Oso provides an ambient intelligence solution designed for caregivers and individuals in vulnerable situations, utilizing advanced audio detection technology to automatically identify distress signals and alert staff. This system enhances resident safety by ensuring timely interventions while reducing caregiver workload, allowing for improved quality of care and peace of mind for families.
Funding
$15.6M 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.
BUFounders
Product
Problem
Many residents in care facilities, particularly those with limited mobility or cognitive impairments, cannot reliably use traditional call systems to signal distress. This can lead to delayed assistance during falls, medical emergencies, or other urgent situations, impacting resident safety and increasing caregiver workload. Furthermore, existing monitoring methods may not respect resident privacy or promote a comfortable environment.
Solution
OSO provides an ambient intelligence solution that uses advanced audio analysis to automatically detect distress signals and unusual events in care environments. The system leverages acoustic sensors and machine learning algorithms to identify sounds indicative of falls, calls for help, or other emergencies, without requiring residents to wear devices or actively trigger alarms. When a critical event is detected, the system sends immediate alerts to caregivers, enabling rapid response and intervention. By continuously monitoring the environment and filtering out irrelevant noises, OSO aims to improve resident safety, reduce caregiver burden, and enhance the overall quality of care through proactive, non-intrusive monitoring.
Target Audience
The primary target audience includes assisted living facilities (EHPAD in French), nursing homes, disability centers, and other residential care providers seeking to improve resident safety and staff efficiency.
Features
- Real-time audio analysis using machine learning to detect distress events
- Automated fall detection based on sound patterns
- Customizable alert settings to filter out background noise and reduce false alarms
- Secure, encrypted data transmission and storage to protect resident privacy
- Integration with existing nurse call systems and mobile devices for immediate notifications
- Web-based dashboard for caregivers to review alerts, analyze trends, and manage system settings
- Continuous monitoring without the need for wearable sensors or manual activation
- Remote configuration and maintenance for easy deployment and ongoing support