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Smarter Motion Starts With Synthetic Data

Kinevo generates physiologically accurate, fully-labeled movement data empowering engineers to build breakthrough wearables and train superior AI models without expensive labs or time-consuming data collection.

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Why Motion Data Is Holding You Back

Unaffordable

€70K-100K+ for motion capture equipment alone without expert hours for planning and analysis.

Painfully Slow

Months wasted on participant recruitment, data collection, and cleaning.

Lab-Limited Data

High-quality data is limited to controlled settings, while diverse data remains inaccessible.

Privacy Barriers

Data sharing restrictions and lack of standardization block effective collaboration.

How Kinevo Works: Simplified Motion Data Simulation

Setup & Data Loading

First, install our software package and import Kinevo.
Then load your reference motion data to prepare the system for simulation and analysis.

Step 1
Data Loading and Calibration
kinevo_workflow.py
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from kinevo import Kinevo, MovementType, KinevoDataset
from kinevo.config import Demographics, Constraints, Biomechanics
import torch

# Initialize the Kinevo simulation using the SDK
simulation = Kinevo()

# Load reference data from motion capture and sensor files
simulation = simulation.load_reference(
    mocap_path = "reference.trc",  
    sensor_path = "sensor_data.json",  
    movement_type = MovementType.COUNTERMOVEMENT_JUMP,  
    sensor_placement = {
        "accelerometer": "right_ankle",  
        "gyroscope": "right_ankle",     
    }
)

# Calibrate the internal sensor model
# This step aligns the simulated sensor measures with the reference data
simulation = simulation.calibrate_sensors()

# Configure simulation parameters
simulation.with_demographics(
    Demographics()
    .age(range(20, 40))
    .height(range(160, 190))
    .weight(range(60, 90))
).with_movement_constraints(
    Constraints()
    .arms_on_hips()
    .max_knee_flexion(120)
).with_biomechanics(
    Biomechanics()
    .add_fatigue(range(0.0, 0.3))
    .add_asymmetry(
        muscles=["soleus_r"],
        strength_reduction=range(0.0, 0.2)
    )
)

# Setup population
population = simulation.setup_population(size=1000)

# Preview workflow, adjust parameters if needed
simulation.preview(n_samples=5).visualize_results()

# Run full simulation and export results
simulation_results = (
    simulation
    .run(population)
    .parallel(n_jobs=3)
)

# Create datasets and loaders with specific input and output features
train_dataset = KinevoDataset(
    simulation_results,
    input_features=["accelerometer_data", "age", "height", "weight"],
    output_features=["ground_reaction_forces", "achilles_tendon_stress"]
)

# Create data loaders with pytorch
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32)

# Train model...
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Why Choose Kinevo?

Built For HealthTech & SportsTech

Simulate accurate joint movements, muscle activations and forces for wearables, rehabilitation tools and training algorithms.

Customizable Simulations

Tailor data to your specific sensors and target population with adjustable parameters for demographics and biomechanics.

Superior Data Quality

Unlike animation-focused competitors, we provide physiologically plausible data with complete biomechanical details.

Faster & More Cost-Effective

Generate diverse movement data without expensive motion capture equipment or lengthy collection processes.

Perfect For

  • Wearable device manufacturers
  • Sports performance analytics companies
  • Rehabilitation technology developers
  • AI/ML teams training movement models

Not For

  • Animation or visual effects studios
  • Projects needing only static, pre-recorded data
  • Products without customization requirements
  • Applications not focused on biomechanical accuracy
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Trusted Partners & Supporters

We collaborate with leading institutions and organizations to deliver the highest quality biomechanical data solutions.

Industry Leaders Back Our Approach

Synthetic data isn't just an alternative.
It's the only viable path forward for modern movement analysis.

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Meet The Kinevo Team

Our team combines expertise in biomechanics, AI, and product development to deliver innovative movement data solutions that solve real customer problems.

Aurel Berger

Aurel Berger

Co-Founder, Technical Lead

Develops core simulation engine

Biomechanics & AI Expert

MSc in Medical Technology, ETHZ

Philip Giryes

Philip Giryes

Co-Founder, Product Lead

Turns customer needs into solutions

Full-stack developer

MSc in Artificial Intelligence, UZH

Tumen Dambiev

Tumen Dambiev

Co-Founder, Operations Lead

Steers operations & business strategy

Automation & Data Specialist

MSc in Data Science, UZH

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Frequently Asked Questions

Find answers to common questions about Kinevo's biomechanical simulation software package.

What data can Kinevo simulate?

We simulate physiologically accurate movement data including:
  • IMU sensor data (accelerometer, gyroscope, magnetometer)
  • GPS positioning
  • Pressure sensor readings
  • Joint movements and angles
  • Muscle activations and forces
  • Ground reaction forces

All simulations are highly customizable with adjustable parameters for demographics, movement types, sensor placement, and biomechanics.

Why is Kinevo's data superior to alternatives?

Unlike animation-focused competitors, our data is:
  • Physically and physiologically plausible - based on real-world biomechanics using musculoskeletal models
  • Customizable - tailored to your specific sensors and target population
  • Comprehensive - includes joint loads, muscle activations, and forces that other systems miss
  • Validated - through academic research and partnerships with institutions like ETH Zürich and companies like Muvity

We continuously validate our models through real-world case studies and research collaborations.

What applications is Kinevo best suited for?

Kinevo excels in applications requiring biomechanical accuracy:
  • Wearable Device Development: Enhance accuracy with simulated sensor data for various movements
  • Sports Performance Analytics: Model complex athletic movements to optimize training and reduce injuries
  • Rehabilitation Technology: Develop and test devices with realistic movement patterns
  • AI/ML Training: Generate diverse datasets for movement recognition algorithms

Not ideal for: Animation studios, projects needing only static data, or applications not focused on biomechanical accuracy.

How do I get started with Kinevo?

Our collaboration begins with three calls to ensure we're a good fit for each other:

Discovery Call

An in-depth conversation to understand your specific challenges and determine if Kinevo is the optimal solution for your needs.

Technical Assessment

A session with your technical team to define requirements and discuss the integration processes.

Custom Demo

A personalized demonstration of our solution with sample code and customized simulations.

All consultations are complimentary and designed to establish clear alignment between your objectives and our capabilities, ensuring a productive partnership from the start.

How do you handle data privacy and security?

Each customer receives a personal, tailored instance of our simulator.

You never need to upload sensitive data to our servers.

Instead, you can use any data you have to tune your personal instance locally, eliminating privacy risks while still benefiting from our simulation capabilities.

What about performance, support, and integration?

Performance

Simulation time depends on complexity, but we deliver high-quality data within hours to days depending on scale.

Support

During the pilot phase, we provide comprehensive support including technical assistance, integration help, and regular check-ins.

Integration

Our simulator is designed to integrate seamlessly with existing applications and systems. We'll work with your technical team to ensure compatibility.

We're currently in the pilot phase with limited spots available at reduced prices. Soon we'll release documentation and tutorial scripts to help you test our workflow independently.

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