Wearable inertial sensors for human movement analysis: a five-year update

Area of Science:

• Biomedical Engineering
• Human Movement Science
• Digital Health

Background:

• Wearable inertial measurement units (IMUs) have evolved from controlled laboratory environments to real-world applications.
• The focus has shifted towards long-term, unsupervised monitoring of human movement in naturalistic settings.

Purpose of the Study:

• To review the evolution of wearable IMUs in human movement analysis.
• To identify and synthesize emerging application areas for IMUs in clinical and applied settings.
• To provide practical guidance for researchers and clinicians on contemporary IMU use.

Main Methods:

• Systematic review of scientific literature on wearable IMUs.
• Identification and synthesis of four main application areas: mobile health, occupational ergonomics, rehabilitation, and cognitive assessment.
• Focus on applied and clinical perspectives for practical guidance.

Main Results:

• Wearable IMUs are increasingly used in unsupervised, naturalistic settings.
• Key application areas include mobile health (frailty, falls, neurological diseases, active living), occupational ergonomics, rehabilitation, and cognitive assessment.
• Successful implementation requires machine learning for data interpretation.

Conclusions:

• IMU-based wearable devices have rapidly transitioned to applied, unsupervised settings.
• Machine learning algorithms are crucial for managing and interpreting the large datasets generated by IMUs.
• Effective use of IMUs in applied settings depends on extracting clinically relevant and interpretable information for practitioners.