valued patient representations that enforce consistency across partial observations and integrate multimodal self-supervised ob...
-
WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records
WISTERIA learns robust clinical representations from noisy EHR labels by enforcing consistency across multiple weak supervision views plus ontology regularization.
-
Emergent Symbolic Structure in Health Foundation Models: Extraction, Alignment, and Cross-Modal Transfer
Independently trained PPG and accelerometer health foundation models share a linearly alignable subspace in which health-condition classifiers transfer with >95% of in-domain AUC.
-
Wearable AI in the Era of Large Sensor Models
Large Sensor Models trained on large-scale multimodal wearable data can provide a scalable, general framework for wearable AI by learning transferable representations across modalities and tasks.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.