The startup develops a wearable sensor for real-time hydration monitoring, utilizing an IoT-based platform that filters unwanted signals from hydration data streams. Their proprietary deep-learning algorithms analyze these streams to identify temporal patterns and anomalies, providing clients with actionable cloud-based data analytics for informed decision-making.
Funding
$220K 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
Current methods for assessing hydration levels are either invasive and expensive clinical procedures or field methods that provide only intermittent data. The lack of continuous, non-invasive hydration monitoring can lead to adverse health conditions, reduced performance, and increased medical costs, especially in vulnerable populations.
Solution
Onda Vision Technologies offers a wearable sensor for real-time, continuous, and non-invasive hydration monitoring. The sensor utilizes bioimpedance analysis (BIA) or bioimpedance spectroscopy (BIS) via silver nanowires inlaid in a stretchable matrix to improve the accuracy of hydration assessment. Data from the sensor is processed using proprietary signal processing techniques to remove noise and motion artifacts. The processed data is then analyzed with deep learning algorithms to identify temporal patterns and anomalies, providing actionable insights through a cloud-based platform.
Target Audience
The primary target markets include sports teams, military personnel, occupational workers, agricultural workers, healthcare providers, and the elderly, especially those at risk of dehydration due to exertion, mission requirements, medical conditions, or environmental factors.
Features
- Patented wearable hydration sensor for continuous, non-invasive monitoring
- Stretchable sensor constructed from silver nanowires (AgNWs) inlaid in a polydimethylsiloxane (PDMS) matrix
- Bioimpedance analysis (BIA) or bioimpedance spectroscopy (BIS) for improved accuracy
- Proprietary signal processing to remove noise and motion artifacts from data streams
- Deep learning algorithms to discover temporal patterns and anomalies in hydration data
- IoT-based platform for scalable monitoring across individuals, small teams, or large groups
- Cloud-based data analytics for timely, meaningful end-user decisions