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Inertial Sensors for Human Motion Analysis: A Comprehensive Review

Sara García-de-Villa, David Casillas-Pérez, Ana Jiménez-Martín, Juan Jesús García-Domínguez

TL;DR

The paper addresses the need to consolidate knowledge on inertial sensor-based human motion analysis by systematically surveying IMU literature up to Aug 2022. It analyzes sensor configurations, target motion units, applications, algorithms (especially sensor fusion and ML), validation, and subject demographics, highlighting a shift toward 3D, full-body kinematics and sparse IMU strategies. Key findings show prevalent use of EKF/KF-based fusion, rising DL/LSTM/CNN methods, and RMSE improvements in ML approaches, though data availability remains a bottleneck. The study underscores the practical impact of enabling in-field motion analysis beyond costly optical systems, while calling for standardized datasets and broader population validation to enable generalizable, clinically relevant solutions.

Abstract

Inertial motion analysis is having a growing interest during the last decades due to its advantages over classical optical systems. The technological solution based on inertial measurement units allows the measurement of movements in daily living environments, such as in everyday life, which is key for a realistic assessment and understanding of movements. This is why research in this field is still developing and different approaches are proposed. This presents a systematic review of the different proposals for inertial motion analysis found in the literature. The search strategy has been carried out on eight different platforms, including journal articles and conference proceedings, which are written in English and published until August 2022. The results are analyzed in terms of the publishers, the sensors used, the applications, the monitored units, the algorithms of use, the participants of the studies, and the validation systems employed. In addition, we delve deeply into the machine learning techniques proposed in recent years and in the approaches to reduce the estimation error. In this way, we show an overview of the research carried out in this field, going into more detail in recent years, and providing some research directions for future work

Inertial Sensors for Human Motion Analysis: A Comprehensive Review

TL;DR

The paper addresses the need to consolidate knowledge on inertial sensor-based human motion analysis by systematically surveying IMU literature up to Aug 2022. It analyzes sensor configurations, target motion units, applications, algorithms (especially sensor fusion and ML), validation, and subject demographics, highlighting a shift toward 3D, full-body kinematics and sparse IMU strategies. Key findings show prevalent use of EKF/KF-based fusion, rising DL/LSTM/CNN methods, and RMSE improvements in ML approaches, though data availability remains a bottleneck. The study underscores the practical impact of enabling in-field motion analysis beyond costly optical systems, while calling for standardized datasets and broader population validation to enable generalizable, clinically relevant solutions.

Abstract

Inertial motion analysis is having a growing interest during the last decades due to its advantages over classical optical systems. The technological solution based on inertial measurement units allows the measurement of movements in daily living environments, such as in everyday life, which is key for a realistic assessment and understanding of movements. This is why research in this field is still developing and different approaches are proposed. This presents a systematic review of the different proposals for inertial motion analysis found in the literature. The search strategy has been carried out on eight different platforms, including journal articles and conference proceedings, which are written in English and published until August 2022. The results are analyzed in terms of the publishers, the sensors used, the applications, the monitored units, the algorithms of use, the participants of the studies, and the validation systems employed. In addition, we delve deeply into the machine learning techniques proposed in recent years and in the approaches to reduce the estimation error. In this way, we show an overview of the research carried out in this field, going into more detail in recent years, and providing some research directions for future work
Paper Structure (22 sections, 3 equations, 14 figures, 2 tables)

This paper contains 22 sections, 3 equations, 14 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Materials and Methods
  3. Eligibility Criteria
  4. Literature Search Strategy
  5. Publisher and Years
  6. Data Extraction, Analysis and Examination
  7. Review findings
  8. Sensors
  9. Application
  10. Monitored Motion Unit
  11. Adopted Algorithms
  12. Sensor fusion filters
  13. Data science algorithms
  14. Other algorithms
  15. Approaches for error reduction
  16. ...and 7 more sections

Figures (14)

  • Figure 1: Number of publications focused on the inertial motion analysis, referred to obtaining kinematic parameters by using portable inertial sensors, found in the literature.
  • Figure 2: PRISMA search strategy flowchart.
  • Figure 3: Distribution of the publications related to human motion analysis and IMUs, sorted by the semantic information obtained. This work focuses on the topic included in the left square of the lower row: kinematic parameters with magneto-inertial measurements.
  • Figure 4: Distribution of the papers with respect to the type of publication environment in which they were published. Top: conference and journals distribution. Bottom: journals that published the analyzed works.
  • Figure 5: Year of publication of the reviewed papers. Top: trend since the first motion analysis related work until nowadays. Bottom: distribution of publications in last 5-year period.
  • ...and 9 more figures