**4.4 Construction of virtual limb using active orthosis**

In order to provide users with physical support, the generated virtual limb can be re-constructed on motoring system to drive Hip–knee–ankle–foot orthoses (HKAFOs) [97, 98]. Paralysis of hip abductor muscles is one of the most common reasons for prescribing HKAFOs. They can incorporate flexion–extension and abduction–adduction control and have free or locking joints [99]. Different from passive and semi-active orthoses, the HKAFOs have basically built-in power supplies, one or more actuators for moving the joint, the sensors for getting feedback data [97].

The designed active orthosis is shown in **Figure 12(A)**. Knee and ankle are considered rigid; but with locking mechanisms located at the hip and knee joints, and these parts can move anytime person desires. Therefore, in consequence of any adverse motion, the limb will be protected from harm. Also, in the active orthosis, the system acts from the hip zone and only performs "flexion" and "extension" motions. The HKAFO has two mechanical structures: (1) the gear and T type deflector reducer mechanism to transmit the generated torques of an actuator to the hip joints; and (2) pulley and four-bar mechanism, which is used for transferring the generated torque to the knee joints. With the mechanical system used for the motor to move in both directions, also provided power save, it is being aimed to

#### **Figure 12.**

*Hip-knee-ankle-foot-orthosis (HKAFO): (A) system configuration, with T-type deflector reducer; (B) control circuit (online video: [98]).*

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

reduce battery consumption to minimum which was a huge problem in these devices. Illustration of the control circuit is shown in **Figure 12(B)**. The patient's intention to perform a flexion or extension motion is detected by both EMG and accelerometer sensors. In order to determine the last location of the patient after movement, physical feedback is utilized from the mechanical system. Adding the new ankle joint to HKAFOs for real-time virtual limb can also be considered.

The EMG signals may be subject to preprocessing to remove unwanted interference; the most common sources of interference are power line harmonics and motion artifact from electrode movement. As myoelectric signals have a time sequence with a random number of elements, it is not practical for classification. Therefore, the signal sequence should be mapped to feature vectors. Feature vectors of EMG signals are classified to detect which movement produces specific results. Deep neural networks, fuzzy logic, finite state machine and support vector machine, etc. may be adopted as classifiers. In this work, the Finite State Machine (FSM) was chosen as a classifier. The FSM consists of a status set, input, output, event set, and state transition functions. The behavior of each system's state is characterized by a possible system state. Here, the transitions between output states are provided, depending on the input variable and the present state of the system. The EMG signals and the accelerometer data collected from both legs are classified using the FSM method. The result of this classification is used for three different situations for actuator input. These situations are: the patient stops, moves right leg or moves left leg, respectively.
