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Crash Signal Processing and Injury Calculation with Python

30 Chapters
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01
Crash signal acquisition fundamentals
transducer types, sampling rates, anti-aliasing filters, and data logger configuration for vehicle crash tests.
acquisitiontransducer
02
Python environment for crash data analysis
NumPy, SciPy, pandas, and matplotlib setup; loading common crash data formats (ISO MME, DIAdem, CSV) and ensuring reproducible workflows.
environmentanalysis
03
Signal conditioning and preprocessing
offset correction, drift removal, baseline zeroing, and unit conversion for accelerometers and load cells.
conditioningpreprocessing
04
Digital filtering for crash signals
Butterworth, Chebyshev, and SAE J211 CFC filters; zero-phase filtering with filtfilt and filter order selection.
filteringButterworth
05
Time-domain analysis of crash pulses
peak acceleration, pulse duration, rise time, and crash severity metrics (delta-v, ASI).
Time-domainanalysis
06
Frequency-domain analysis
FFT, power spectral density, and spectrograms to identify structural resonances and sensor noise in crash data.
Frequency-domainanalysis
07
Integration and differentiation of crash signals
velocity and displacement from acceleration; drift mitigation and boundary conditions.
Integrationdifferentiation
08
Occupant kinematics from vehicle acceleration
sled test data processing, relative displacement, and restraint system interaction.
Occupantkinematics
09
Head injury criteria calculation
HIC15 and HIC36 implementation, windowing, and sensitivity to filter class per SAE J211.
criteriacalculation
10
Chest injury metrics
chest deflection, V*C (viscous criterion), and chest acceleration limits; processing rib deflection sensor data.
deflectioncriterion
11
Neck injury criteria
Nij, Nkm, and beam element models; calculating axial force and moment from load cell data.
criteriacalculating
12
Abbreviated Injury Scale (AIS) and injury risk curves
mapping calculated metrics to AIS severity and probability of injury.
AbbreviatedAIS
13
Pelvis and lower extremity injury measures
femur force, tibia index, and foot acceleration; processing load cell and accelerometer data.
extremitymeasures
14
Crash pulse characterization and delta-v calculation
windowing, integration methods, and comparison with crash test standards.
characterizationcalculation
15
Barrier and sled test data processing
rigid barrier, offset deformable barrier, and pole impact; handling multiple sensor channels.
processingdeformable
16
Signal alignment and synchronization
cross-correlation, time-shift correction, and merging data from multiple loggers.
alignmentsynchronization
17
Outlier detection and data quality assessment
identifying dropouts, saturation, and sensor failures; robust statistical methods.
detectionassessment
18
Uncertainty and error propagation in injury metrics
Monte Carlo simulation and sensitivity analysis using Python.
Uncertaintypropagation
19
Automated reporting and visualization
generating plots, tables, and summary reports with matplotlib and pandas; exporting to PDF/HTML.
Automatedreporting
20
Batch processing of crash test data
scripting pipelines for multiple tests, parallel processing, and database integration.
processingscripting
21
Real-world crash reconstruction
using vehicle acceleration data to estimate impact speed and delta-v; validation against event data recorder (EDR) outputs.
Real-worldreconstruction
22
Pedestrian and cyclist impact analysis
head impact time, wrap-around distance, and injury metrics from accelerometer and contact sensor data.
Pedestriananalysis
23
Child restraint system (CRS) performance evaluation
processing CRS accelerometer data and calculating injury criteria for Q-series dummies.
restraintCRS
24
Rear impact and whiplash analysis
neck injury criteria (NIC, Nkm) and seatback deflection measurement.
whiplashanalysis
25
Side impact specific processing
door intrusion, pole impact, and ES-2re dummy data; calculating rib deflection and abdominal injury metrics.
specificprocessing
26
Motorcycle and helmet impact testing
helmet linear and angular acceleration processing; calculating HIC and rotational injury metrics.
Motorcycleacceleration
27
Data-driven injury prediction
feature extraction from crash signals and machine learning models for injury severity classification.
Data-drivenprediction
28
Validation and verification of injury calculation code
unit tests, benchmark datasets, and comparison with commercial software (e.g., DIAdem, MATLAB).
Validationverification
29
Case study
full-scale frontal crash test processing from raw sensor data to final injury criteria report.
full-scaleprocessing
30
Capstone project
end-to-end crash signal processing and injury calculation pipeline for a provided dataset, including documentation and peer review.
Capstoneend-to-end