Somni offers an AI-powered platform that predicts and mitigates workplace accidents caused by worker fatigue. By analyzing biometric data, sleep patterns, and vigilance tests, the system provides real-time alerts to workers and supervisors, enabling proactive fatigue management and enhancing safety.
Funding
$20K 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
Worker fatigue and drowsiness are significant contributors to workplace accidents, leading to safety incidents and reduced operational efficiency. Traditional methods for monitoring alertness often lack precision and real-time actionable insights, leaving critical risks unaddressed.
Solution
Somni provides an AI-powered platform designed to predict and mitigate accidents stemming from worker fatigue and drowsiness. The system leverages machine learning models trained on biometric data, vigilance tests, sleep patterns, and fatigue perception surveys to assess individual risk levels. Predictive alerts are delivered to both workers and supervisors, enabling proactive intervention and the implementation of fatigue management protocols, such as scheduled breaks or task reassignment. This approach fosters safer work environments and enhances overall productivity by addressing fatigue-related hazards before they escalate.
Target Audience
The primary users are companies in industries with high-risk operational environments, such as transportation, logistics, and heavy industry, seeking to improve worker safety and operational continuity.
Features
- Predictive analytics engine utilizing machine learning and biomathematical models to forecast fatigue-related risks.
- Vigilance testing (PVT) for objective assessment of psychomotor performance.
- Integration of individual sleep opportunity data and biocompatibility factors.
- Real-time alert system for workers and supervisors based on identified risk thresholds.
- Protocol suggestions for fatigue mitigation, including active breaks and task adjustments.
- Data integration from multiple sources to create a comprehensive fatigue risk profile.
- Secure platform for data input, analysis, and alert dissemination.