Knee Impedance Modulation to Control an Active Orthosis Using Insole Sensors

Abstract

Robotic devices for rehabilitation and gait assistance have greatly advanced with the objective of improving both the mobility and quality of life of people with motion impairments. To encourage active participation of the user, the use of admittance control strategy is one of the most appropriate approaches, which requires methods for online adjustment of impedance components. Such approach is cited by the literature as a challenge to guaranteeing a suitable dynamic performance. This work proposes a method for online knee impedance modulation, which generates variable gains through the gait cycle according to the users’ anthropometric data and gait sub-phases recognized with footswitch signals. This approach was evaluated in an active knee orthosis with three variable gain patterns to obtain a suitable condition to implement a stance controller: two different gain patterns to support the knee in stance phase, and a third pattern for gait without knee support. The knee angle and torque were measured during the experimental protocol to compare both temporospatial parameters and kinematics data with other studies of gait with knee exoskeletons. The users rated scores related to their satisfaction with both the device and controller through QUEST questionnaires. Experimental results showed that the admittance controller proposed here offered knee support in 50% of the gait cycle, and the walking speed was not significantly different between the three gain patterns (p = 0.067). A positive effect of the controller on users regarding safety during gait was found with a score of 4 in a scale of 5. Therefore, the approach demonstrates good performance to adjust impedance components providing knee support in stance phase.

Assessment of an Assistive Control Approach Applied in an Active Knee Orthosis Plus Walker for Post-Stroke Gait Rehabilitation

“…The gait phases considered are as follows: initial contact (heel contact); mid-stance (flat foot contact); terminal stance (heel off); and swing (foot-off). More details about this control system can be found in our previous work [36], where also are shown validation results with volunteers. The results showed that this control system is able to execute movements according to the volunteers' movement intention, providing suitable reference patterns for gait as well as proper torques for the motion classes considered here.…”

“…Safety and usability evaluation with healthy subjects and post-stroke patients are also addressed [21,27,[37][38][39]. On the other hand, studies conducted about muscles and inertial patterns during walking with these devices have focused on the evaluation of lower-limb muscle activity [36,37], with few studies evaluating other muscles related to maintenance of dynamic postural equilibrium during gait, such as trunk muscles [40]. It is worth commenting that the muscular activity gives a representation of which action users of these devices are attempting to execute.…”

A Review on the Control of the Mechanical Properties of Ankle Foot Orthosis for Gait Assistance

“…Controlling the motion enables the actuators to be controlled; thus, the tracking of the position [30] or velocity [21] can be done. However, it would be better if the output reference is an adaptive one, especially in the stance phase [93]. Because there is only one reference, it is assumed that the position of the ankle joint is the same the entire time.…”

Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG

“…Although terminal stance phase was not detected in all trials, this is also reported in other studies, due the gait dynamic (Wentink et al, 2013;Agostini et al, 2014), the admittance modulation was able to generate a G pattern to obtain a SC performance as shown in Figure 5. It allows adapting different impedances at the knee joint during the phases stance and swing (Villa-Parra et al, 2017). For gait phase recognition in pathological cases, such as stroke survivors that present different footswitch patterns (Agostini et al, 2014), an alternative for gait phase detection is using data fusion considering the knee angle, initial movement from inertial sensors or sEMG signals to detect the gait phases (Lee et al, 2015).…”