Deep Learning Technology Consolidates Wearable Sensor Data

The Top-Performer series highlights papers published by Aging that have generated a high Altmetric attention score. Altmetric scores, located at the top-left of trending Aging papers, provide an at-a-glance indication of the volume and type of online attention the research has received.

Read Aging’s Top 100 Altmetric papers.

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Wearable sensors (smartwatches, smartphones, and other devices) allow users to monitor some biomarkers of their own health with mobile biofeedback technology. In 2019, one-in-five adults in the United States reported regularly using a wearable fitness tracker or smartwatch. Since the COVID-19 pandemic, mobile downloads of health and home fitness apps have increased by 46%—in addition to a boom in the use of wearable sensors.

“Wearable device motion data have already been used for monitoring acute illnesses including detection of early signs of the outbreak of influenza-like illnesses [28] and COVID-19 [30, 34].” 

Large quantities of these data are being collected consistently from individual users. This potentially useful information is also being collected from large populations of people living in different countries, working in different occupations, with unique health statuses, and across multiple environmental seasons and stages of life. Wearable sensor data provides an opportunity to conduct large-scale studies that could lead to new global discoveries in aging and disease research.

“In fact, only mobile technology can support large-scale studies involving monitoring of early signs of a disease or measuring recovery rates, all requiring sampling more often than once per week.”

However, there are many databases for wearable sensor data and different manufacturers of wearable sensors, smartwatches, and mobile devices. In addition to inevitable inaccuracies, such as missing data, outliers, and even seasonal variation of physical activity, there are also varying measurements between devices of different manufacturers. These inaccuracies and variations create inconsistencies when comparing large-scale data from wearable sensor databases.

“We applied deep learning technology to systematically address these challenges.”

In 2021, researchers from Singapore’s Gero AI and Russia’s Moscow Institute of Physics and Technology authored a paper, published in Aging’s Volume 13, Issue 6, and entitled, “Deep longitudinal phenotyping of wearable sensor data reveals independent markers of longevity, stress, and resilience.” To date, this top-performing research paper has generated an Altmetric attention score of 43. 

The Study

“We trained and characterized a simple model that learns physical activity patterns from wearable devices, which are directly associated with morbidity risks on the population level.”

Three wearable sensor databases were used in this study: UK Biobank, NHANES, and Healthkit. Researchers collected wearable sensor data for physical activity (steps per minute) from 103,830 users over the course of one week and, among 2,599 users, up to two years of data were collected. The team trained and validated a deep learning neural network technology—the GeroSense Biological Age Acceleration (BAA) system—to extract health-associated features from the physical activity recordings.  

“GeroSense BAA model employs additional neural network components to address this domain shift problem to ensure learning device-independent representations of the input signal.”

Conclusion

“We demonstrate that deep neural networks trained to predict morbidity risk from wearable sensor data can provide a high-quality and cheap alternative for BAA determination.”

The researchers explained that the application and wide deployment of their GeroSense BAA system may provide the means to accurately monitor stress and resilience in response to environmental conditions and interventions among people in different populations, countries, and socio-economic groups. 

“We hope that future developments will lead to further applications of AI in geroscience research, public health, and policy decision-making.”

Click here to read the full research paper, published by Aging.

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