Paragliding Harness Impact  Index -  My POV

What is DRI_EN
DRI_EN (Dynamic Response Index – normative approach) is an index used to evaluate the risk of spinal injuries following a vertical impact, typically within the context of harness and seat certification.

Calculation Principle

• It is based on the analysis of vertical acceleration over time (a(t)).

• The acceleration signal is filtered and analyzed within a meaningful time window.

• The index is derived from integrating the dynamic response of the human body, modeled as a mass–spring–damper system.

What It Represents
The resulting value represents an estimate of the load transmitted to the spine during the impact and is useful for comparing different protection systems.

Limitations
DRI_EN is primarily designed for vertical impacts and does not fully describe real-world scenarios.

What DRI_biom is
DRI_biom is a biomechanical risk index that uses models closer to the physiological response of the human body compared to purely regulatory indices.

Calculation Principle

• It always starts from the acceleration signal over time.

• It uses biomechanical models of the spine and tissues.

• It evaluates the probability of injury based on known biomechanical thresholds.

What it represents
It provides an estimate of injury risk based on human tolerance criteria, often resulting in a more medically realistic assessment.

Limitations
It requires assumptions about the subject (mass, posture, age) and therefore can vary significantly from person to person.

This document describes in a clear and implementation-faithful manner how the LEAI (Low-Energy

Absorption Index) is calculated. The description follows the actual computational logic used in the

experimental implementation.

1. Signal and Units

The input signal is a time history of acceleration expressed in g units, sampled at discrete time intervals.

Time is expressed in seconds.

2. Start Time (t0)

The integration starts at the first instant t0 for which the absolute value of the measured acceleration

exceeds 1 g. All data prior to this instant are ignored.

3. Threshold-Based End Time (t_lim)

For each predefined acceleration threshold (for example 5 g, 10 g, 15 g, 20 g), the end time

t_lim is defined as the first instant at which the absolute acceleration reaches or exceeds the selected

threshold. If the threshold is never reached, the entire signal duration is used.

4. Analysis Interval

The LEAI is computed over the time interval from t0 to t_lim. If this interval contains fewer than two

samples, the LEAI value is considered undefined.

5. Local Jerk Computation

Between each pair of consecutive samples, the local jerk is computed as the finite difference of

acceleration divided by the corresponding time step.

jerk = (a2 - a1) / dt

6. LEAI Integration

The LEAI is obtained by integrating the absolute value of the jerk raised to a power p over time. In

discrete form, the computation is performed as a cumulative sum.

LEAI = sum( |jerk|^p * dt )

In the current implementation, the exponent p is equal to 1.5.

7. Normalization and Filtering

No normalization, scaling, or energy-based filtering is applied. The LEAI is a raw time integral of the

powered jerk over the selected interval.

8. Output

The procedure outputs one LEAI value per acceleration threshold, together with the corresponding

integration end time and a risk class derived from predefined bands.

From a mathematical point of view, the SIC formulation is internally consistent. It represents the maximum, over a moving time window, of the time duration multiplied by the square of the average acceleration within that interval.

However, from a scientific and biomechanical perspective, the SIC does not have a solid foundation as an injury criterion.

Although its structure is similar to the well-established Head Injury Criterion (HIC), the use of an exponent equal to 2 is not supported by experimental or physiological evidence. In contrast, the HIC exponent of 2.5 is derived from extensive empirical research (notably the Wayne State Tolerance Curve) and reflects the nonlinear response of human tissues, particularly the brain, to acceleration.

As a consequence, the SIC can be interpreted only as an engineering or comparative metric, useful for internal or relative evaluations, but not as a predictor of human injury or as a safety threshold. No validated tolerance limits or injury correlations are associated with this formulation in the scientific literature.

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In conclusion, while the SIC has mathematical meaning, it lacks biomechanical validation and should not be considered a scientifically established injury criterion.

It is also important to clarify the scope of applicability of the Head Injury Criterion (HIC).

The HIC was specifically developed to estimate the risk of traumatic brain injury resulting from rapid head acceleration and deceleration. It is primarily correlated with cerebral injury mechanisms, such as diffuse axonal injury and intracranial stress responses, rather than with skeletal injuries (e.g., skull fractures) or injuries to other body structures.

Bone fractures and structural damage are governed by different mechanical parameters, such as peak force, stress distribution, material strength, and contact mechanics, which are not directly captured by the HIC formulation. Therefore, while HIC is a well-established criterion for brain injury assessment, it should not be interpreted as a general indicator of overall bodily injury or structural failure.

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From my point of view

A reasonable approach is to use the SIC value as a descriptive and statistical parameter, rather than as a direct injury criterion.

By collecting SIC values across different events or scenarios, it becomes possible to build a statistical dataset and observe how this parameter behaves under varying conditions. This allows us to analyze trends, relative differences, and correlations with other measurable quantities, without assuming a direct physiological meaning.

In this context, the SIC can be used to explore which parameters most strongly influence its magnitude, how it scales with duration and average acceleration, and whether it shows consistent patterns when comparing different impact profiles. The goal is not to predict injury, but to extract useful information from the data and improve qualitative understanding through comparison.

Only after sufficient statistical analysis and correlation with established injury metrics could any further interpretation be considered.

In any case, I will try to integrate the SCI into my calculations and will keep you updated on the results and my thoughts😊