A 3D Human-Machine Integrated Design and Analysis Framework for Squat Exercises with a Smith Machine
AbstractIn this paper, we propose a three-dimensional design and evaluation framework and process based on a probabilistic-based motion synthesis algorithm and biomechanical analysis system for the design of the Smith machine and squat training programs. Moreover, we implemented a prototype system to validate the proposed framework. The framework consists of an integrated human–machine–environment model as well as a squat motion synthesis system and biomechanical analysis system. In the design and evaluation process, we created an integrated model in which interactions between a human body and machine or the ground are modeled as joints with constraints at contact points. Next, we generated Smith squat motion using the motion synthesis program based on a Gaussian process regression algorithm with a set of given values for independent variables. Then, using the biomechanical analysis system, we simulated joint moments and muscle activities from the input of the integrated model and squat motion. We validated the model and algorithm through physical experiments measuring the electromyography (EMG) signals, ground forces, and squat motions as well as through a biomechanical simulation of muscle forces. The proposed approach enables the incorporation of biomechanics in the design process and reduces the need for physical experiments and prototypes in the development of training programs and new Smith machines. View Full-Text
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Lee, H.; Jung, M.; Lee, K.-K.; Lee, S.H. A 3D Human-Machine Integrated Design and Analysis Framework for Squat Exercises with a Smith Machine. Sensors 2017, 17, 299.
Lee H, Jung M, Lee K-K, Lee SH. A 3D Human-Machine Integrated Design and Analysis Framework for Squat Exercises with a Smith Machine. Sensors. 2017; 17(2):299.Chicago/Turabian Style
Lee, Haerin; Jung, Moonki; Lee, Ki-Kwang; Lee, Sang H. 2017. "A 3D Human-Machine Integrated Design and Analysis Framework for Squat Exercises with a Smith Machine." Sensors 17, no. 2: 299.
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