Human Body as a Signal Transmission Medium for Body-Coupled Communication: Galvanic-Mode Models
- Present two electrical circuit models of a BCC system: one adapted from existing work (Model #1) and another that is simpler but similarly accurate (Model #2);
- Provide closed-form expressions of the transfer function in both circuit models;
- Apply linear regression and random forest regression methods to construct and evaluate multiple predictive models.
2. Related Work
3. Electrical Circuit Models
3.1. Problem Formulation
- Makes the system more energy-efficient and alleviates any health-related concerns by minimizing the electric current that enters the human body;
- Reduces the sophistication required from the detector on the Rx side, as higher amplitude signals are easier to register and decode without errors.
- Provide circuit models (Figure 1 and Figure 4) of the communication system that include the human body and the BCC equipment;
- Simplify and divide the models into parts to make it tractable to express the AFR with closed-form algebraic equations;
- Find nominal values of the components in the models using experimental AFR data as the ground truth.
3.2. Circuit Model #1
- G and — signal generator and its internal resistance;
- — resistances related to electrode and skin contacts;
- — the entry impedance of the signal detector;
- — impedances between the receiver and transmitter electrodes.
3.3. Circuit Model #2
3.4. Instantiation of the Models
3.5. Model #1 Instantiation
3.6. Model #2 Instantiation
4. Predictive Models
4.1. Problem Formulation
- Create a single model that takes the frequency as one of the input data features.
- Create a custom-fitted model for each of the measured frequencies.
4.2. Model Description
- Linear regression models;
- Random forest (RF) regression models.
4.3. Model Evaluation
- The linear regression model, with frequency as the input data, has larger errors as it fails to capture the complex, nonlinear relationship between the input variables and the signal loss.
- For the other models, the prediction error is the highest at the lower frequencies; this decreases up to 10 MHz, reaching or going below 2 dB RMSE, and then starts increasing again.
- If the frequency is fixed, linear regression shows a similar performance to RF.
- RF shows even better results if the frequency is passed as a feature, likely due to the larger training dataset size.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Measurement point variables|
|Distance between points||relative units|
|Tx point fat level||relative units|
|Rx point fat level||relative units|
|Human body variables|
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Aristov, V.; Elsts, A. Human Body as a Signal Transmission Medium for Body-Coupled Communication: Galvanic-Mode Models. Electronics 2023, 12, 4550. https://doi.org/10.3390/electronics12214550
Aristov V, Elsts A. Human Body as a Signal Transmission Medium for Body-Coupled Communication: Galvanic-Mode Models. Electronics. 2023; 12(21):4550. https://doi.org/10.3390/electronics12214550Chicago/Turabian Style
Aristov, Vladimir, and Atis Elsts. 2023. "Human Body as a Signal Transmission Medium for Body-Coupled Communication: Galvanic-Mode Models" Electronics 12, no. 21: 4550. https://doi.org/10.3390/electronics12214550