Admittance Control of a Robotic Knee Orthosis
Based on Motion Intention Through sEMG of
Trunk Muscles

Name: Ana Cecilia Villa Parra
Type: PhD thesis
Publication date: 04/12/2017

Namesort descending Role
Anselmo Frizera Neto Co-advisor *
Teodiano Freire Bastos Filho Advisor *

Examining board:

Namesort descending Role
Andre Ferreira Internal Examiner *
Anselmo Frizera Neto Co advisor *
Antônio Padilha Lanari Bó External Examiner *
Eduardo Rocon de Lima External Examiner *
Eliete Maria de Oliveira Caldeira Internal Examiner *
Teodiano Freire Bastos Filho Advisor *

Summary: The population that requires devices for motion improvement has increased considerably, due to
aging and neurological impairments. Robotic devices, such as robotic orthosis, have greatly advanced
with the objective of improving both the mobility and quality of life of people. Clinical
researches remark that these devices, working in constant interaction with the neuromuscular
and skeletal human system, improves functional compensation and rehabilitation. Hence, the
users become an active part of the training/rehabilitation, facilitating their involvement and
improving their neural plasticity. For this purpose, control approaches based on motion intention
have been presented as a novel control framework for robotic devices.
This work presents the development of a novel robotic knee exoskeleton controlled by motion
intention based on sEMG, which uses admittance modulation to assist people with reduced
mobility and improve their locomotion. For recognition of the lower-limb motion intention,
sEMG signals from trunk are used, which implies a new approach to control robotic assistive
devices. The control system developed here includes a stage for human-motion intention recognition
(HMIR) system, which is based on techniques to classify motion classes related to knee
joint. The motion classes that are taken into account are: stand-up, sit-down, knee flexionextension,
walking, rest in stand-up position and rest sit-down position. For translation of the
user’s intention to a desired state for the robotic knee exoskeleton, the system includes a finite
state machine, in addition to admittance, velocity and trajectory controllers, which has also the
function of stopping the movement according to the users intention. This work also proposes
a method for on-line knee impedance modulation, which generates variable gains through the
gait cycle for stance control during gait.
The proposed HMIR system showed, in off-line analysis, an accuracy between 76% to 83% to
recognize motion intention of lower-limb muscles, and 71% to 77% for trunk. Experimental
on-line results of the controller showed that the admittance controller proposed here offers knee
support in 50% of the gait cycle, and assists correctly the motion classes. A positive effect of
the controller on users regarding safety during gait was also found, with a score of 4 in a scale
of 5. Thus the robotic knee exoskeleton introduced here is an alternative method to empower
knee movements using motion intention based on sEMG signals from lower limb and trunk

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