Hippos
Hippos develops closed-loop human-body intelligence systems that combine wearable technology and artificial intelligence to sense, predict, and intervene in real time to prevent injury, prolong physical capability, and support recovery. Their core solutions are validated across military, clinical, and elite sports environments, with a focus on deploying high-frequency sensing and rapid mechanical interventions to optimize human performance and safety.
Industries
N/A
Products
Wearable system integrating high-frequency sensing, edge AI models, and pneumatic mechanical intervention to monitor and mitigate joint injury risk in real-time for defense, sports, and clinical use.
Wearable system integrating high-frequency sensing, edge AI models, and pneumatic mechanical intervention to monitor and mitigate joint injury risk in real-time for defense, sports, and clinical use.
Expertise Areas
- Biomechanical sensing and analytics
- Real-time injury prediction
- Clinical biomechanics research
- Human performance optimization
Key Technologies
- Edge machine learning
- Multimodal on-body sensing
- Soft exoskeleton design
- Pneumatic actuation
Key People
Co-founder
Co-founder
Co-founder
Co-founder
News & Updates
Replication clinical study demonstrating high accuracy in injury classification and localization using on-body sensing and advanced analytics.
First in-vivo labeled dataset and high-precision detection of ACL injury kinematics, with validated model accuracy and precision.
Completed high-intensity, continuous biomechanical monitoring across athletes, showing the feasibility and reliability of in-the-wild data collection.
Demonstrated reduction in peak valgus angle and engagement faster than reflex in simulated injury scenarios across multiple intervention designs.
Robotic ex-vivo studies quantified reductions in tibial rotation under controlled torque conditions, confirming mechanical intervention efficacy.
Multi-task biomechanical studies showing reliable halting of dangerous knee flexion across multiple movement types.
Replication clinical study demonstrating high accuracy in injury classification and localization using on-body sensing and advanced analytics.
First in-vivo labeled dataset and high-precision detection of ACL injury kinematics, with validated model accuracy and precision.
Completed high-intensity, continuous biomechanical monitoring across athletes, showing the feasibility and reliability of in-the-wild data collection.
Demonstrated reduction in peak valgus angle and engagement faster than reflex in simulated injury scenarios across multiple intervention designs.
Robotic ex-vivo studies quantified reductions in tibial rotation under controlled torque conditions, confirming mechanical intervention efficacy.
Multi-task biomechanical studies showing reliable halting of dangerous knee flexion across multiple movement types.