3. Injury metrics and criteria

Restraint injury analytics depends on a small set of validated physical metrics that translate raw accelerometer and load-cell signals into interpretable injury risk. This section covers the four metrics that dominate occupant restraint analysis: Head Injury Criterion (HIC), chest deflection, the Nij neck injury criterion, and the Abbreviated Injury Scale (AIS) used to code and aggregate outcomes. The goal is not merely to define each metric, but to show how each is computed from real channel data, what thresholds mean in practice, and how they combine into a dashboard-ready injury summary.

HIC: Head Injury Criterion

HIC is derived from the resultant translational acceleration at the head center of gravity, measured in g. The criterion searches all time windows of length up to 36 ms and returns the maximum of the windowed average acceleration scaled by the window duration:

HIC = max over (t1, t2), t2 - t1 <= 36 ms of:
      [ (1/(t2 - t1)) * integral(t1..t2) a(t) dt ]^2.5 * (t2 - t1)

where a(t) is the resultant head acceleration in g,
and t2 - t1 is expressed in seconds.

In practice, the integral is evaluated on the sampled signal using cumulative trapezoidal integration, and the window search is performed over every sample pair satisfying the 36 ms constraint. A common implementation detail that causes disagreement between tools is the treatment of the resultant: compute a(t) = sqrt(ax^2 + ay^2 + az^2) first, then integrate, rather than integrating each axis separately. Another frequent discrepancy is filtering — HIC is conventionally computed on data filtered per SAE J211 channel frequency class (CFC 1000 for head acceleration), and applying a different filter changes the result materially.

Thresholds commonly referenced in restraint analysis are HIC 700 (moderate injury risk boundary in many regulatory contexts) and HIC 1000 (historically associated with serious head injury). For a worked example, suppose a head resultant acceleration pulse produces a 15 ms window with a mean acceleration of 85 g. Then HIC = 85^2.5 × 0.015 ≈ 66,600 × 0.015 ≈ 999. That single calculation shows how sensitive HIC is to the exponent: a 10% increase in mean acceleration raises HIC by roughly 27%. Dashboards should therefore display the governing window (t1, t2) alongside the HIC value so analysts can see which portion of the pulse drove the result.

Chest Deflection and Chest Injury Criteria

Chest deflection is measured by a chest band or IR-TRACC (Infra-Red Telescoping Rod for Assessment of Chest Compression) and reported in millimeters of sternal or rib deflection. Two derived criteria matter: maximum chest deflection and the viscous criterion, VC = max(v(t) × C(t)), where v is the rate of chest deformation in m/s and C is the normalized compression (deflection divided by half chest depth). VC captures the rate-dependent soft-tissue injury that pure deflection magnitude misses.

For analytics, the critical implementation points are sign convention and normalization. Deflection is conventionally positive in compression; the normalization uses half the initial chest depth, so a 40 mm deflection on a 200 mm chest depth yields C = 0.40. A typical dashboard field set is: peak deflection (mm), time of peak (ms), VC max (m/s), and the chest depth used for normalization. If chest depth is not recorded per test, the normalization becomes an assumption that must be flagged, because it directly scales VC.

Nij: Neck Injury Criterion

Nij combines axial force and bending moment at the occipital condyles into a single normalized value. The four-quadrant formulation is:

Nij = Fz / Fzc + My / Myc

with quadrant-specific critical values, e.g.:
  tension + extension:  Fzc = 6800 N, Myc = 310 Nm
  tension + flexion:    Fzc = 6800 N, Myc = 310 Nm
  compression + extension: Fzc = 6160 N, Myc = 310 Nm
  compression + flexion:   Fzc = 6160 N, Myc = 310 Nm

(illustrative values; confirm against the applicable
 regulation or dummy-specific corridor before use)

The sign of Fz and My selects the quadrant, and the criterion is evaluated at every sample; the reported Nij is the maximum across the event. A value of 1.0 corresponds to the threshold. Because the formula is a linear sum of two normalized terms, a dashboard should always show the force and moment contributions separately — a Nij of 0.9 driven mostly by moment implies a different restraint tuning response than one driven by axial tension. Note that the critical values above are illustrative and must be replaced with the values from the governing regulation or dummy specification for the actual test program.

AIS Coding for Restraint Injury Analytics

The Abbreviated Injury Scale codes anatomical injuries on an ordinal severity scale from 1 (minor) to 6 (maximal, currently untreatable). AIS is not a metric computed from signals; it is assigned by trained coders from medical records, autopsy findings, or imaging. In restraint analytics, AIS serves as the outcome variable against which the physical metrics are correlated. The standard aggregation is the Maximum AIS (MAIS) per body region and the Injury Severity Score (ISS), computed as the sum of squares of the three highest MAIS values across six body regions.

A practical coding table for a restraint study typically records: body region, AIS code, AIS severity, injury descriptor, and the source document. For example, a rib fracture coded AIS 2 in the chest region and a pulmonary contusion coded AIS 3 would give a chest MAIS of 3. When joining AIS outcomes to metric data, the join key must be the test ID and occupant position, and the analyst must decide how to handle occupants with no injury (AIS 0) — these are essential negative cases and must not be dropped, or the correlation between HIC and head injury will be biased.

Bringing the Metrics Together

The four metrics operate at different levels: HIC, chest deflection/VC, and Nij are computed per-sample from physical channels and reduced to event maxima; AIS is an outcome label attached per occupant. A well-structured analytics pipeline keeps these separate — a metrics table keyed by test and occupant, and an outcomes table keyed the same way — then joins them for correlation and dashboard display. The SVG below shows this structure and the computation path for each metric.

Two validation checks are worth building into the pipeline before any dashboard is trusted. First, recompute HIC and Nij on a known reference pulse and compare against the tool's published value; a mismatch usually traces to filter class, resultant ordering, or unit conversion (g vs. m/s²). Second, verify that the AIS join produces no orphaned metric rows and no duplicated outcome rows — a one-to-many join on occupant position is a common source of inflated injury counts. Once these checks pass, the metrics table and outcomes table can be joined safely, and the dashboard can present threshold exceedances alongside coded injury severity with confidence that the underlying numbers are comparable across tests.