# Application of Nanogenerators in Lumbar Motion Monitoring: Fundamentals, Current Status, and Perspectives

**Authors:** Yudong Zhao, Hongbin He, Junhao Tong, Tianchang Wang, Shini Wang, Zhuoran Sun, Weishi Li, Siyu Zhou

PMC · DOI: 10.3390/diagnostics15202657 · Diagnostics · 2025-10-21

## TL;DR

Nanogenerators can power and monitor lumbar motion without batteries, offering new possibilities for back pain diagnosis and recovery tracking.

## Contribution

This review highlights the novel use of nanogenerators for self-powered lumbar motion monitoring and their integration with IoT and AI.

## Key findings

- Nanogenerators convert biomechanical energy into electrical energy for self-powered motion sensing.
- NG-based systems offer high-sensitivity, continuous monitoring for low back pain and postoperative recovery.
- Integration with IoT and AI could lead to intelligent, self-sustaining platforms for spine medicine.

## Abstract

Nanogenerators (NGs), especially triboelectric nanogenerators (TENGs), represent an emerging technology with great potential for self-powered lumbar motion monitoring. Conventional wearable systems for assessing spinal kinematics are often limited by their reliance on external power supplies, hindering long-term and real-time clinical applications. NGs can convert biomechanical energy from lumbar motion into electrical energy, providing both sensing and power-generation capabilities in a single platform. This review summarizes the fundamental working mechanisms, device architectures, and current progress of NG-based motion monitoring technologies, with a particular focus on their applications in lumbar spine research and clinical rehabilitation. By enabling high-sensitivity, continuous, and battery-free monitoring, NG-based systems may enhance the diagnosis and management of low back pain (LBP) and postoperative recovery assessment. Furthermore, the integration of NGs with wearable electronics, the Internet of Things (IoT), and artificial intelligence (AI) holds promise for developing intelligent, self-sustaining monitoring platforms that bridge biomedical engineering and spine medicine.

## Full-text entities

- **Diseases:** LBP (MESH:D017116)

## Full text

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## Figures

3 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12564619/full.md

## References

63 references — full list in the complete paper: https://tomesphere.com/paper/PMC12564619/full.md

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Source: https://tomesphere.com/paper/PMC12564619