Application of Machine Learning Techniques in the Measurement of Ground Reaction Force (GRF) waveforms using Inertial Measurement Unit (IMU) Sensors -A Systematic Literature Review

Three-dimensional ground reaction forces (GRFs) are important parameters in human motion analysis, yet their measurement is confined to laboratory settings. GRF estimation from wearable inertial measurement unit (IMU) data using machine learning and deep learning has emerged as a promising avenue for kinetic analysis outside the laboratory, but no consensus on standardized methods or best practices has emerged. This review synthesizes existing methods and provides a framework of current practices and recommendations for future work. The review followed PRISMA guidelines (PROSPERO CRD42024578576) and searched PubMed, Scopus, IEEE Xplore, and ScienceDirect from 2011 to 2026. Studies were included if they used IMUs and machine learning or deep learning to estimate GRFs and reported quantitative performance metrics. Data were extracted on participant characteristics, IMU specifications and placement, preprocessing, models, validation protocols, and error metrics. Twenty-six studies met the inclusion criteria, with sample sizes ranging from 5 to 47 (median = 15) and IMU counts ranging from 1 to 16. Most studies used triaxial accelerometers and gyroscopes sampled at 100-1,000 Hz. Vertical GRF was generally estimated more accurately than the anteroposterior and mediolateral components within the same study. A single cross-study accuracy ranking was not defensible because studies used different normalization denominators, evaluation windows, tasks, sensor configurations, and validation strategies. Heterogeneity in validation protocols and demographic samples limited direct cross-study comparisons. This review synthesizes how implementation choices, including model architecture, feature-engineering strategy, sensor configuration, and validation protocol, shape GRF prediction across axes. The variability observed underscores the need for standardized methodologies, larger and more diverse cohorts, consistent performance metrics, and benchmarking on public datasets to enable fair cross-study comparisons and accelerate clinical translation.