Latent Temporal Flows for Multivariate Analysis of Wearables Data

by   Magda Amiridi, et al.

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' VO_2max, 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.


page 1

page 2

page 3

page 4


Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting

Probabilistic forecasting of high dimensional multivariate time series i...

Multivariate Temporal Autoencoder for Predictive Reconstruction of Deep Sequences

Time series sequence prediction and modelling has proven to be a challen...

Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows

Time series forecasting is often fundamental to scientific and engineeri...

Pattern Discovery in Time Series with Byte Pair Encoding

The growing popularity of wearable sensors has generated large quantitie...

CrossPyramid: Neural Ordinary Differential Equations Architecture for Partially-observed Time-series

Ordinary Differential Equations (ODE)-based models have become popular f...

An empirical evaluation of attention-based multi-head models for improved turbofan engine remaining useful life prediction

A single unit (head) is the conventional input feature extractor in deep...

Deep Canonical Correlation Alignment for Sensor Signals

Sensor technology is becoming increasingly prevalent across a multitude ...

Please sign up or login with your details

Forgot password? Click here to reset