Asilla Launches Proof of Concept for 'Private Room Monitoring' Using Behavioral Recognition AI at Social Welfare Facility

Driven by the mission 'Towards a safe and comfortable world through the power of technology,' Asilla, Inc. (Headquarters: Machida, Tokyo; Representative Director, CEO & COO: Tsuyoshi Onoe; hereinafter 'Asilla'), developer and provider of the AI monitoring system 'asilla care' (hereinafter 'asilla') for nursing and welfare facilities, announces the commencement of a proof of concept (PoC) using behavioral recognition AI technology inside private rooms at the social welfare corporation Fuji Hakuen (hereinafter 'Fuji Hakuen').

Background of the Deployment

The nursing care frontlines are facing severe labor shortages, making it increasingly difficult to ensure the safety of all residents with limited staff. 'Private rooms,' in particular, are challenging to monitor due to privacy concerns, making them locations where the discovery of falls or sudden illnesses is easily delayed. Furthermore, maintaining consistent service quality inside closed rooms remains a significant issue.

In this PoC, 24-hour monitoring powered by behavioral recognition AI will be introduced into private rooms, aiming to achieve both the safety of residents and a reduction in the workload of care staff.

Overview of the Proof of Concept

'asilla' will be deployed at a facility operated by Fuji Hakuen to verify monitoring capabilities in 'private rooms' in addition to common areas. This marks Asilla's first initiative to test utilization inside private rooms.

This PoC will confirm effectiveness across the following four points:

- 1. Early Detection of Falls and Sudden Illnesses: While protecting privacy, the AI detects unexpected falls or extended periods of immobility indicating poor health inside the private room. By instantly notifying staff, it prevents delays in discovery within closed rooms, enabling swift rescue and response.

- 2. Faster Handling of Lost Items: When a lost item is reported in a private room, camera data will be utilized as entry/exit records to support rapid fact-checking and recovery.

- 3. Creating an Environment to Prevent Serious Incidents: The AI accurately captures 'minor incidents' that are one step away from leading to major accidents. In addition to nighttime monitoring and managing restricted areas, the system prevents unauthorized departures by detecting approaches near exits. By catching signs of accidents early, it curbs the occurrence of major incidents.

- 4. Reducing the Burden on Care Staff: Operating 24/7 as 'another set of eyes,' the AI alleviates the mental and physical monitoring load on staff. The goal is to create an environment where even a limited number of personnel can maintain high safety standards and focus on interpersonal care.

About 'asilla care'

'asilla care' is a system where AI analyzes footage from existing security cameras within a facility in real-time to quickly detect anomalies such as residents falling, wandering, or stumbling. It takes approximately 1 second from the occurrence of an incident to detection. The detected information is immediately notified to staff smartphones or intercoms, allowing even those far away or engaged in other tasks to respond swiftly. It requires no new sensors or major construction work and can be implemented simply by adding it to the current equipment environment.