VALIDATION
Built on independently validated data.
Movement metrics are validated against gold-standard references — force plates, instrumented treadmills, and motion capture — across multiple independent research programs.
Approved wearable:
Validation comes first
Leading institutions validate Plantiga independently before they use it. That validation is the first step, and it is why the platform is trusted across many different fields of research.
NBA
Wearables Program · Fraunhofer Institute
Simon Fraser University
Burnaby, BC, Canada
University of British Columbia
Vancouver, BC, Canada
Auckland University of Technology
Auckland, New Zealand
Australian Catholic University
Melbourne, Australia
Other institutions worldwide
Validation as a first step before use
NBA Wearables Program, Fraunhofer Institute
Plantiga was validated through the NBA Wearables Program, a joint initiative of the NBA and the NBPA that independently vets wearable technologies before they are approved for use.
Testing was conducted at the Fraunhofer Institute in Germany, against the gold standards, over a period of several months.
Plantiga passed on every metric on the platform. The program tested the technology under controlled, independent conditions rather than relying on manufacturer-reported data.
Scope
All platform metrics
Reference
Force plates, motion capture, IMUs
Duration
Several months
Validation in three layers
The metrics are the foundation. With those validated, the work extends to the technology in real-world conditions, and then to the applications the data is used for.
Metric validation
Accelerations, decelerations, g-load, speed, distance, gait timing, jump height, RSI, and left-right asymmetries, measured against force plates, instrumented treadmills, and motion capture.
Technology validation
Reliability across footwear, running speeds, surfaces, and sensor placement, and over time, demonstrated week-to-week and month-to-month rather than in a single session.
Application-level validation
Using validated data to study protocols and interventions: measuring what changes movement, and by how much.
Research built on that validation
Because the metrics are independently validated, Plantiga is used as a measurement tool across a wide range of research, well beyond sport performance alone.
Rehab
Return-to-play
Taking the guesswork out of return-to-play decision making with objective data on how athletes move in their actual sporting environment.
Women
Women's biomechanics
Building the research and understanding of how women move, perform, and compete in sport.
Performance
Sport performance and apparel biomechanics
Studying movement and motion quality as they relate to performance and product design.
Surfaces
Sports surface companies
Quantifying how playing and training surfaces change biomechanics and impact loading.
Apparel
Large apparel companies
Measuring how footwear and performance apparel affect movement, loading, and asymmetry.
Healthcare
Neurodegenerative conditions
Understanding disease progression and assessing the efficacy of interventions.
Publications and ongoing projects
Peer-reviewed papers and conference work from independent research groups, summarized below with links to the full studies.
One-Week and Three-Month Reliability of Acceleration Outcomes From an Insole-Embedded IMU During Treadmill Running
Peak resultant, vertical and anteroposterior accelerations showed good to excellent short-term reliability and moderate to excellent long-term reliability, supporting clinical use of insole-embedded IMUs to measure peak accelerations.
Full paperAccuracy of Reactive Strength Index (RSI) Assessed with an Instrumented Insole
A deep-learning RSI model from the insole agreed with force-plate RSI to within a 2.1% mean difference, supporting accurate vertical-jump RSI estimates in athletes.
Accuracy of Running Speed Prediction with Smart Insoles
Across 33 treadmill trials, the insoles predicted running speed within roughly 0.15 m/s with low coefficients of variation.
One-Week Reliability of Spatiotemporal Walking Gait Metrics Using Insole-Embedded IMUs
Ground contact, swing, single and double limb support and cadence showed excellent week-to-week reliability across walking speeds, with the strongest results at faster speeds.
Full summaryOne-Week Reliability of Spatiotemporal Running Gait Characteristics Using Insole-Embedded IMUs
Step rate, flight time, swing time, ground contact time and duty factor showed good to excellent one-week reliability, supporting week-to-week tracking.
Full summaryOne-Week Reliability of Jump Tests Using an Insole-Embedded IMU
Across five common jump tests, every outcome including RSI, jump height, and distance showed high or excellent reliability.
Full summaryExamining Gait Adaptations in Loaded Foot Marches Using Instrumented Insoles
Instrumented insoles captured shifts in cadence, ground contact, and double-support time during a two-hour loaded march, and their correlation with individual fitness, indicating field-screening potential.
Full paperIMU Analysis for Greater Diagnostic Value During the Modified 5-0-5 Change of Direction Test
In elite netball athletes, the IMU insole reliably measured peak acceleration, deceleration, max speed, and ground contact time during a 5-0-5 test, adding diagnostics beyond timing gates.
Full paperVariability of Dual-Task Walking Parameters Using In-Shoe Inertial Sensors
No dual-task gait measure varied more than 6.5% between sessions, with acceptable reliability across three walking protocols, supporting in-shoe sensors for concussion-related gait assessment.
Full paperDifferences in Peak Impact Accelerations Among Foot Strike Patterns in Recreational Runners
Insole-embedded IMUs detected meaningful differences in peak acceleration across rearfoot, midfoot, and forefoot strike patterns, indicating these groups should be analyzed separately.
Full paperEffect of Footwear, Running Speed and Location on the Validity of Two Commercially Available IMUs
The Plantiga insole IMU was comparable to a tibia-mounted IMU as a surrogate for average vertical loading rate, with the relationship strengthening at faster running speeds.
Full paperNBA Wearables Program · Fraunhofer Institute
Validation across all platform metrics against gold-standard motion capture.
Validation: jump testing, Reactive Strength Index, and others.
Observational: running injuries, improved outcomes with coaching plus monitoring.
Validation: speed and distance.
Single Leg Jump Validation
Against a Kistler dual force-plate platform, jump height and distance both reached an r-squared of 0.99.
Full reportGait Metrics Validation
Against an instrumented treadmill, gait metrics were predicted with a maximum error IQR of 1.6% for single-leg and 6.1% for double-leg metrics.
Full reportSpeed Validation
Across outdoor walking and running from 1.5 to 4.8 m/s, speed was predicted within -0.24% error and distance within 1.93% error, both at R-squared of 1.00.
Full reportGround Interaction Algorithm Validation
Across jumps, walking, running, and in-place activities on a dual force-plate system, takeoff and landing detection showed median errors of 0 ms.
Full reportReactive Strength Index (RSI) Validation
Against a Kistler dual force plate, RSI from the insoles reached an r-squared of 0.985.
Full reportCapturing Gait and Jump Data with Plantiga Insoles
An overview of the gait and jump parameters Plantiga measures, and how the sensor-embedded insole captures them at each foot strike.
Full white paperPlantiga's Machine Learning Platform
How the platform was designed for machine learning, producing high-quality tagged data, illustrated with a human movement recognition case study.
Full white paperEffect of Load Carriage on Gait
A small-scale study using machine learning to quantify how the body reacts to load, relevant to military training, hiking, and injury prevention.
Full white paperIDEaS Program 1B: Predicting Load, Injury Risk and Progression with Sensor Insoles
Work across two research contracts for the Canadian Department of National Defence, producing a predictive load-carriage readiness score and a predictive movement-health score for real-world monitoring.
Full report