RP1 - Wearonomics v1 : Verified Human Movement Program | 25 Datasets (2026)

Type: Empirical extension study
Status: Completed

This program represents the second structured application of the Wearonomics framework to verify human movement across a continuous set of real-world wearable datasets. A total of 25 sessions were processed, where recorded activity was evaluated through explicit validation rules to determine whether movement could be attributed to genuine human locomotion.

• 25 datasets processed

• 96.84 km recorded

• 27.30 hours logged

• 1,228.0 validated minutes

• 410.2 non-validated minutes

• 74.9% validation efficiency

• 122.8 credits (0.1 credits per validated minute of walking).

Across the program, 96.84 kilometres and 27.30 hours of activity were recorded. Of the total time, 1,228.0 minutes were verified as human movement, while 410.2 minutes were excluded as non-validated segments, resulting in a validation efficiency of 74.9%. These verified segments were systematically translated into ledger entries, producing a total of 122.8 credits (0.1 credits per validated minute of walking).

The dataset-level results show consistent system behaviour. Sessions with sustained and uninterrupted movement, such as D15 and D17, generated higher credit outputs, while shorter or interrupted sessions, such as D13, produced minimal attribution. This confirms that the framework does not rely on raw activity volume, but on the ability to verify movement as human and continuous.

Due to processing constraints, datasets were validated in sequential batches and later aggregated into a unified ledger. The final accumulation shows a continuous and coherent growth profile, with no discontinuities across runs, demonstrating that the system remains stable under segmented execution and scalable across larger dataset collections.

The results across these datasets confirm that wearable movement data can be transformed into a reliable and structured signal when processed through transparent validation rules. The integration of a ledger-based attribution model establishes a direct link between verified activity and measurable value, supporting the feasibility of economic attribution grounded in validated human behaviour.

Note: These findings should be interpreted with consideration of practical limitations. The framework depends on the accuracy and integrity of device-generated signals, and certain datasets may contain artefacts, signal smoothing, or non-wear inconsistencies that require further validation. Ongoing analysis in the lab continues to examine these conditions to strengthen the robustness and reliability of the framework.