ETSEME develops a real-time stress monitoring solution that utilizes radar technology to analyze physiological signals, such as cardiac and respiratory activities, through remote sensing of micro-movements. This technology provides measurable and actionable data on stress levels, addressing the significant issue of work-related stress that affects 67% of employees and leads to decreased productivity and increased turnover.
Funding
$201.6K 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
Work-related stress affects a significant portion of the workforce, leading to decreased productivity, increased accident rates, higher employee turnover, and burnout. Traditional methods of stress monitoring are often reactive, subjective, and fail to provide real-time insights into an individual's stress levels. This lack of timely and objective data hinders proactive interventions and personalized support for employees.
Solution
ETSEME is developing a real-time stress monitoring solution that utilizes radar technology to remotely analyze physiological signals, specifically cardiac and respiratory activities, by detecting micro-movements. The system extracts these signals without physical contact, providing objective and continuous data on an individual's stress level variations. Embedded AI processes and analyzes the data locally, offering real-time stress dynamics. This technology aims to transform stress into measurable, accessible, and actionable data, empowering companies to proactively address employee well-being and optimize performance.
Target Audience
The primary target audience includes companies and organizations seeking to improve employee well-being, reduce work-related stress, and enhance overall productivity.
Features
- Remote monitoring of cardiac and respiratory activities using radar technology.
- Non-contact measurement of physiological signals through micro-movement analysis.
- Embedded AI for local data processing and real-time stress level analysis.
- Real-time stress variation dynamics to track changes in stress levels.
- Continuous and objective stress data for proactive intervention.
- Integration of affective computing to recognize and classify human emotions.