IEEE Transactions on Neural Systems and Rehabilitation Engineering · 2004 · 385 citations · 12 references
EngineeringMechatronics EngineeringRobotics EngineeringHuman-robot InteractionRoboticsSensor FusionHuman Movement MechanicsMotion AnalysisMotion CaptureMotion SensorsPose EstimationTracking SystemsAccelerometersHuman Motion AnalysisInertial SensingInertial SensorsKalman Filter FusionMulti-sensor Kalman FusionReal-time Motion TrackingReal-time Tracking Systems
Tracking human motion is essential for virtual environments. The paper presents and evaluates a real‑time human motion‑tracking system. The system uses tri‑axis MEMS accelerometers, gyros, and magnetometers combined with a Kalman‑based fusion algorithm to estimate dynamic orientations and positions, and was validated against conventional methods and an arm‑motion experiment. The method proved effective in real‑time human motion tracking.
A basic requirement in virtual environments is the tracking of objects, especially humans. A real time motion-tracking system was presented and evaluated in this paper. System sensors were built using tri-axis microelectromechanical accelerometers, rate gyros, and magnetometers. A Kalman-based fusion algorithm was applied to obtain dynamic orientations and further positions of segments of the subject's body. The system with the proposed algorithm was evaluated via dynamically measuring Euler orientation and comparing with other two conventional methods. An arm motion experiment was demonstrated using the developed system and algorithm. The results validated the effectiveness of the proposed method.
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Recognition of human body motion using phase space constraints
Lee W. Campbell, Aaron Bobick · 2002 · 298 citations