hominin labs develops deep learning algorithms to sequence human movement data into personalized intelligence. This movement fingerprinting is applied across neurological health, musculoskeletal care, and athletic performance monitoring. The company provides tools for healthcare providers, sports teams, and apparel developers to leverage movement biomarkers for tailored outcomes.
Funding
$100K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Quantifying and tracking human movement patterns is challenging, especially outside of controlled lab environments. Current methods often lack the granularity and personalization needed for effective applications in healthcare, sports, and product development. Understanding individual movement nuances is difficult, hindering the creation of tailored interventions and optimized products.
Solution
InStep has developed a system that analyzes and sequences individual human movement patterns to create a unique "movement fingerprint" for each user. This technology leverages Large Movement Models (LMMs) and data from consumer wearables to provide personalized insights. By establishing movement baselines, InStep enables the quantification of recovery after injury and the development of products that align with specific movement profiles. The platform facilitates the creation of tailored treatment plans, enhances athletic training, and informs the design of active apparel and footwear. InStep's API suite allows developers to integrate human movement intelligence into various applications, fostering personalization and predictive modeling.
Target Audience
The primary target audience includes healthcare providers seeking tailored treatment plans, sports teams aiming to quantify athlete recovery, and active apparel/footwear companies focused on product testing and development.
Features
- Analysis of movement patterns using Large Movement Models (LMMs)
- Creation of a unique "movement fingerprint" for each user
- Integration with existing consumer wearables for data collection
- API suite for developers to build applications leveraging human movement intelligence
- Athlete health baselining to quantify recovery after injury
- Product testing for active apparel and footwear companies