Alan Hernández
CEO
Biogrip Tech
8/10Traction
BioGrip develops a non-invasive, high-resolution neuromuscular human-machine interface (HMI) platform that captures sensitive biological signals. This platform uses machine learning to decode signals into precise, simultaneous movements for real-time wireless control of robotic systems. The technology aims to enhance human capabilities, support rehabilitation, and provide advanced prosthetic control for individuals with physical limitations.
- Artificial Intelligence
- Biotechnology
- Hardware
- Healthcare Technology
Updated 18 months ago
Funding
Raised to date
$670KRaised 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.
Funding rounds are not available yet.
Founders
Product
Problem
Existing bionic systems often struggle to provide users with natural and intuitive control of artificial limbs, requiring significant mental effort to perform complex, simultaneous movements. This lack of seamless integration between mind and machine limits the functionality and user experience of current prosthetic devices.
Solution
BioGrip is developing a smart bionic system that uses a wireless nerve-machine interface to connect robotic systems to the human body, enabling natural and simultaneous control of artificial limbs. Their non-invasive sensor membrane collects nerve signals and employs AI algorithms to compare them to a limb motion model, predicting the user's intended movements. The system then wirelessly transmits these signals to the device, executing the desired motion with minimal conscious effort. This technology aims to restore natural movement and independence for individuals with physical disabilities.
Target Audience
The primary target audience includes individuals with physical disabilities requiring artificial limbs or rehabilitation devices, as well as medical professionals and researchers in the field of bionics and prosthetics.
Features
- Wireless nerve-machine interface for direct connection between the human body and robotic systems
- Non-invasive sensor membrane for collecting nerve signals
- AI-powered motion prediction based on a limb motion model
- Real-time control of complex and simultaneous movements
- Voice control and indicators for enhanced usability
- Assisted prediction providing feedback to control pressure applied to objects and anticipate desired movements