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Published on: March 14, 2017
Gait Analysis Using an Artificial Intelligence-Based Motion Capture System With a Single Smartphone Camera
Takuya Usami1, Masaya Kisohara2, Kazuki Nishida3
1Department of Orthopedic Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, JPN.
Introduction:
Motion capture is widely used to analyze human gait and enables measurement of various biomechanical parameters. However, conventional infrared-based motion-capture systems are expensive and require a large amount of space, making them difficult to implement in many facilities. Recently, artificial intelligence (AI) has been applied in numerous medical fields, including gait analysis. This study aimed to evaluate the effectiveness of an AI-based motion capture system using a single smartphone camera compared to a conventional infrared-based motion capture system.
Methods And Analysis:
Twenty-two straight walks of healthy volunteers were simultaneously captured using a smartphone (iPhone X®, Apple Inc., Cupertino, CA) placed on the right side of the participants (Group AI) and an infrared-based motion capture system (Group M). In Group AI, gait videos were evaluated by the Sportip Motion 3D AI-based motion capture system (Sportip Inc., Tokyo, Japan). The same walking cycles were analyzed for both methods. Gait parameters, including gait velocity, gait cycle time, step length, and flexion angles of the hip and knee joints, were compared between the two groups.
Results:
The shapes of the hip and knee flexion angle graphs in Group AI were similar to those in Group M. Variables, such as gait velocity, bilateral step length, and maximum flexion angle of the hip and knee joints, showed high accuracy. Most variables showed high correlation coefficients (gait velocity, r = 0.94; right and left step lengths, r = 0.91 and 0.93; right and left maximum flexion angle of the hip joint, r = 0.87 and 0.71; knee joint, r = 0.84 and 0.93; right and left minimum flexion angles of the hip joint, r = 0.73 and 0.75). However, low correlation coefficients were observed in gait cycle time (r = 0.68) and minimum knee flexion angle (right and left, r = 0.30 and 0.47).
Conclusion:
Our findings suggest that an AI-based motion capture system using a single smartphone camera may provide reliable gait parameters for certain applications.
