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Human movement analysis has become very popular in different domains, such as clinical gait analysis, rehabilitation, robotics, sports science, animation and entertainment, video surveillance, and smart houses.
Quantitative analysis of human movements and posture is an effective tool used to analyze the correct movement execution, identify injury risk factors, help clinicians make the best decision to reduce patients' recovery time, and suggest a proper treatment plan.
This tutorial presents an overview of the process of human movement analysis, from data capture to analysis and interpretation, for health-related applications.
This narrative review aims to provide an overview of the modeling of biomechanical systems used for the analysis of human movement within the framework of multibody dynamics, for those enrolled in engineering, clinical, rehabilitation and sports applications.
Highlights. •. Three innovative methods for explainable human movement models. •. Body dexterity analysis: Models reveal how artisans and operators perform tasks. •. Minimal motion descriptors to enhance task recognition. •. Artificial generation of full-body professional movements. •. Extensive experimentation with real-world datasets. Abstract.
When large amounts of information can be collected and analyzed, the appeal and “unreasonable effectiveness of data” (Halevy et al., 2009) has found fertile ground in the study of complex biological and physical systems, human movement science among them.
Laban movement analysis is an effective method to annotate human movement in dance that describes communication and expression. Technology-supported human movement analysis employs motion sensors, infrared cameras, and other wearable devices to capture critical joints of the human skeleton and facial key points.
Analyzing how humans move, how we coordinate our muscles, and what forces act on the musculoskeletal system is important for studying neuro-musculoskeletal conditions. Traditionally, measuring these quantities requires expensive laboratory equipment, a trained expert, and hours of analysis.
Our machine learning models predict parameters include walking speed (r = 0.73), cadence (r = 0.79), knee flexion angle at maximum extension (r = 0.83), and Gait Deviation Index (GDI), a...
AIMS AND OBJECTIVES. This tutorial presents the basics for understanding the applications of human motion capture and motion-based interaction for health and related areas. Participants will be...