Predicting Sit-to-Stand Adaptations due to Muscle Strength Deficits and Assistance Trajectories to Complement Them

FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY(2022)

引用 0|浏览4
暂无评分
摘要
Sit-to-stand (STS) transition is one of the most bio-mechanically challenging task necessary for performing activities of daily life. With muscle strength being the most dominant, many co-occurring factors influence how individuals perform STS. This study investigates the STS changes and STS failure caused by strength deficits using the trajectories generated employing an open-loop single shooting optimization framework and musculoskeletal models. The strength deficits were introduced by simultaneously scaling the maximum isometric strength of muscles in steps of 20%. The optimization framework could generate successful STS transitions for models with up to 60% strength deficits. The joint angle kinematics, muscle activation patterns, and the ground reaction forces from the 0% strength deficit model's STS transition match those observed experimentally for a healthy adult in literature. Comparison of different strength deficit STS trajectories shows that the vasti muscle saturation leads to reduced activation of the antagonistic hamstring muscle, and consequently, the gluteus maximus muscle saturation. Subsequently, the observation of reduced hamstring activation and the motion tracking results are used to suggest the vasti muscle weakness to be responsible for STS failure. Finally, the successful STS trajectory of the externally assisted 80% strength deficit model is presented to demonstrate the optimization framework's capability to synthesize assisted STS transition. The trajectory features utilization of external assistance as and when needed to complement strength deficits for successful STS transition. Our results will help plan intervention and design novel STS assistance devices.
更多
查看译文
关键词
sit-to-stand, musculoskeletal model, strength deficit, single shooting optimization, open loop controller, assist-as-needed
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要