Hippos
Hippos is building a closed-loop physical intelligence system that predicts and prevents bodily injury before it occurs. Its first product is a soft knee exoskeleton with multimodal sensing at 6,400 Hz and a pneumatic micro airbag that intervenes faster than human reflex. The platform learns each wearer's body-state transitions to anticipate injury, extend capability, and eventually support lifespan extension.
- Artificial Intelligence
- Hardware
- Healthcare Technology
- Wellness & Fitness Technology
Funding
Founders
Product
Problem
Current wearable technology can only react to injuries after they happen, leaving the body's warning signals unread until it is too late. No machine can predict what a person's body will do next, how much capacity remains today, or that a knee is moments from giving out—so injuries occur that could have been prevented with early intervention.
Solution
Hippos is building the first Human Body Model, a machine-learning system that learns the transition between body states: state, action, context, intervention, and next state. The model ships inside a closed-loop physical intelligence system whose first form factor is a soft exoskeleton for the knee. The system senses body signals at 6,400 Hz, predicts imminent risk, and intervenes through both software alerts and a pneumatic micro airbag that activates faster than human reflex. This loop enables injury prevention today, with a roadmap toward extending lifespan and eventually supporting human augmentation.
Target Audience
Primary users are athletes, physically active individuals, and workers in physically demanding roles who face elevated injury risk and need real-time protection and load guidance.
Features
- Proprietary multimodal sensing infrastructure sampling at 6,400 Hz for high-resolution body-state tracking
- Pneumatic micro airbag hardware that deploys faster than human reflex to protect joints during high-risk movements
- Kinematic risk alerts that notify users of impending injury before physical failure occurs
- Prescriptive load planning software that recommends safe activity levels based on predicted body capacity
- A transition-learning model that captures state, action, context, and intervention data from every hour of wear