Selection of Noninvasive Features in Wrist-Based Wearable Sensors to Predict Blood Glucose Concentrations Using Machine Learning Algorithms
Open accessAbstract
Glucose monitoring technologies allow users to monitor glycemic fluctuations (e.g., blood glucose levels). This is particularly important for individuals who have diabetes mellitus (DM). Traditional self-monitoring blood glucose (SMBG) devices require the user to prick their finger and extract a blood drop to measure the blood glucose based on chemical reactions with the blood. Unlike traditional glucometer devices, noninvasive continuous glucose monitoring (NICGM) devices aim to solve these issues by consistently monitoring users’ blood glucose levels (BGLs) without invasively acquiring a sample. In this work, we investigated the feasibility of a novel approach to NICGM using multiple off-the-shelf wearable sensors and learning-based models (i.e., machine learning) to predict blood glucose. Two datasets were used for this study: (1) the OhioT1DM dataset, provided by the Ohio University; and (2) the UofM dataset, created by our research team. The UofM dataset consists of fourteen features provided by six sensors for studying possible relationships between glucose and noninvasive biometric measurements. Both datasets are passed through a machine learning (ML) pipeline that tests linear and nonlinear models to predict BGLs from the set of noninvasive features. The results of this pilot study show that the combination of fourteen noninvasive biometric measurements with ML algorithms could lead to accurate BGL predictions within the clinical range; however, a larger dataset is required to make conclusions about the feasibility of this approach.
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Skin temperature collected from wrist-worn wearables has been assessed for changes with hyperglycemic events in cohorts ranging from 10 to 20 individuals.
“Skin temperature was additionally collected from wrist-worn wearables was assessed for changes with hyperglycemic events in cohorts ranging from 10 to 20 individuals29–32.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [29]
Methods §2.1 identifies skin temperature as a feature measured by wristbands, and Results §3.1 assesses its relationship with glucose levels. But Methods §2.2 says the OhioT1DM dataset had 12 participants while this study evaluated only six; Discussion §4 says testing for hyperglycemic events remains future work. The source therefore does not support describing this as an assessment of skin-temperature changes during hyperglycemic events in a cohort of 10–20. (LLM, conf=0.88)
Experimental — accuracy not yet measuredChanges in statistical measures of distal body temperature can be used for identifying hypoglycemic events and predicting blood glucose concentration.
“These works analyzed how changes in statistical measures of distal body temperature can be used for identifying hypoglycemic events and prediction blood glucose concentration29–32.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [29]
The UofM Results (§3.2; page unavailable) report that skin temperature correlated with blood glucose in one subject, supporting its potential relevance to glucose prediction. The cited passages do not show that changes in statistical measures of distal body temperature identify hypoglycemic events; the paper instead evaluates several wearable-sensor features for predicting glucose concentration. (LLM, conf=0.85)
Experimental — accuracy not yet measuredPrior work was generated from small cohorts (the largest cohort of 70 individuals), raising questions of the generalizability of their findings.
“Second, prior work was generated from small cohorts (the largest cohort of 70 individuals), that raise questions of the generalizability of their findings5,22–32.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [29]
The Materials and Methods section reports that this paper’s UofM dataset had two participants and that the OhioT1DM dataset had twelve participants, supporting the characterization of this work as based on small cohorts. The Discussion proposes future work on a global rather than user-specific model. Our copy does not establish that 70 individuals was the largest cohort across the prior works cited in the sentence. (LLM, conf=0.85)
Experimental — accuracy not yet measuredPrior work chiefly assessed statistical aggregation functions of wearable data streams (primary features), which are nonspecific to the effects of DM within data streams that are constantly fluctuating in natural settings.
“Third, prior work chiefly assessed statistical aggregation functions of wearable data streams (primary features)5,26–32, which are nonspecific to the effects of DM within data streams that are constantly fluctuating in natural settings.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [29]
The Methods (§2.2) say the study averaged wearable-sensor readings with matching timestamps, supporting the aggregation aspect. But the paper chiefly evaluates sensor features for glucose prediction, not statistical aggregation functions, and it does not establish that those features are nonspecific to diabetes effects in naturally fluctuating data. The Discussion (§4) instead proposes more sophisticated feature extraction as future work. (LLM, conf=0.82)
Experimental — accuracy not yet measuredOur data is more representative of real-world performance than prior work because we captured a broader population segment with larger cohorts.
“While prior work reported higher performance, our data is more representative of real-world performance as we captured a broader population segment with our larger cohorts (balanced cohort is 5× and imbalanced cohort is 91× the largest prior cohort of wearable data)5,29–32.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [29]
Prior work typically uses less than a day of input.
“First, where prior work typically uses less than a day of input5,22–32, we assess the impact of longitudinal input windows ranging up to multiple weeks and demonstrate improved sensitivity with longer windows (AUROC increases by 0.15 from 1-night to 21-night models, in balanced and imbalanced cohorts).”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [29]
Neither prior literature was able to test their efficacy in large, real-world cohorts representing a practical assessment.
“Though interesting, neither literature was able to test their efficacy in large, real-world cohorts representing a practical assessment.”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [29]
The balanced cohort is 5 times and the imbalanced cohort is 91 times the largest prior cohort of wearable data.
“balanced cohort is 5× and imbalanced cohort is 91× the largest prior cohort of wearable data”
From Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysis (2026) · cites this paper as [29]
Section 2.2 describes the OhioT1DM and UofM datasets, including their participant counts, but does not identify a balanced or imbalanced cohort or compare either with the largest prior wearable-data cohort. Searches for the stated 5× and 91× comparisons found no passage addressing them. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Bogue-Jimenez, B., Huang, X., Powell, D., & Doblas, A.
- Affiliations
- University of Memphis (US)
- Corresponding author
- Doblas, Ana
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-05-06
- Volume
- 22
- Issue
- 9
- Pages
- 3534
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC9100498
- OpenAlex
- W4229372191
- Cited by
- 24
- References
- 36
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 7 · 2023: 4 · 2022: 1
- FWCI
- 1.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Herff College of Engineering at the University of Memphis
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- non-invasive glucose monitoring, blood glucose prediction, wrist-worn sensors, wearable sensors, diabetes mellitus, continuous glucose monitoring, feature selection, linear and nonlinear models
- MeSH terms
- Machine Learning, Wearable Electronic Devices, Algorithms, Blood Glucose, Glucose, Humans, Pilot Projects, Wrist, Blood Glucose Self-Monitoring
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