arXiv subjects

Sean Jewell

Publications and source records attributed to Sean Jewell.

5 recordsLinked to original sources

arXiv2025

Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions

Wearable devices record physiological and behavioral signals that can improve health predictions. While foundation models are increasingly used for such predictions, they have been primarily applied to low-level sensor data, despite behavioral data often being more informative due to their alignment with physiologically relevant timescales and quantities. We develop foundation models of such behavioral signals using over 2.5B hours of wearable data from 162K individuals, systematically optimizing architectures and tokenization strategies for this unique dataset. Evaluated on 57 health-related tasks, our model shows strong performance across diverse real-world applications including individual-level classification and time-varying health state prediction. The model excels in behavior-driven tasks like sleep prediction, and improves further when combined with representations of raw sensor data. These results underscore the importance of tailoring foundation model design to wearables and demonstrate the potential to enable new health applications.

cs.LG↗

arXiv2022

Latent Temporal Flows for Multivariate Analysis of Wearables Data

Increased use of sensor signals from wearable devices as rich sources of physiological data has sparked growing interest in developing health monitoring systems to identify changes in an individual's health profile. Indeed, machine learning models for sensor signals have enabled a diverse range of healthcare related applications including early detection of abnormalities, fertility tracking, and adverse drug effect prediction. However, these models can fail to account for the dependent high-dimensional nature of the underlying sensor signals. In this paper, we introduce Latent Temporal Flows, a method for multivariate time-series modeling tailored to this setting. We assume that a set of sequences is generated from a multivariate probabilistic model of an unobserved time-varying low-dimensional latent vector. Latent Temporal Flows simultaneously recovers a transformation of the observed sequences into lower-dimensional latent representations via deep autoencoder mappings, and estimates a temporally-conditioned probabilistic model via normalizing flows. Using data from the Apple Heart and Movement Study (AH&MS), we illustrate promising forecasting performance on these challenging signals. Additionally, by analyzing two and three dimensional representations learned by our model, we show that we can identify participants' $\text{VO}_2\text{max}$, a main indicator and summary of cardio-respiratory fitness, using only lower-level signals. Finally, we show that the proposed method consistently outperforms the state-of-the-art in multi-step forecasting benchmarks (achieving at least a $10\%$ performance improvement) on several real-world datasets, while enjoying increased computational efficiency.

cs.LG↗

arXiv2019

Testing for a Change in Mean After Changepoint Detection

While many methods are available to detect structural changes in a time series, few procedures are available to quantify the uncertainty of these estimates post-detection. In this work, we fill this gap by proposing a new framework to test the null hypothesis that there is no change in mean around an estimated changepoint. We further show that it is possible to efficiently carry out this framework in the case of changepoints estimated by binary segmentation and its variants, $\ell_{0}$ segmentation, or the fused lasso. Our setup allows us to condition on much less information than existing approaches, which yields higher powered tests. We apply our proposals in a simulation study and on a dataset of chromosomal guanine-cytosine content. These approaches are freely available in the R package ChangepointInference at https://jewellsean.github.io/changepoint-inference/.

stat.ME↗

arXiv2018

Fast Nonconvex Deconvolution of Calcium Imaging Data

Calcium imaging data promises to transform the field of neuroscience by making it possible to record from large populations of neurons simultaneously. However, determining the exact moment in time at which a neuron spikes, from a calcium imaging data set, amounts to a non-trivial deconvolution problem which is of critical importance for downstream analyses. While a number of formulations have been proposed for this task in the recent literature, in this paper we focus on a formulation recently proposed in Jewell and Witten (2017) which has shown initial promising results. However, this proposal is slow to run on fluorescence traces of hundreds of thousands of timesteps. Here we develop a much faster online algorithm for solving the optimization problem of Jewell and Witten (2017) that can be used to deconvolve a fluorescence trace of 100,000 timesteps in less than a second. Furthermore, this algorithm overcomes a technical challenge of Jewell and Witten (2017) by avoiding the occurrence of so-called "negative" spikes. We demonstrate that this algorithm has superior performance relative to existing methods for spike deconvolution on calcium imaging datasets that were recently released as part of the spikefinder challenge (http://spikefinder.codeneuro.org/). Our C++ implementation, along with R and python wrappers, is publicly available on Github at https://github.com/jewellsean/FastLZeroSpikeInference.

stat.ME↗

arXiv2017

Exact Spike Train Inference Via $\ell_0$ Optimization

In recent years, new technologies in neuroscience have made it possible to measure the activities of large numbers of neurons simultaneously in behaving animals. For each neuron, a fluorescence trace is measured; this can be seen as a first-order approximation of the neuron's activity over time. Determining the exact time at which a neuron spikes on the basis of its fluorescence trace is an important open problem in the field of computational neuroscience. Recently, a convex optimization problem involving an $\ell_1$ penalty was proposed for this task. In this paper, we slightly modify that recent proposal by replacing the $\ell_1$ penalty with an $\ell_0$ penalty. In stark contrast to the conventional wisdom that $\ell_0$ optimization problems are computationally intractable, we show that the resulting optimization problem can be efficiently solved for the global optimum using an extremely simple and efficient dynamic programming algorithm. Our R-language implementation of the proposed algorithm runs in a few minutes on fluorescence traces of $100,000$ timesteps. Furthermore, our proposal leads to substantial improvements over the previous $\ell_1$ proposal, in simulations as well as on two calcium imaging data sets. R-language software for our proposal is available on CRAN in the package LZeroSpikeInference. Instructions for running this software in python can be found at https://github.com/jewellsean/LZeroSpikeInference.

stat.AP↗