EXTRACT: Strong Examples from Weakly-Labeled Sensor Data

Blalock, Davis; Guttag, John V.

doi:10.1109/icdm.2016.0093

Cited by 5 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advent of wearable technologies has given individuals the opportunity to unobtrusively track everyday behaviour. Given the rapid growth in adoption of Internet-enabled wearable devices, sensor time-series comprise a considerable amount of user-generated data [137]. However, extracting meaning from these data can be challenging, since sensors measure low-level signals (e.g.…”

Section: Ai For Mobile Health—case Studiesmentioning

confidence: 99%

Human-centred artificial intelligence for mobile health sensing: challenges and opportunities

Dang,

Spathis,

Ghosh

et al. 2023

R. Soc. open sci.

View full text Add to dashboard Cite

Advances in wearable sensing and mobile computing have enabled the collection of health and well-being data outside of traditional laboratory and hospital settings, paving the way for a new era of mobile health. Meanwhile, artificial intelligence (AI) has made significant strides in various domains, demonstrating its potential to revolutionize healthcare. Devices can now diagnose diseases, predict heart irregularities and unlock the full potential of human cognition. However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints. Despite the recognition of the value of mobile sensing, leveraging these datasets has lagged behind other areas of ML. Furthermore, obtaining quality annotations and ground truth for such data is often expensive or impractical. While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling. This paper explores the challenges and opportunities of human-centred AI for mobile health, focusing on key sensing modalities such as audio, location and activity tracking. We discuss the limitations of current approaches and propose potential solutions.

show abstract

Section: Ai For Mobile Health—case Studiesmentioning

confidence: 99%

Human-centred artificial intelligence for mobile health sensing: challenges and opportunities

Dang,

Spathis,

Ghosh

et al. 2023

R. Soc. open sci.

View full text Add to dashboard Cite

show abstract

“…(Recall that each column is one variable of the time series). If all columns require 0 bits, Sprintz continues reading in blocks until some error requires >0 bits (lines [11][12][13]. At this point, it writes out a header of all 0s and then the number of all-zero blocks.…”

Section: Overviewmentioning

confidence: 99%

“…This makes dictionary-based methods a natural fit. In time series, however, the presence of noise makes exact repeats less common [11,57]. (2) Multiple variables.…”

Section: Data Characteristicsmentioning

confidence: 99%

Sprintz

Blalock

Madden

Guttag

2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

View full text Add to dashboard Cite

Thanks to the rapid proliferation of connected devices, sensor-generated time series constitute a large and growing portion of the world's data. Often, this data is collected from distributed, resource-constrained devices and centralized at one or more servers. A key challenge in this setup is reducing the size of the transmitted data without sacrificing its quality. Lower quality reduces the data's utility, but smaller size enables both reduced network and storage costs at the servers and reduced power consumption in sensing devices. A natural solution is to compress the data at the sensing devices. Unfortunately, existing compression algorithms either violate the memory and latency constraints common for these devices or, as we show experimentally, perform poorly on sensor-generated time series.We introduce a time series compression algorithm that achieves state-of-the-art compression ratios while requiring less than 1KB of memory and adding virtually no latency. This method is suitable not only for low-power devices collecting data, but also for servers storing and querying data; in the latter context, it can decompress at over 3GB/s in a single thread, even faster than many algorithms with much lower compression ratios. A key component of our method is a high-speed forecasting algorithm that can be trained online and significantly outperforms alternatives such as delta coding.Extensive experiments on datasets from many domains show that these results hold not only for sensor data but also across a wide array of other time series.

show abstract

“…The advent of wearable technologies has given individuals the opportunity to unobtrusively track everyday behavior. Given the rapid growth in adoption of internet-enabled wearable devices, sensor time-series comprise a considerable amount of user-generated data (Blalock and Guttag 2016). However, extracting meaning from this data can be challenging, since sensors measure low-level signals (e.g., acceleration) as opposed to the more high-level events that are usually of interest (e.g., arrhythmia, infection or obesity onset).…”

Section: Introductionmentioning

confidence: 99%

Self-supervised transfer learning of physiological representations from free-living wearable data

Spathis

Perez-Pozuelo

Brage

et al. 2021

Proceedings of the Conference on Health, Inference, and Learning

View full text Add to dashboard Cite

Wearable devices such as smartwatches are becoming increasingly popular tools for objectively monitoring physical activity in freeliving conditions. To date, research has primarily focused on the purely supervised task of human activity recognition, demonstrating limited success in inferring high-level health outcomes from low-level signals. Here, we present a novel self-supervised representation learning method using activity and heart rate (HR) signals without semantic labels. With a deep neural network, we set HR responses as the supervisory signal for the activity data, leveraging their underlying physiological relationship. In addition, we propose a custom quantile loss function that accounts for the long-tailed HR distribution present in the general population.We evaluate our model in the largest free-living combined-sensing dataset (comprising >280k hours of wrist accelerometer & wearable ECG data). Our contributions are two-fold: i) the pre-training task creates a model that can accurately forecast HR based only on cheap activity sensors, and ii) we leverage the information captured through this task by proposing a simple method to aggregate the learnt latent representations (embeddings) from the window-level to user-level. Notably, we show that the embeddings can generalize in various downstream tasks through transfer learning with linear classiers, capturing physiologically meaningful, personalized information. For instance, they can be used to predict variables associated with individuals' health, tness and demographic characteristics (AUC >70), outperforming unsupervised autoencoders and common bio-markers. Overall, we propose the rst multimodal self-supervised method for behavioral and physiological data with implications for large-scale health and lifestyle monitoring. Code: https://github.com/sdimi/Step2heart.

show abstract

EXTRACT: Strong Examples from Weakly-Labeled Sensor Data

Cited by 5 publications

References 24 publications

Human-centred artificial intelligence for mobile health sensing: challenges and opportunities

Human-centred artificial intelligence for mobile health sensing: challenges and opportunities

Sprintz

Self-supervised transfer learning of physiological representations from free-living wearable data

Contact Info

Product

Resources

About