JMIR Publications

ABSTRACT

Background:

Over the past decade, research on sensor applications for pressure injury prevention has increased steadily, demonstrating significant advantages in the real-time and dynamic monitoring of pressure injuries related risk factors. Meanwhile, the rapid development of artificial intelligence has accelerated interest in intelligent smart sensors with advanced computational techniques. By combining the strengths of artificial intelligence and sensors technologies, these systems can achieve superior pattern recognition and predictive capabilities compared with traditional approaches, offering a new paradigm for personalized pressure injury prevention. However, systematic evidence summarizing the current applications of smart sensors in personalized pressure injury prevention remains lacking.

Objective:

To summarize current evidence regarding the application, technical maturity, and clinical translation of smart sensors in personalized pressure injury prevention.

Methods:

PubMed, Web of Science, Embase, Cochrane Library, CNKI, WEIPU, WANFANG, and SINOMED were searched for relevant studies using terms related to pressure injuries, sensors, artificial intelligence, and machine learning. Studies focusing on the development or validation of smart sensors for pressure injury prevention were included, while studies unrelated to personalized pressure injury prevention or treating pressure injury prevention as a secondary outcome were excluded.

Results:

From January 2015 to October 2025, a total of 2158 articles were identified, of which 78 studies were ultimately included in this review. Current applications mainly involve pressure monitoring, posture recognition, moisture and temperature sensing, and multimodal monitoring systems combined with machine learning algorithms. Most studies remained at the prototype development or preliminary validation stage, with limited large-scale clinical implementation.

Conclusions:

Although smart sensors demonstrate considerable potential for improving pressure injury prevention, current research is still largely limited to early-stage technological development and descriptive investigations. Several barriers hinder bedside translation, including the lack of high-quality clinical trials, insufficient involvement of nursing professionals during device development, poor device stability in complex clinical environments, and the black-box nature of machine learning algorithms. Future research should prioritize the development of explainable artificial intelligence systems to enhance clinical trust and facilitate adoption. Furthermore, future efforts should move beyond passive data collection toward closed-loop intervention systems capable of automatically delivering precise pressure-relief strategies based on individual tissue tolerance thresholds. Clinical Trial: PROSPERO CRD420251183646, registered 4 November 2025