Fig 1.
The neural network architecture.
BN = batch normalization. DO = dropout.
Table 1.
Variable combination of input variables for 5 predictive models.
Fig 2.
RMSE loss value curve for the epochs while running the MLP regression.
Fig 3.
Mean and standard deviation of original grip strength (reference) and predicted values by gender from polynomial regressions (linear, quadratic, and cubic) and multi-layer perceptron (MLP) regression.
Fig 4.
Predicted grip strength of males by multi-layer perceptron (MLP) and polynomial regressions (linear, quadratic, and cubic).
Model 1 (including all variables) was considered.
Fig 5.
Predicted grip strength of females by multi-layer perceptron (MLP) and polynomial regressions (linear, quadratic, and cubic).
Model 1 (including all variables) was considered.
Table 2.
Comparison of the performances between the polynomial regressions (linear, quadratic, and cubic) and MLP regression.
Fig 6.
RMSE (N) of grip strength by MLP and polynomial regressions (linear, quadratic, and cubic).
Fig 7.
The standard deviation of grip strength (N) by the individual participant.