CareTaker.ai—A Smart Health-Monitoring and Caretaker-Assistant System for Elder Healthcare
There are several systems for patient care, including elderly healthcare, which rely on sensor data acquisition and analysis. These sensors are typical vital-monitoring sensors and are coupled with Artificial Intelligence (AI) models to quickly analyze emergency situations or even predict them. Thes...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/78/1/7 |
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| Summary: | There are several systems for patient care, including elderly healthcare, which rely on sensor data acquisition and analysis. These sensors are typical vital-monitoring sensors and are coupled with Artificial Intelligence (AI) models to quickly analyze emergency situations or even predict them. These systems are deployed in hospitals and require expensive monitoring and analysis equipment. Eldercare specifically encompasses monitoring, smart analysis, and even the emotional aspects of care. Existing systems do not provide a portable, easy-to-use system for at-home eldercare. Further, existing systems do not address advanced analysis capabilities around mood/sentiment/mental state/mental disorder analysis or the analysis of issues around sleep disorders, apnea, etc., based on sound capture and analysis. Also, existing systems disregard the emotional needs of elderly patients, which are a critical aspect of patient wellbeing. A low-cost and effective solution is therefore required for extended use in eldercare. In this paper, the CareTaker.ai system is proposed to address the shortcomings of the existing systems and build a comprehensive caretaker assistant using sensors, audio, video, and AI. It consists of smart bed sheets, pillow covers with embedded sensors, and a processing unit with GPUs, conversational AI, and generative AI capabilities, with associated functional modules. Compared to existing systems, the proposed system has advanced monitoring and analysis capabilities with potential for low-cost mass manufacturing and a widespread commercial application. |
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| ISSN: | 2673-4591 |