A numerical procedure for inferring from experimental data the optimization cost functions using a multibody model of the neuro-musculoskeletal system
A numerical procedure for inferring from experimental data the optimization cost functions using a multibody model of the neuro-musculoskeletal system
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DOI：
10.1007/s11044-006-9019-1
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发表时间：
2006-09-01
影响因子：
3.4
通讯作者：
Sartirana, Stefano
中科院分区：
文献类型：
--
作者：
Bottasso, Carlo L.;Prilutsky, Boris I.;Sartirana, Stefano
我们提出了一个计算过程推断的成本函数，根据最优性原则，实验观察到的电机策略的基础。在目前使用的神经肌肉骨骼系统的优化为基础的数学模型，成本函数是不知道的先验，因为他们不能直接观察或测量的真实的生物系统。因此，成本函数需要假设为任何给定的电机任务的兴趣，洞察的基础上的物理过程，管理的problem.This工作试图克服需要假设的成本函数，从实验数据中提取这种非直接观察到的信息。在此，使用（a）生物系统的数学模型;以及（B）可能的成本函数的参数数学模型，即，以这样的方式构造的搜索空间，即，假定包含生物系统在给定的感兴趣的运动任务中使用的未知函数，间接地导出所观察的运动任务的最优性标准。通过求解嵌套优化问题，在搜索空间内识别与实验数据最匹配的成本函数。这个问题可以被改写为一个非线性规划问题，因此解决了使用标准techniques.The方法在这里制定的静态和动态问题，然后测试有代表性的例子。
We propose a computational procedure for inferring the cost functions that, according to the Principle of Optimality, underlie experimentally observed motor strategies. In the current use of optimization-based mathematical models of neuro-musculoskeletal systems, the cost functions are not known a-priori, since they can not be directly observed or measured on the real bio-system. Consequently, cost functions need to be hypothesized for any given motor task of interest, based on insight into the physical processes that govern the problem.This work tries to overcome the need to hypothesize the cost functions, extracting this non-directly observable information from experimental data. Optimality criteria of observed motor tasks are here indirectly derived using: (a) a mathematical model of the bio-system; and (b) a parametric mathematical model of the possible cost functions, i.e. a search space constructed in such a way as to presumably contain the unknown function that was used by the bio-system in the given motor task of interest. The cost function that best matches the experimental data is identified within the search space by solving a nested optimization problem. This problem can be recast as a non-linear programming problem and therefore solved using standard techniques.The methodology is here formulated for both static and dynamic problems, and then tested on representative examples.