Monitoring of upper limbs motor skills is important throughout the rehabilitation
process. The doctoral thesis, consisting of three studies, deals with the
analysis of upper limb activity in the clinical setting, during activities of daily living,
and the evaluation of upper limb interaction during robot training.
Upper limb movement was measured with a wearable sensory system consisting
of wireless inertial-magneto measurement units (IMU) and electromyography sensors.
Sensors are small and do not interfere with upper limb movement. The methodology
for computation of upper limb kinematics based on IMU data is presented.
First study focuses on measuring and quantifying upper limb and trunk movement
while executing ARAT and WMFT motor tasks. Equipped with wearable sensory
system, patients after stroke and healthy volunteers executed tasks of clinical tests
according to the standard protocol. The movement was quantified with five parameters
that are associated with clinical assessment: movement time, movement smoothness,
similarity of hand trajectories, trunk stability, and fingers and wrist muscle activity.
Tasks were segmented into object manipulation phases and movement phases, for which
the five parameters were computed. Data were allocated into four groups. Patients who
suffered stroke were grouped based on their clinical scores obtained for each task. Based
on the proposed parameters, it is possible to differentiate between groups of patients.
Numerical quantification of movement was additionally compared to the total ARAT
scores obtained by each patient, and shows strong correlation for movement time and
movement smoothness.
Throughout the rehabilitation process patients after stroke need to re-learn movements
for performing activities of daily living. In the second study of the doctoral
thesis we used wearable sensory system for monitoring upper limbs movement while
performing activities of daily living. In the first step, time quantization of movement
is used for computation of activity counts, counts of muscle activity and power counts
for each upper limb. Time quantization allows comparison of upper limb activities
within short time intervals. In the second step, upper limb motion was segmented into
individual movements based on changes in velocity and direction of movement of the
upper limbs. On the basis of segmentation, we analysed path length of the hand movement,
the achieved hand height, joint angles and muscle activity. The parameters can
be used to distinguish between the activity of one and the other limb. We introduced
a parameter for estimating movement coordination, based on which it is possible to
distinguish between unimanual and bimanual activities.
In the third study, the analysis of the interaction of the upper limbs during a bimanual
tasks with a robot was performed. Robot system, tasks in virtual environment,
and methodology for computation of movement parameters are presented. The system
was used with a group of healthy volunteers and a group of patients after stroke. The
analysis was based on measurements of the interaction forces between the upper limbs
and the robot. We performed a comparison between groups of subjects at a given robot
resistance and a comparison of the impact of robot resistance within each group of
subjects. The proposed method enables quantification of activities of each upper limb
and differentiates between the groups with different degrees of impairment. Analysis
of robot resistance shows to a large extent statistical significant differences for most
of the computed parameters within the group of healthy subjects and to lesser extent
within the groups of patients after stroke.
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