**4.3 Formulating the kinetics of virtual limbs using the measured kinetics of functional body parts**

As described in Eqs. (2) and (4), the generation of virtual limb kinetics consists of two steps: (1) create preliminary kinetics of virtual limbs according to the measured kinetics of functional body parts; and (2) correct the preliminary kinetics using time series prediction models such as ARIMA. This subsection will focus on Step (1) because it faces more technical challenges.

### *4.3.1 Configuration of network architecture according human anatomy*

It is known that any system can be regarded as a hierarchical structure (i.e., system ! subsystem ! sub-subsystem, ...). As illustrated in **Figure 10(a)**, the human body system can be always divided into sub-components that are mechanically correlated. Inspired by the Bayesian network, we propose a visible and hierarchical neural network (VHNN), which is derived from human anatomy, to accurately formulate a system. As illustrated in **Figure 10(b)**, a sample visible and hierarchical neural network, which is directly derived from the human body

#### **Figure 10.**

*(a) Hierarchical human anatomy; (b) visible and hierarchical neural network derived from human anatomy.*

## *VIGOR: A Versatile, Individualized and Generative ORchestrator to Motivate the Movement… DOI: http://dx.doi.org/10.5772/intechopen.96025*

system, is employed to specify the musculoskeletal kinematics. The VHNN can be employed in virtual limb generation, 4D kinetic behavior recognition, and individualized Tai-Chi choreography (to be discussed in the remaining sections). Preliminary experimental results demonstrate that VHNN is superior to a classical neural network from the point of view of training speed and stability.
