Position paper on how technology for human motion analysis and relevant clinical applications have evolved over the past decades: Striking a balance between accuracy and convenience - Bonato, Paolo; Feipel, Véronique; Corniani, Giulia; Arin-Bal, Gamze; Leardini, Alberto (Gait & posture, 2024)
Over the past decades, tremendous technological advances have emerged in human motion analysis (HMA). How has technology for analysing human motion evolved over the past decades, and what clinical applications has it enabled? The literature on HMA has been extensively reviewed, focusing on three main approaches: Fully-Instrumented Gait Analysis (FGA), Wearable Sensor Analysis (WSA), and Deep-Learning Video Analysis (DVA), considering both technical and clinical aspects. FGA techniques relying on data collected using stereophotogrammetric systems, force plates, and electromyographic sensors have been dramatically improved providing highly accurate estimates of the biomechanics of motion. WSA techniques have been developed with the advances in data collection at home and in community settings. DVA techniques have emerged through artificial intelligence, which has marked the last decade. Some authors have considered WSA and DVA techniques as alternatives to “traditional” HMA techniques. They have suggested that WSA and DVA techniques are destined to replace FGA. We argue that FGA, WSA, and DVA complement each other and hence should be accounted as “synergistic” in the context of modern HMA and its clinical applications. We point out that DVA techniques are especially attractive as screening techniques, WSA methods enable data collection in the home and community for extensive periods of time, and FGA does maintain superior accuracy and should be the preferred technique when a complete and highly accurate biomechanical data is required. Accordingly, we envision that future clinical applications of HMA would favour screening patients using DVA in the outpatient setting. If deemed clinically appropriate, then WSA would be used to collect data in the home and community to derive relevant information. If accurate kinetic data is needed, then patients should be referred to specialized centres where an FGA system is available, together with medical imaging and thorough clinical assessments. •Gait analysis by stereophotogrammetry provides highly accurate biomechanics of motion.•Wearable sensors enable ecological data collection at home and in community setting.•Deep-learning video analysis can perform motion capture without instruments on subjects.•Several authors suggest more modern technologies shall replace traditional gait analysis.•We argue that these three technologies complement each other and shall be “synergistic”.