Exploring the Role of Artificial Intelligence in Enhancing Nursing Bed Equipment: A Scoping Review

Digital Technologies Research and Applications

Review

Exploring the Role of Artificial Intelligence in Enhancing Nursing Bed Equipment: A Scoping Review

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https://doi.org/10.54963/dtra.v5i1.2225
Mei, L., Teh, C. S., Zhang, Y., Yang, J., An, N., & Fang, Z. (2026). Exploring the Role of Artificial Intelligence in Enhancing Nursing Bed Equipment: A Scoping Review. Digital Technologies Research and Applications, 5(1), 311–323. https://doi.org/10.54963/dtra.v5i1.2225
Volume 5 Issue 1: March 2026 (in progress)
Copyright (c) 2026 Long Mei, Chee Siong Teh, Yu Zhang, Jiaoyun Yang, Ning An, Zhidong Fang
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Authors

  • Long Mei

    Faculty of Cognitive Sciences & Human Development, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia
  • Chee Siong Teh

    Faculty of Cognitive Sciences & Human Development, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia
  • Yu Zhang

    School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
  • Jiaoyun Yang

    School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
  • Ning An

    School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
  • Zhidong Fang

    Shanghai Sanlianren Technology Co., Ltd., Shanghai 201109, China

Received: 30 December 2025; Revised: 9 February 2026; Accepted: 23 February 2026; Published: 24 March 2026

Artificial intelligence (AI) has been increasingly integrated into nursing bed equipment to enable continuous patient monitoring, reduce adverse events, and improve the quality of care for bedridden and elderly individuals. Smart nursing beds equipped with sensors and AI algorithms can non-invasively detect posture changes, falls, and physiological abnormalities; however, the scope, technological maturity, and limitations of these systems remain insufficiently synthesized in the existing literature. This study presents a scoping review of AI-based nursing bed equipment, focusing on sensor technologies, application areas, and analytical methods. Four electronic databases—Web of Science, PubMed, IEEE Xplore, and CINAHL—were searched for studies published between January 2010 and July 2024. A total of 5,496 records were identified, and 4,184 unique articles remained after duplicate removal. Following screening in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, 135 studies (3.23%) were included in the final analysis. Pressure sensors were the most frequently used sensing modality (43.0%), followed by RGB (Red–Green–Blue) cameras (11.1%), infrared and thermal imaging sensors (8.9%), and depth cameras (7.4%), while other modalities—including accelerometers, radar, radio-frequency sensors, microphones, and multi-sensor systems—accounted for 37.0% of the studies. The primary application domains were in-bed posture classification and activity monitoring, followed by fall detection, physiological monitoring, and human–machine interaction, with deep learning methods, particularly convolutional and recurrent neural networks, being the most commonly employed analytical approaches. Overall, AI-based nursing bed equipment shows considerable potential to enhance patient safety and care efficiency; nevertheless, challenges related to deployment costs, data privacy, and limited clinical validation remain and must be addressed to enable large-scale adoption and real-world implementation of intelligent nursing bed systems.

Keywords:

Artificial Intelligence Deep Learning Elderly Care Fall Detection Machine Learning Non-Contact Monitoring Nursing Bed Equipment Posture Classification Sensors

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