Sensor Integration for Gait Analysis

Yunus Celik, Rodrigo Vitório, Dylan Powell, Jason Moore, Fraser Young, Graham Coulby, James Tung, Mina Nouredanesh, Robert Ellis, Elena Izmailova, Samuel Stuart, Alan Godfrey*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Advancements in Microelectromechanical systems (MEMS) have enabled the manufacture of affordable and efficient wearable devices. In sensor-based gait analysis, motion and biofeedback sensor devices are easily attached to different parts of the body. Instrumentation of gait using different sensor technologies enables researchers and clinicians to capture high-resolution quantitative motion data within and beyond the lab. Integration of advanced sensor technologies provides objective and rater-independent multimodal outcomes that complement established clinical examination. Multi-modal data capture in ecologically valid, patient-relevant habitual settings opens new possibilities to monitor fluctuating and rare incidents by informing different aspects of impaired gait. Interconnected device communication and the Internet of Things (IoT) provide the infrastructural platform to enable remote gait assessment. However, an extended period of motion data recorded by different sensor technologies results in a vast amount of unlabeled data. Computational methods and artificial intelligence techniques (e.g., data mining) provide opportunities to manage data collected in unsupervised environments. Although technological advancement and algorithms promote remote gait assessment, more work needs to be done in terms of analytical and clinical validation to achieve robust and reliable gait analysis tools that contribute to better rehabilitation and treatment.
Original languageEnglish
Title of host publicationEncyclopedia of Sensors and Biosensors
EditorsRoger Narayan
Number of pages21
ISBN (Electronic)9780128225486
ISBN (Print)9780128225493
Publication statusPublished - 1 Jan 2023


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