# Bioengineering Innovations for Personalized Care in Low Back Pain: From Sensors to Smart Therapeutics

## TL;DR

This paper reviews how bioengineering innovations, like sensors and smart therapeutics, can improve personalized care for low back pain by enabling continuous monitoring and adaptive rehabilitation.

## Contribution

The paper introduces a framework for integrating biosensing technologies with clinical rehabilitation workflows to address individual variability in low back pain.

## Key findings

- Multimodal biosensing can track neuromuscular and movement patterns for personalized LBP care.
- Continuous monitoring allows early detection of non-response to rehabilitation, enabling timely adjustments.
- Integration of biosensing with transparent analytics supports adaptive, patient-centered rehabilitation.

## Abstract

Low back pain (LBP) remains one of the most prevalent and disabling musculoskeletal conditions worldwide, shaped by interacting mechanical, neurophysiological, inflammatory, vascular, and behavioral factors. Conventional care often relies on generalized exercise programs and episodic, predominantly subjective assessment, which can underrepresent inter-individual heterogeneity and longitudinal change. Recent bioengineering advances enable continuous, multimodal monitoring of objective correlates of function—neuromuscular activation and coordination (sEMG/polyEMG), movement patterns and activity exposure (IMU), and complementary physiological context (e.g., autonomic and perfusion-related signals). Rather than measuring pain directly, these signals can contextualize symptoms, support treatment stratification within non-surgical care, and enable trajectory monitoring with early non-response flags to guide timely rehabilitation adjustment under clinician oversight. When integrated with transparent, implementation-oriented analytics, biosensing can also support incremental closed-loop rehabilitation through patient-facing feedback and adaptive progression rules. This review synthesizes current and emerging biosensing approaches for LBP and highlights key translational requirements—outcome-linked validation, standardization, and workflow integration—to bridge engineering innovation with clinically usable, data-informed rehabilitation.