The purpose of this work is to develop a computational model to describe the task of sit to stand (STS). STS is an important movement skill which is frequently performed in human daily activities, but has rarely been studied from the perspective of optimization principles. In this study, we compared the recorded trajectories of STS with the trajectories generated by several conventional optimization-based models (i.e., minimum torque, minimum torque change and kinetic energy cost models) and also with the trajectories produced by a novel multi-phase cost model (MPCM). In the MPCM, we suggested that any complex task, such as STS, is decomposable into successive motion phases, so that each phase requires a distinct strategy to be performed. In this way, we proposed a multi-phase cost function to describe the STS task. The results revealed that the conventional optimization-based models failed to correctly predict the invariable features of STS, such as hip flexion and ankle dorsiflexion movements. However, the MPCM not only predicted the general features of STS with a sufficient accuracy, but also showed a potential flexibility to distinguish between the movement strategies from one subject to the other. According to the results, it seems plausible to hypothesize that the central nervous system might apply different strategies when planning different phases of a complex task. The application areas of the proposed model could be generating optimized trajectories of STS for clinical applications (such as functional electrical stimulation) or providing clinic

Trajectory of human movement during sit to stand: a new modeling approach based on movement decomposition and multi-phase cost function.

EMADI ANDANI, Mehran;
2013

Abstract

The purpose of this work is to develop a computational model to describe the task of sit to stand (STS). STS is an important movement skill which is frequently performed in human daily activities, but has rarely been studied from the perspective of optimization principles. In this study, we compared the recorded trajectories of STS with the trajectories generated by several conventional optimization-based models (i.e., minimum torque, minimum torque change and kinetic energy cost models) and also with the trajectories produced by a novel multi-phase cost model (MPCM). In the MPCM, we suggested that any complex task, such as STS, is decomposable into successive motion phases, so that each phase requires a distinct strategy to be performed. In this way, we proposed a multi-phase cost function to describe the STS task. The results revealed that the conventional optimization-based models failed to correctly predict the invariable features of STS, such as hip flexion and ankle dorsiflexion movements. However, the MPCM not only predicted the general features of STS with a sufficient accuracy, but also showed a potential flexibility to distinguish between the movement strategies from one subject to the other. According to the results, it seems plausible to hypothesize that the central nervous system might apply different strategies when planning different phases of a complex task. The application areas of the proposed model could be generating optimized trajectories of STS for clinical applications (such as functional electrical stimulation) or providing clinic
minimum angular jerk space; motor planning; movement decomposition; multi-phase cost function; optimization
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/879997
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