• *The measurement-induced noise is significant*.

**Figure 5** shows the flowchart of data preprocessing of VIGOR [36, 41, 62, 63]. *θ*, *v* ! , where *<sup>θ</sup>* is the rotation angle about axis *<sup>v</sup>* !, indicates a joint's Quaternion rotation; h i *x*, *y*, *z* denotes a joint's position; *ft* indicates a joint's applied force, which is derived from inverse dynamics; h i *α*, *β*, *γ* indicates a joint's rotation under normalized Joint Coordinate System (JCS) – Euler angle. Its main implementation techniques include data fusion, inverse dynamics analysis, spatial normalization, Kalman filtering, and reconstruction of disable input channels. The kinetic data is stored in JSON format.

#### **Figure 5.**

*Flowchart of VIGOR's Preprocessing for kinetic movement identification.*

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

#### *3.1.1 Formulating musculoskeletal kinetic features*

Inverse dynamics analysis (IDA), which is derived from Newton-Euler Equations [60, 64–68], aims to calculate unknown kinetic information (the net joint forces and moments) from measured kinematic information (e.g., position, velocities and accelerations of joints) and measured kinetic information (e.g., ground reaction forces). As illustrated in **Figure 5**, given joint locations *xi*, *yi* , *zi* � �, Euler rotation *αi*, *βi*, *γ<sup>i</sup>* h i where *i* denotes the identity of a joint, and ground-reaction force *F*, the joint force *fi* and other musculoskeletal kinetic features can be computed via IDA.

As illustrated in **Figure 5**, VIGOR employs *inverse dynamics* to compute internal joint forces and moments with given ground reaction forces. Inverse dynamics is implemented by dividing the human body into multiple connected rigid bodies [69, 70], which correspond to relevant anatomical segments such as the thigh, calf, foot, arm, etc. The model's anthropometric properties (e.g., the mass and moment of inertia) are derived from statistical analysis. In addition, it is assumed that each joint is rotationally frictionless. The proposed methods in **Figure 5** can be customized to investigate the biomechanical response of human motion by considering different health issues such as cerebral palsy, poliomyelitis, spinal cord injury, and muscular dystrophy [67].

#### *3.1.2 Spatial normalization*

As addressed in Section 2, we can acquire joint positions and rotations, which are denoted as h i *x*, *y*, *z* and *θ*, *v* ! D E respectively. Both need to be normalized to ease and boost the gesture recognition: (1) *Normalization of Joint Rotations* through the interchangeable conversion among quaternion *θ*, *v* ! D E, Euler angle *<sup>α</sup>, <sup>β</sup>, <sup>γ</sup>*, and joint positions h i *x*, *y*, *z* [71]. (2) *Normalization of Joint Positions* using bone scaling [61], axis-oriented rotating of view angle, origin translating (which makes a user positioned at the center of a sensor), re-constructing h i *x*, *y*, *z* according to joint rotation [49], and polishing the kinetic curve using a Savitzky–Golay filter. Our preliminary experimental results demonstrate that the normalization techniques addressed above can greatly improve the quality of data (less noise and smoother kinetic performance) so as to achieve higher recognition [36, 41, 61].

#### *3.1.3 Recovering occlusion-induced missing data*

During sensory data acquisition, unavoidable occlusion may introduce missing data or lost-tracking. VIGOR employs spherical linear interpolation (SLERP) to fix the issues caused by short-term occlusion [35], and employs Kalman filter [72–74] to fix the missing information (including both position and rotation) caused by long-term occlusion. A preliminary comparison between the raw and preprocessed physical rehabilitation kinematic data is available on our online video [62, 63].

#### *3.1.4 Normalization of the kinetics of users with limited mobility*

In order to recognize the kinetic movement of users with disabilities, VIGOR normalizes their kinetic data by compensating the missing data incurred by disabled input channels: in the event that several input channels are disabled, the VIGOR model is able to construct the void input channels by taking the advantage of correlation among all inputs. Compensation can normalize the input data so that

VIGOR can achieve higher recognition rate, and its psychological and physiological benefits to users are also under our investigation. **Figure 6** demonstrates the application of deep neural network [37, 75] on compensating the missing channels introduced by limited mobility. As our preliminary contribution, multilayer perceptron (MLP), temporal convolutional neural network (tCNN) [46], and autoencoder methods are employed to construct disabled legs and the resulted recognition accuracy is improved [20].
