CRONOGRAMA DE IMPLEMENTACIÓN

According to [133], physical activity is any bodily movement. As a result, these activities can easily be detected and quantied to determine the rate of locomotion or the extend of movement of the user. Accelerometers and gyroscopes have been widely used as inertial measurement units during gait analysis [134]. In some cases, these sensors are used to determine both anatomical and orientation angles of the participant during gait analysis [135]. The sensor placement pro- cedure and the calibration of these sensors have been regarded as complex and tedious. This is mainly because the human body is not at, neither is it uniform nor similar from one individual to another. Therefore, results can either be reported for static or dynamic experiments. When these sensors are used for clinical diagnosis, it is advisable to report the timing accuracy of the classications instead of the accuracy on event counts [136]. Although these sensors had been used within a system, it is possible to measure the behavior or the orientation of a single limb in space [137] using the individual sensor technique.

The introduction of an orientation measurement system in myoelectric control systems en- ables the use of control interlocks within the control architecture. The position of the thigh and the shank is known to vary with the stage within the gait cycle [138]. Therefore, individual sections of the limb could be monitored independently. It is then prudent to use the individual sensor outputs to determine the position of the limb. The output from such sensors could also be used as feedback systems to provide a complete closed loop control system. This will, in turn, allow for adaptive control resulting in a more robust control architecture.

The main characteristic of a control system designed for prosthetic applications is to achieve the desired trajectory with minimum to no errors. As a result, the determination of kinematic models of the lower limb is a prerequisite for proper motion planning. Computations of the position and orientation are based on the joint position and kinematics of the limb, hence these transformations require actuator space, joint space and Cartesian space [139]. The success of lower limb rehabilitation designs are constrained by foot-ankle motion, degrees of freedom and forces determined by computational models. However, a combination of reliable measurements and mathematical modelling techniques has proved to be a powerful tool for investigating the complicated behaviour of human joints for the past three decades [139], [140], [141] Any model of a joint, often inspired by engineering, is related to an idea of a mechanical analogy (hinge joint, ball-and-socket joint, universal joint) that behaves in a similar manner as a biological joint [142]. The kinematics model represents the motion of the prosthetic limb without considering the forces that cause the motion. The dynamic model establishes the relationships between the motion and the forces involved, taking into account the masses and moments of inertia, for in- stance, the dynamic model considers the masses and inertias involved and relates the forces with the observed motion, or instead calculates the forces necessary to produce the required motion [139].

Su et al. [143], clearly outlined that the validation of the two or four segment models pro- posed by [144], [145] and [146] have not yet been done regarding repeatability and reliability. Therefore, the applicability of such models is still questionable during the design of rehabilitation devices of the lower limb. According to [143], a three-dimensional motion analysis of the foot and ankle is more relevant.

for a stable and ecient amputee gait [147]. Motion at the ankle joint is usually divided into that at the ankle and at the subtalar joints [148]. Tulchin et al. [149] argued that the previous work done by [150] and [151] of modelling a foot as a single rigid body was not adequate since the detailed motion between individual joints within the foot cannot be appreciated. The group went on to propose a multi-segmented foot model which comprises of a two joint model of hind-foot and fore-foot motion. This model was successful in determining the foot kinematics during high speeds. However, these early foot and ankle models all lacked the consideration of the internal foot movements [148].

According to [152], the mean overall rotation is much higher at the ankle 630 _{than at subtalar}

joint (40_{) regarding the exion on maximal dorsi- to maximal plantar-exion. Lundgren et al.}

[153] also reported that during the stance phase of walking, the joint rotations in the three anatomical planes were found to be on a range of about 80 _{to 15}0 _{at the ankle joint, and about}

70 to 100 at the subtalar joint. Thus, human motor control has always acted as an inspiration in both robotic manipulator design and control. The development of a human lower limb robotic prosthesis is highly motivated by the human foot ankle mechanics, its dexterity, and its vast repertoire of motion. Figure 2.3 shows the terminology for indicating the spatial location, relative position and motion of bones and tissues in the foot, illustrated for a right foot, displayed from the front. The the planes are indicated with bold font, axes names are underlined, directions are indicated with italic font and motions are in capital font [154].

Figure 2.3: The human foot and ankle

An understanding of the mathematical model of the human ankle is of paramount impor- tance in developing a control and electro-mechanical system of the robotic prosthesis. The human shank and foot complex is an intricate, multi-joint mechanism, which is fundamental for the in- teraction between the lower limb and ground during locomotion [149]. At the same time, the ankle has a very complicated anatomical system. As a result, the large amount of literature on

experimental and modelling studies has not fully described the coupled joint motion, position and orientation of the joint axis of rotation, stress and strain in the ligaments. Even the ankle's role on guiding and stabilising joint motion, conformity and congruency of the articular surfaces, patterns of contact at the articular surfaces, patterns of rolling and sliding at the joint surfaces, and muscle lever arm lengths are yet to be fully exploited [147].

Moreover, most models [155], [156], [157] considered for computational model analysis were based on the hinge joint. The suitability of the hinge joint assumption is supported by early studies [158], [159], [160] which show only one or two rotation axes, but other studies have shown that this may not be true [161], [162], [163]. The much appreciated kinetic proles in use were achieved by [157] considering the talocrural and talocalcaneal joint rotations as hinge joints acting as a single monocentric one degree of freedom joint [164].