Sensor Integration for Gait Analysis

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.