Optimizing Cardiovascular Health Monitoring with IoT-Enabled Sensors and AI: A Focus on Obesity-Induced Cardiovascular Risks in Young Adults
Abstract
:1. Introduction
- This study introduced an advanced noninvasive heart blood sensor with an accuracy rate as high as 73.33%. Compared to the existing system that only measures heart rate, pulse pressure, and systolic blood pressure [24], CI value prediction has been improved by 10%.
- Through statistical analysis, the study identifies optimal physiological parameters for evaluating cardiac function, demonstrating that these parameters can enhance prediction accuracy by approximately 20%.
- The application of data enhancement techniques resulted in CI value predictions exceeding 90.01% accuracy, which is 30% higher than current evaluation methods [25].
2. Materials and Methods
2.1. Collecting Data
2.2. Statistical Analysis of Physiological Parameters
2.3. Data Enhancement
- For HRs below the midpoint:
- b.
- For HRs above the midpoint:
Algorithm 1. Data Enhancement for CI Prediction. |
Input: HR, BMI, Additional Physiological Signals (SV, SVI, CO) Output: Preprocessed data for model, Significant parameters for CI prediction Optimal parameter combinations for accurate CI prediction #Step 1: Collect and preprocess the data 1.1 Collect HR, BMI, SV, SVI, CO data 1.2 Preprocess the data for model input #Step 2: Statistical analysis to find significant parameters 2.1 For each physiological parameter in [HR, BMI, SV, SVI, CO]: 2.1.1 Calculate correlation with CI 2.1.2 If p-value < threshold (0.05): 2.1.2.1 Assign higher weight to the parameter in the model #Step 3: Test different parameter combinations for CI prediction 3.1 Combinations to test: 3.1.1 HR + BMI 3.1.2 HR + BMI + 1 physiological signal (SV, SVI, or CO) 3.1.3 HR + BMI + 2 physiological signals 3.1.4 HR + BMI + 3 physiological signals 3.2 Goal: Find the minimum number of parameters for accurate CI prediction #Step 4: Conversion of HR to RGB Code Function: convert_HR_to_RGB(hr_value, min_hr, mid_hr, max_hr, min_rgb, mid_rgb, max_rgb) Input: hr_value, min_hr, mid_hr, max_hr, min_rgb, mid_rgb, max_rgb Output: target_rgb 4.1 If hr_value < mid_hr: 4.1.1 Normalize HR: norm_hr = (hr_value − min_hr)/(mid_hr − min_hr) 4.1.2 Compute target RGB: target_rgb = min_rgb + norm_hr * (mid_rgb − min_rgb) 4.2 Else: 4.2.1 Normalize HR: norm_hr = (hr_value − mid_hr)/(max_hr − mid_hr) 4.2.2 Compute target RGB: target_rgb = mid_rgb + norm_hr * (max_rgb − mid_rgb) 4.3 Return target_rgb |
2.4. Multivariable Linear Regression Models
Algorithm 2. Multivariable Linear Regression CI Prediction. |
#Define Model Input: Physiological parameters (X1, X2, …, Xn) Output: CI Equation: Y = β0 + β1X1 + β2X2 + … + βnXn Where: - Y = CI - β0 = Intercept - β1 to βn = Regression coefficients #Data Collection Input: Physiological parameters dataset (X1, X2, …, Xn) Output: Raw data for model training #Data Augmentation a. Increase data quantity and diversity using augmentation techniques b. Train the model on augmented data to enhance feature learning #Model Training a. Train the MLR model using the processed data b. Optimize model parameters (β0, β1, …, βn) for best fit using an optimization method such as Stochastic Gradient Descent (SGD). #Prediction Input: Physiological parameters (X1, X2, …, Xn) Output: Predicted CI Predicted CI = Y #Health Status Assessment a. If 2.5 ≤ Predicted CI ≤ 4: - Output: “Subject is healthy, good cardiac status.” b. Else: - Output: "Health risk detected, further medical examination needed." End Algorithm |
3. Results
3.1. Result of Statistical Analysis of Parameters
3.2. Result of MLR Models
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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10 min | 3 min | 10 min | 5 min | 20 min |
Sit and rest | Blood pressure | Paste electrodes | Sit and rest | Detection of cardiac hemodynamic parameters |
Parameter | Description | Formula | Reference Value |
---|---|---|---|
Stroke Volume (SV) (mL) | The volume of blood ejected by the heart per beat. | 70 mL | |
Stroke volume index (SVI) (mL/m2) | Stroke volume per body surface area. | 40–50 mL/m2 | |
Cardiac Output (CO) (L/min) | The amount of blood ejected by the left ventricle per minute. | 4.0–6.5 L/min | |
Cardiac Index (CI) (L/min/m2) | Cardiac output per minute per body surface area. | 3 L/min/m2 | |
End Diastolic Volume (EDV) (mL) | The volume of blood when the left ventricles are filled with oxygenated blood. | 108 + 24 mL | |
End Systolic Volume (ESV) (mL) | The amount of blood remaining in the ventricle after ejection. | 45 + 46 mL | |
Ejection Fraction % (EF%) | The ratio of the volume of blood ejected from each ventricle. | 65–70% |
Level | Range |
---|---|
Normal Value | 67% |
Low Risk | >50% |
Moderate Risk | 35–49% |
High Risk | <35% |
LV Dysfunction | <40% |
M ± SD | Min | Max | |
---|---|---|---|
Age | 21.72 ± 1.78 | 20 | 29 |
Height | 159.41 ± 5.21 | 147 | 171 |
Body Weight | 75.81 ± 8.76 | 62 | 103.9 |
BMI | 29.61 ± 3.42 | 24.22 | 40.59 |
Waist Circumference | 87.03 ± 9.45 | 70 | 122 |
Hip Circumference | 106.24 ± 8.9 | 94 | 155.5 |
Waist-to-Hip ratio | 0.82 ± 0.07 | 0.62 | 1.04 |
Parameters | Unit | Value (M ± SD) | r | p | |
---|---|---|---|---|---|
Body Weight | (kg) | 75.81 ± 8.76 | |||
Blood Pressure Values | SBP | (mmHg) | 111.83 ± 13.18 | 0.363 | 0.007 ** |
DBP | (mmHg) | 69.09 ± 11.32 | 0.068 | 0.625 | |
HR | (bpm) | 88.61 ± 13.48 | 0.228 | 0.097 | |
PP | (mmHg) | 42.74 ± 13.77 | 0.292 | 0.032 * | |
MAP | (mmHg) | 83.34 ± 10.05 | 0.209 | 0.129 |
Body Weight | SBP | DBP | HR | PP | MAP | |
---|---|---|---|---|---|---|
Body Weight | 1 | 0.363 ** | 0.068 | 0.228 | 0.292 * | 0.209 |
SBP | 1 | 0.376 ** | 0.305 * | 0.648 ** | 0.719 ** | |
DBP | 1 | −0.076 | −0.462 ** | 0.915 ** | ||
HR | 1 | 0.355 ** | 0.076 | |||
PP | 1 | −0.063 | ||||
MAP | 1 |
Parameters | Unit | Value (M ± SD) | r | p | |
---|---|---|---|---|---|
Body Weight | (W) | (kg) | 75.81 ± 8.76 | ||
Cardiac Hemodynamic | SV | (mL) | 77.48 ± 8.9 | 0.234 | 0.089 |
SVI | (mL/m2) | 42.14 ± 5.11 | −0.31 | 0.023 * | |
CO | (L/min) | 6.61 ± 1.1 | 0.238 | 0.084 | |
CI | (L/min/m2) | 3.59 ± 0.59 | −0.153 | 0.27 | |
VET | (ms) | 320.55 ± 45.55 | 0.034 | 0.809 | |
EDV | (mL) | 123.01 ± 16.98 | 0.381 | 0.004 ** | |
ESV | (mL) | 43.56 ± 11.97 | 0.35 | 0.009 ** | |
EF% | (%) | 64.75 ± 6.41 | −0.213 | 0.121 |
Body Weight | SV | SVI | CO | CI | VET | EDV | ESV | EF% | |
---|---|---|---|---|---|---|---|---|---|
Body Weight | 1 | 0.234 | −0.310 * | 0.238 | −0.153 | 0.034 | 0.381 ** | 0.350 ** | −0.213 |
SV | 1 | 0.837 ** | 0.529 ** | 0.445 ** | −0.022 | 0.601 ** | 0.103 | 0.353 ** | |
SVI | 1 | 0.371 ** | 0.521 ** | −0.035 | 0.361 ** | −0.097 | 0.458 ** | ||
CO | 1 | 0.914 ** | −0.204 | 0.163 | −0.292 * | 0.593 ** | |||
CI | 1 | −0.214 | 0.002 | −0.445 ** | 0.694 ** | ||||
VET | 1 | 0.177 | 0.205 | −0.132 | |||||
EDV | 1 | 0.796 ** | −0.395 ** | ||||||
ESV | 1 | −0.858 ** | |||||||
EF% | 1 |
Color Space Conversion | ||||
---|---|---|---|---|
HR | CIELAB [28] | RGB | LM [25] | |
Accuracy | 50.00% | 45.00% | 60.00% | 53.33% |
ER | 21.88% | 24.67% | 18.98% | 19.71% |
R2 | 0.06933 | 0.10728 | 0.062188 | 0.11772 |
RMSE | 0.74238 | 0.79957 | 0.51346 | 0.73299 |
HR + Any Parameter | HR + Any 2 Parameters | HR + 3 Parameters | |||||||
---|---|---|---|---|---|---|---|---|---|
HR + SV | HR + SVI | HR + CO | Average | HR + SVI + CO | HR + SV + CO | HR + SV + SVI | Average | HR + SV + SVI + CO | |
Accuracy | 20.00% | 27.00% | 33.00% | 26.67% | 53.33% | 40.00% | 33.33% | 42.22% | 100.00% |
ER | 2.09% | 5.27% | 4.12% | 3.83% | 1.90% | 0.15% | 0.46% | 0.84% | 0.00% |
R2 | 0.34288 | 0.62239 | 0.88123 | 0.6155 | 0.94192 | 0.82482 | 0.52019 | 0.76231 | 0.99595 |
RMSE | 0.60441 | 0.39152 | 0.22015 | 0.40536 | 0.22459 | 0.21733 | 0.38391 | 0.275276667 | 0.03 |
Fold 1 | Fold 2 | Fold 3 | Fold 4 | Fold 5 | Average | |
---|---|---|---|---|---|---|
RMSE | 0.17 | 0.06 | 0.11 | 0.14 | 0.17 | 0.13 |
R2 | 0.97 | 0.99 | 0.97 | 0.93 | 0.88 | 0.95 |
Accuracy | ER | R2 | RMSE | ||
---|---|---|---|---|---|
HR + co | Original | 33.00% | 4.12% | 0.88123 | 0.22015 |
LM [25] | 60.00% | 23.06% | 0.0046979 | 0.69737 | |
This work | 73.33% | 0.41% | 0.9839 | 0.07367 | |
HR + svi + co | Original | 53.33% | 1.90% | 0.94192 | 0.22459 |
LM [25] | 60.00% | 11.08% | 0.47657 | 0.37362 | |
This work | 90.01% | 0.56% | 0.98705 | 0.07668 | |
HR + sv + svi + co | Original | 100.00% | 0.00% | 0.99595 | 0.03 |
LM [25] | 100.00% | 0.00% | 0.99572 | 0.03 | |
This work | 100.00% | 0.00% | 0.99692 | 0.02 |
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Chan, M.; Yu, Y.; Chang, P.; Chen, T.-Y.; Wong, H.-L.; Huang, J.-H.; Zhang, W.; Chen, S.-L. Optimizing Cardiovascular Health Monitoring with IoT-Enabled Sensors and AI: A Focus on Obesity-Induced Cardiovascular Risks in Young Adults. Electronics 2025, 14, 121. https://doi.org/10.3390/electronics14010121
Chan M, Yu Y, Chang P, Chen T-Y, Wong H-L, Huang J-H, Zhang W, Chen S-L. Optimizing Cardiovascular Health Monitoring with IoT-Enabled Sensors and AI: A Focus on Obesity-Induced Cardiovascular Risks in Young Adults. Electronics. 2025; 14(1):121. https://doi.org/10.3390/electronics14010121
Chicago/Turabian StyleChan, Meiling, Ying Yu, Pohan Chang, Tsung-Yi Chen, Hok-Long Wong, Jian-Hua Huang, Wiping Zhang, and Shih-Lun Chen. 2025. "Optimizing Cardiovascular Health Monitoring with IoT-Enabled Sensors and AI: A Focus on Obesity-Induced Cardiovascular Risks in Young Adults" Electronics 14, no. 1: 121. https://doi.org/10.3390/electronics14010121
APA StyleChan, M., Yu, Y., Chang, P., Chen, T.-Y., Wong, H.-L., Huang, J.-H., Zhang, W., & Chen, S.-L. (2025). Optimizing Cardiovascular Health Monitoring with IoT-Enabled Sensors and AI: A Focus on Obesity-Induced Cardiovascular Risks in Young Adults. Electronics, 14(1), 121. https://doi.org/10.3390/electronics14010121