Technological methods for sensors’ data analysis for Y-balance test results: A systematic review
Journal
Array
Date Issued
2026-07
Author(s)
Pimenta, Luís
Lopes, Mário
Al-Jumaili, Saif
Albuquerque, Carlos
Coelho, Paulo Jorge
Pires, Ivan Miguel
Branco, Frederico
DOI
10.1016/j.array.2026.100846
Abstract
Objective
To evaluate the impact of sensor integration on YBT outcomes, including data precision, injury risk prediction, real-time monitoring, and athletic performance assessment. A secondary objective is to identify gaps in automated real-time evaluation methods.
Methods
A systematic review was conducted using a search window covering studies published between 2020 and 2026. After screening and eligibility assessment, the final included studies were published between 2021 and 2025. This review focused on studies that either applied sensor-based or technology-assisted methods directly to Y-Balance Test assessment or used YBT as a functional outcome alongside technology-supported measurement approaches.
Results
After screening and eligibility assessment, 21 studies met the inclusion criteria and were included in the qualitative and descriptive synthesis. The reviewed studies suggest that technology-assisted approaches can broaden the assessment of Y-Balance Test performance by adding biomechanical, functional, or task-related information beyond conventional manual scoring. Several studies reported improved monitoring of balance-related outcomes or intervention-related changes, but direct evidence for improved measurement precision and formal injury prediction was limited.
Conclusions
Sensor-assisted approaches in YBT show promising potential to improve measurement objectivity and broaden functional assessment in clinical and athletic settings. However, the current literature does not yet demonstrate a fully automated or real-time YBT system, and further development is required before such applications can be considered established for routine practice. Future progress will require larger and more diverse cohorts, methodological standardization, robust validation procedures, and the development of portable real-time YBT-specific systems suitable for routine implementation.
Significance
This review contributes a structured evidence map of sensor-assisted YBT research and highlights the gap between existing technology-supported assessment approaches and truly automated, real-time, YBT-specific systems.
To evaluate the impact of sensor integration on YBT outcomes, including data precision, injury risk prediction, real-time monitoring, and athletic performance assessment. A secondary objective is to identify gaps in automated real-time evaluation methods.
Methods
A systematic review was conducted using a search window covering studies published between 2020 and 2026. After screening and eligibility assessment, the final included studies were published between 2021 and 2025. This review focused on studies that either applied sensor-based or technology-assisted methods directly to Y-Balance Test assessment or used YBT as a functional outcome alongside technology-supported measurement approaches.
Results
After screening and eligibility assessment, 21 studies met the inclusion criteria and were included in the qualitative and descriptive synthesis. The reviewed studies suggest that technology-assisted approaches can broaden the assessment of Y-Balance Test performance by adding biomechanical, functional, or task-related information beyond conventional manual scoring. Several studies reported improved monitoring of balance-related outcomes or intervention-related changes, but direct evidence for improved measurement precision and formal injury prediction was limited.
Conclusions
Sensor-assisted approaches in YBT show promising potential to improve measurement objectivity and broaden functional assessment in clinical and athletic settings. However, the current literature does not yet demonstrate a fully automated or real-time YBT system, and further development is required before such applications can be considered established for routine practice. Future progress will require larger and more diverse cohorts, methodological standardization, robust validation procedures, and the development of portable real-time YBT-specific systems suitable for routine implementation.
Significance
This review contributes a structured evidence map of sensor-assisted YBT research and highlights the gap between existing technology-supported assessment approaches and truly automated, real-time, YBT-specific systems.
Subjects