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Non-invasive glucose estimation leaderboard

BIG IDEAs Lab Glycemic Variability and Wearable Device Data: RMSE (mg/dL)

4 reported results for non-invasive glucose estimation on BIG IDEAs Lab Glycemic Variability and Wearable Device Data, ranked by RMSE (mg/dL). Top reported…

This page ranks every result ChatPPG has recorded for non-invasive glucose estimation on BIG IDEAs Lab Glycemic Variability and Wearable Device Data, measured by Glucose RMSE (mg/dL) (lower is better). Each number is copied from the publication cited in its row, and ranks apply only within this dataset and metric.

Glucose estimation: BIG IDEAs Lab Glycemic Variability and Wearable Device Data, Glucose RMSE (mg/dL). Reported rankings; evaluation protocols may differ.
RankMethod & evaluationGlucose RMSE (mg/dL)↓ Lower is better
1XGBoost (Karunarathna)

XGBoost regression (Karunarathna & Liang 2025)

Evaluation details

236 wearable + circadian + demographic features, no food logs; random 80/20 split of 5-min epochs (same participants in train and test), mean of 20 random seeds. Not subject-independent, so not comparable with LOPOCV rows.

Reported as of: 2026-10-16

11.9
2Random forest (Karunarathna)

Random forest regression (Karunarathna & Liang 2025)

Evaluation details

236 wearable + circadian + demographic features, no food logs; random 80/20 split of 5-min epochs (same participants in train and test), mean of 20 random seeds. Not subject-independent, so not comparable with LOPOCV rows.

Reported as of: 2026-10-16

15.6
3Bent 2021 personalized

Personalized gradient boosting (Bent et al. 2021)

Evaluation details

Personalized gradient-boosted model: trained on the first half of each participant's own data, tested on the second half. MARD here is the paper's MAPE against Dexcom G6.

Source: Bent et al. 2021, npj Digit Med

Reported as of: 2026-10-16

21.1
4Bent 2021 population

Population gradient boosting, LOPOCV (Bent et al. 2021)

Evaluation details

Gradient-boosted model on Empatica E4 features + food logs, 5-min CGM targets; leave-one-person-out CV across 16 participants (mean over participants). MARD here is the paper's MAPE against Dexcom G6.

Source: Bent et al. 2021, npj Digit Med

Reported as of: 2026-10-16

21.22

Rows are ranked only within this task × dataset × metric. Protocols can differ; read each method's evaluation details before comparing. Latest reported row: 2026-10-16.

About the dataset

BIG IDEAs Lab Glycemic Variability and Wearable Device Data

Subjects
16
Signals
ppg, accel, skin-temp, eda, cgm
Licence
Open Data Commons Attribution License v1.0

Related leaderboards

Other metrics on BIG IDEAs Lab Glycemic Variability and Wearable Device Data

Non-invasive glucose estimation on other datasets

Data and submissions

Download every leaderboard as JSON or CSV. Missing a published result? Email hello@chatppg.com with the paper link and the exact table it comes from.

Cite this

Cite the page URL and the date you accessed it. Please also cite the original publications listed above.

APA

ChatPPG. (2026). BIG-IDEAS-GLYCEMIC RMSE (mg/dL) leaderboard: non-invasive glucose estimation [Data set]. ChatPPG Research Hub. https://chatppg.com/research/leaderboards/glucose-estimation/big-ideas-glycemic-rmse-mgdl

BibTeX
@misc{chatppg_glucose_estimation_big_ideas_glycemic_rmse_mgdl,
  author       = {{ChatPPG}},
  title        = {{BIG-IDEAS-GLYCEMIC RMSE (mg/dL) leaderboard: non-invasive glucose estimation}},
  year         = {2026},
  month        = oct,
  howpublished = {\url{https://chatppg.com/research/leaderboards/glucose-estimation/big-ideas-glycemic-rmse-mgdl}},
  note         = {ChatPPG Research Hub}
}
CITATION.cff
cff-version: 1.2.0
message: "If you use this resource, please cite it as below."
type: dataset
title: "BIG-IDEAS-GLYCEMIC RMSE (mg/dL) leaderboard: non-invasive glucose estimation"
authors:
  - name: "ChatPPG"
url: "https://chatppg.com/research/leaderboards/glucose-estimation/big-ideas-glycemic-rmse-mgdl"
date-released: "2026-10-16"