SynthesisSensors (Basel, Switzerland)2022
Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review.
Synthesis in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- IMU- and Vision-Based Measurement Techniques for Joint Kinematics: A Narrative Review.Sensors (Basel, Switzerland) · 2026Review
- A Multimodal Dataset of Full-Body Kinematics and Kinetics from Laboratory- and Wearable-Based Systems.Scientific data · 2026Article
- Generalization of Knee Joint Moment Prediction During Drop Vertical Jumps Under Graded Visuo-Proprioceptive Conflict: The Role of Multijoint Kinematics Across Validation Frameworks.Bioengineering (Basel, Switzerland) · 2026Article
- Distinct patterns in neuromuscular adaptation to repeated perturbations in chronic ankle instability.Journal of neuroengineering and rehabilitation · 2025Article
- Analytical Formulation of Relationship Between Sensors and Euler Angle Errors for Arbitrary Stationary Alignment Based on Accelerometer and Magnetometer.Sensors (Basel, Switzerland) · 2025Article
- Relationship Between Lower-Extremity Co-Contraction and Jerk During Gait.Sensors (Basel, Switzerland) · 2025Article
- The perceptual and biomechanical effects of scaling back exosuit assistance to changing task demands.Scientific reports · 2025Article
- Learning based lower limb joint kinematic estimation using open source IMU data.Scientific reports · 2025Article
- Anthropometry and diagnostic aware deep learning for exercise assessment.Frontiers in medical technology · 2025Article
- Improving Biological Joint Moment Estimation During Real-World Tasks With EMG and Instrumented Insoles.IEEE transactions on bio-medical engineering · 2024Article
- Application of Machine Learning Methods to Investigate Joint Load in Agility on the Football Field: Creating the Model, Part I.Sensors (Basel, Switzerland) · 2024Article
- Accessibility of Motion Capture as a Tool for Sports Performance Enhancement for Beginner and Intermediate Cricket Players.Sensors (Basel, Switzerland) · 2024Review
- Estimating Compressive and Shear Forces at L5-S1: Exploring the Effects of Load Weight, Asymmetry, and Height Using Optical and Inertial Motion Capture Systems.Sensors (Basel, Switzerland) · 2024Article
- IMU-Based Energy Expenditure Estimation for Various Walking Conditions Using a Hybrid CNN-LSTM Model.Sensors (Basel, Switzerland) · 2024Article
- Using inertial measurement units to estimate spine joint kinematics and kinetics during walking and running.Scientific reports · 2024Article
- Estimating 3D kinematics and kinetics from virtual inertial sensor data through musculoskeletal movement simulations.Frontiers in bioengineering and biotechnology · 2024Article
- Article
- Validation of Inertial Measurement Units for Analyzing Golf Swing Rotational Biomechanics.Sensors (Basel, Switzerland) · 2023Article
- Wireless pressure insoles for measuring ground reaction forces and trajectories of the centre of pressure during functional activities.Scientific reports · 2023Observational
- Proof of Concept Testing of Safe Patient Handling Intervention Using Wearable Sensor Technology.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
In biomechanics, joint kinetics has an important role in evaluating the mechanical load of the joint and understanding its motor function. Although an optical motion capture (OMC) system has mainly been used to evaluate joint kinetics in combination with force plates, inertial motion capture (IMC) systems have recently been emerging in joint kinetic analysis due to their wearability and ubiquitous measurement capability. In this regard, numerous studies have been conducted to estimate joint kinetics using IMC-based wearable systems. However, these have not been comprehensively addressed yet. Thus, the aim of this review is to explore the methodology of the current studies on estimating joint kinetic variables by means of an IMC system. From a systematic search of the literature, 48 studies were selected. This paper summarizes the content of the selected literature in terms of the (i) study characteristics, (ii) methodologies, and (iii) study results. The estimation methods of the selected studies are categorized into two types: the inverse dynamics-based method and the machine learning-based method. While these two methods presented different characteristics in estimating the kinetic variables, it was demonstrated in the literature that both methods could be applied with good performance for the kinetic analysis of joints in different daily activities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.