## 2 Methodology  
The initial step to build the system involves designing a cost-effective dummy that preserves the segmental mass distribution and center-of-mass locations characteristic of human surrogates. Additionally, the dummy should feature a modular design for ease of assembly and disassembly, while maintaining low manufacturing costs. The primary focus of this study is the head-neck region of the dummy. The head-neck structure consists of alternating aluminum vertebral analogs and silicone intervertebral discs, secured by latex straps simulating ligaments. This design enables basic mobility and injury recognition. Although the segmental mass and center of gravity of the simplified dummy were designedto approximate those of the Hybrid III 50 th percentile model, notable differences persist due to the use of cost-effective materials and the omission of moment of inertia considerations. To address these discrepancies, a mapping model based on 1D-CNN is developed. In the second step, the 1D-CNN network is trained to address the discrepancy between the simplified dummy and the high-fidelity dummy. While the simplified dummy replicates segmental mass and center-of-mass distributions, its response still deviates from that of the high-bifidelity dummy due to the use of low-cost materials and simplified joint connections. The mapping model aims to minimize this deviation. The training dataset for the model was obtained through a repeatable and controlled pendulum experiment. Section Relationship Between the Pendulum and Explosive Impact investigates the relationship between pendulum impact and simulated explosions using empirical formulas for blast overpressure. An overview of the model-training procedure is presented as follows: As illustrated in Figure 1, loading is applied to the pedal of the simplified dummy using the pendulum. Acceleration signals from the chest and head of the simplified dummy serve as inputs for the 1D-CNN, while the signals from the accelerometer mounted on the pedal are used as inputs for the Hybrid III FEM. The calibrated head acceleration and upper neck axial force are derived from the computations performed by the 1D-CNN. A comparison is made with the results from the Hybrid III model, and the error is computed. If the error is within acceptable limits, the optimal parameters of the modified model are retained. Otherwise, NSGA-II generates a new set of parameters (CNN's filters and biases), and the process is repeated. The model is ultimately validated using TNT explosion test. The simple dummy tests the response in an explosive environment, and after the network with training parameters calculates and outputs the calibration value. The accelerometer signals from the test rig are used as inputs for the Hybrid III FEM, with the Hybrid III FEM serving as the reference. Although the simple dummy is not equipped with a neck force transducer, making it impossible to test neck forces, the relative movement changes of the chest and head can reflect the changes in upper neck force. The mapping test model can map the acceleration of the chest and head to the axial compression force of the upper neck of Hybrid III 50 th FEM. Due to the inherent instability of energy release in explosive detonations, using explosive charge as input for finite element models introduces considerable uncertainty. As a result, this study focuses on the measured pedal acceleration as the key input parameter. Although using the pedal acceleration measured during testing as the input for the finite element model neglects the impact of local pedal deformation, since the head and neck do  
Figure 1: Workflow of the training process. not directly interact with the pedal, the effect of local pedal deformation on the head-neck response is negligible. In the practical application of this system. The composition of the test system is shown in Figure 2. The system includes a simple dummy (built-in head-neck physical model), accelerometers, signal conditioning and data collector, and special data acquisition and processing software.