Windows into human health through wearables data analytics

Abstract

Background: Wearable sensors (wearables) have been commonly integrated into a wide variety of commercial products and are increasingly being used to collect and process raw physiological parameters into salient digital health information. The data collected by wearables are currently being investigated across a broad set of clinical domains and patient populations. There is significant research occurring in the domain of algorithm development, with the aim of translating raw sensor data into fitness-or health-related outcomes of interest for users, patients, and health care providers. Objectives: The aim of this review is to highlight a selected group of fitness-and health-related indicators from wearables data and to describe several algorithmic approaches used to generate these higher order indicators. Methods: A systematic search of the Pubmed database was performed with the following search terms (number of records in parentheses): Fitbit algorithm (18), Apple Watch algorithm (3), Garmin algorithm (5), Microsoft Band algorithm (8), Samsung Gear algorithm (2), Xiaomi MiBand algorithm (1), Huawei Band (Watch) algorithm (2), photoplethysmography algorithm (465), accelerometry algorithm (966), ECG algorithm (8287), continuous glucose monitor algorithm (343). The search terms chosen for this review are focused on algorithms for wearable devices that dominated the commercial wearables market between 2014-2017 and that were highly represented in the biomedical literature. A second set of search terms included categories of algorithms for fitness-related and health-related indicators that are commonly used in wearable devices (e.g. accelerometry, PPG, ECG). These papers covered the following domain areas: fitness; exercise; movement; physical activity; step count; walking; running; swimming; energy expenditure; atrial fibrillation; arrhythmia; cardiovascular; autonomic nervous system; neuropathy; heart rate variability; fall detection; trauma; behavior change; diet; eating; stress detection; serum glucose monitoring; continuous glucose monitoring; diabetes mellitus type 1; diabetes mellitus type 2. All studies uncovered through this search on commercially available device algorithms and

Investigating sources of inaccuracy in wearable optical heart rate sensors

“…Measured factors included skin tone, gender, body fat percentage, weight, height, waist circumference, and sun exposure habits, chosen based on scientific literature and anecdotal evidence of their potential effects on wrist-based optical HR sensing accuracy. Subjective analysis of skin tone using the FP skin tone scale (1)(2)(3)(4)(5)(6) and the von Luschan skin tone scale were taken in addition to an objective measurement of skin tone on the wrist using a spectrophotometer (Linksquare, Stratio Inc., San Jose, CA).…”

“…In order to examine the effect of lag time on our model, we iterated through rolling windows of 5,10,20,30,40,50,60,90,120,150,180,210, 240 s for each participant, each device, and each condition (rest or activity). We found the optimal window length of MAE and MDE by determining the window length that minimized the MAE and MDE, respectively.…”

“…The lack of clarity surrounding the verification and validation procedures and the unknown reliability of the data generated by these wearable technologies poses significant challenges for their adoption in research and healthcare applications. [4][5][6] Recently, the accuracy of wearable optical heart rate (HR) measurements using photoplethysmography (PPG) has been questioned extensively. [7][8][9][10][11][12][13] Wearables manufacturers sometimes report some expected sources of error, but the reporting and evaluation methods are inconsistent [14][15][16][17][18][19][20][21][22] (Table 1).…”

Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs)

“…Analytical validation involves evaluation of a BioMeT for generating physiological-and behavioral metrics. This involves evaluation of the processed data and requires testing with human subjects 24 . After verified sample-level data have been generated by a BioMeT, algorithms are applied to these data in order to create behaviorally or physiologically meaningful metrics, such as estimated sleep time, oxygen saturation, heart rate variability, or gait velocity.…”

Augmentation of Dispersion Entropy for Handling Missing and Outlier Samples in Physiological Signal Monitoring

“…With the advancement of physiological recording technology deployed across a broad spectrum of applications, from wearable devices to intensive care units, increased amounts of data are becoming available for analysis [ 1 , 2 ]. While derived information can aid medical decision making, leading to personalised and prompt treatments, the successful implementation of data analysis algorithms is limited by challenges arising from the quality of recorded data due to the increased amount of missing and outlier samples, which are common occurrences due to user movement, loose equipment attachment, and electromagnetic interference [ 3 , 4 , 5 ].…”