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Movement Performance Index (MPI)

ESTIMATING REAL-WORLD PHYSICAL CAPABILITY FROM PRIOR MOVEMENT EVIDENCE

The Movement Performance Index (MPI) is a planned analytical layer within DataUniversa designed to estimate a personā€™s likely capability for real-world physical tasks using movement evidence they have already generated.

Individuals have only performed a small number of measurable activitiesā€”such as fitness benchmarks, sports movements, or daily tasks. Yet those activities contain meaningful signals about broader physical capabilities.

MPI is designed to translate that existing evidenceā€”especially Global Fast Fit (GFF) benchmarks and Local Records (LR)ā€”into estimates of how capable a person may be in related but untested real-world situations, such as:

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Recovering movement after surgery
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Performing demanding construction tasks
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Managing sustained caregiving work
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Maintaining independent mobility in later life

Movement Performance Index (MPI)

The Movement Performance Index (MPI) is an output produced after data run through GMIP.

MPI does not produce a single universal score.

Instead it produces domain-level capability estimates, each:

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Scored on a 1ā€“100 scale
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Tagged with completeness (confidence level)
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Backed by explicit evidence sources

Example structure:

Capability Domain Estimated Score Estimated Score
Locomotion 74 High
High 68 Moderate
Balance & Stability Unknown Low
A single combined MPI score appears only when evaluating a specific context, such as:
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Construction-Fit MPI
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Construction-Fit MPI
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Post-Surgery Recovery MPI

Core Design Principle

MPI is organized by capability domains, not exercises.

Exercises such as push-ups, squats, or benchmark routines are evidence inputs, not the structure of the system.

This design allows the system to incorporate movement evidence from many environments:

Athletic
Athletic training
Labor
Informal labor
Rural
Rural work
Caregiving
Caregiving
Rehabilitation
Rehabilitation
Aging
Aging populations
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This prevents the index from becoming biased toward gym-based fitness data.

MPI Capability Domains

Each domain represents a fundamental class of human movement capability.

These domains are used to translate exercise evidenceā€”especially from GFF and Local Recordsā€”into estimates of real-world task readiness.

MPI Domain What it Represents Example Real-World Tasks Possible Evidence from GFF / LR
Locomotion Ability to move effectively through space Walking outdoors, uneven terrain, stair navigation, worksite mobility Walking outdoors, uneven terrain, stair navigation, worksite mobility
Load Handling Ability to carry and transport weight Carrying construction materials, groceries, water, tools Strength signals, lifting records, push/pull tasks
Postural Transitions Ability to change body position safely sitā†”stand, floorā†”stand, kneelā†”stand, getting up after a fall Squat capacity, lower-body strength/endurance
Balance & Stability Maintaining control while moving Stair descent, turning, carrying objects while walking Coordination signals, unilateral stability evidence
Sustained Activity Tolerance Ability to maintain activity over time chores, farm work, caregiving, extended work shifts GFF completion time, endurance evidence
Fine Manipulation & Coordination Precision and hand-body coordination Tool use, table tennis, repetitive manual tasks coordination-related sports or activity evidence
Structured Exercise (input layer) Standardized physical testing data not itself the real-world taskā€”used as upstream evidence GFF benchmarks, Local Records

How Prior Exercise Data Becomes Capability Estimates

MPI uses a structured process.

Step 1 ā€” Collect Evidence

The system gathers movement records such as:

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GFF benchmark results
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Local Record performances
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Activity videos or logs

Each record includes:

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Timestamp
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Participant ID
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Protocol definition
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Provenance documentation

Step 2-Map Evidence to Capability Domains

Each observed movement contributes signals to one or more domains.

Example:

Evidence Domains Informed
GFF Standard Endurance,locomotion
Heavy lifting record Local handling
Shuttle movement Locomotion,balance
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This converts exercise results into capability signals.

Step 3- Apply Recency and Evidence Weighting

Movement capability changes over time.

Older observations carry less predictive weight.

Example conceptual weighting:

Time since test Influence
>3 months Strong signal
3-12 months Moderate
Shuttle movement Weak
>3 months Minimal

Step 4-Generate Capability Estimates

The system then produces estimated domain scores with transparency about evidence quality.

Example output:

Domain Estimated score confidence evidence
Locomotion 78 High GFF Benchmarks
Load Handling 70 Moderate Lifting Record
Balance & Stability Unknown Low Insufficient Evidence
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This shows both what can be inferred and where more testing is needed.

Why This Matters

MPI bridges two traditionally separate areas:

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Exercise performance
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Real-world functional capability

By translating structured exercise evidence into broader capability estimates,
the system can help evaluate readiness for contexts such as:

checkRehabilitation planning
checkAging and independent living
checkLabor and job-fit assessment
checkCaregiving workload
checkPhysical task readiness
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The index therefore moves beyond simple fitness scoring toward real-world human capability intelligence.

Data Snapshot

13,274
Total scores
6,942
Videos
1,867
Total unique users
29
Country distribution
292
Documented Exercise Types

Whether youā€™re exploring interoperability, dataset valuation, AI readiness, or ecosystem participation, we welcome conversations with researchers, organizations, and strategic partners interested in the future of structured data systems.

info@datauniversa.com

Frequently Asked Questions

MPI is not a fitness test. It is a structured capability estimation framework that uses available movement-related evidence to estimate what a person may be capable of doing. Rather than measuring a single workout or performance event, MPI evaluates evidence quality, relevance, recency, and applicability to generate decision-support estimates across multiple capability domains.

Direct measurements are not always available when decisions need to be made. MPI allows organizations to evaluate existing evidence and determine what can reasonably be inferred from that evidence. This reduces the need for unnecessary testing while providing a structured and transparent assessment process based on available data.

Yes. MPI is specifically designed to operate on existing evidence sources. Training records, assessments, operational performance data, validated observations, and other admissible records can be evaluated and weighted according to relevance and recency. This allows organizations to generate capability estimates without rebuilding collection systems from scratch.

MPI is one of several DU index systems designed to transform raw evidence into decision-support intelligence. It works alongside DU interoperability, admissibility, provenance, and validation frameworks to ensure that capability estimates are traceable, explainable, and supported by documented evidence rather than assumptions alone.