Towards automated disability assessment of upper limb movement after stroke /
Movement quality assessment has been integral to appraise physical therapy in upper limb rehabilitation after stroke. The outlook is attractive because it provides a non-invasive insight to movement quality for the purpose of benchmarking the level of impairment before the rehabilitation begins and...
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| Format: | Software eBook |
| Language: | English |
| Published: |
2019.
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| Summary: | Movement quality assessment has been integral to appraise physical therapy in upper limb
rehabilitation after stroke. The outlook is attractive because it provides a non-invasive insight
to movement quality for the purpose of benchmarking the level of impairment before the
rehabilitation begins and measuring the extent of recovery afterwards. However, a clinically
acceptable measurement technique has been limited to laboratory setup with complex subject
preparation. The attachment of numerous retro-reflective markers is time consuming and
requires careful palpation on bare skin to record movements. The recorded marker positions
need to be manually labeled in post-processing to interpret the movements according to
accepted clinical model. This laboratory setup would require a highly skilled bio-mechanics’
scientist and therapist to locate the anatomical landmarks and to analyze the kinematics
data. Furthermore, the cost and complexity of the setup limits the amount of assessment
replication.
Recently, off-the-shelf marker-less motion capture becomes available and provides plausible
joint estimation in form of skeletal data in real-time. This feature is particularly attractive
especially for upper limb assessment due to multiple joints required to be monitored over time.
Moreover, it trades off cost, complexity and accuracy of the assessment. Multiple researches
have reported acceptable accuracy in joint angles estimation in various task similar to the
existing stroke assessment task. These reports had inspired this research towards automating
the kinematic disability assessment for stroke patients.
Firstly, an extensive literature review was conducted in the beginning of this research to
discover the gaps in current kinematic assessment. It revealed the importance of determining
the extent of compensation in stroke patients which was overlooked when assessing end-point
movement. Particularly, reaching and drawing assessment tasks which were ubiquitous in
evaluating robotic rehabilitation outcomes lack the compensatory movement measure. Kinematic
result of patient’s hand movement can be misleading if the extent of joint coordination
and torso compensation are not taken into account. While previous studies in marker based
setting have provided a number of parameters to determine the extent of compensation
during assessment, such similar work in marker-less setting was non-existent. Therefore,
this research investigated the use of marker-less motion capture to determine the extent of compensation in existing clinically-accepted assessment tasks to provide further insight to
the outcomes of the assessment.
The excessive movement of the torso when performing assessment tasks that require
arm-forearm coordination is typical in stroke patients. Therefore, a three dimensional
measurement model which explains the adaptation of this compensatory strategy is essential
to determine the extent of motor recovery. A Torso Principal Component Analysis (PCA)
Frame model was developed utilizing Kinect’s joint prediction as a proposal to assess torso
orientation over time. By re-orientating and aligning the axes to the clinically accepted
torso orientation model, all the independent torso angles can be decomposed and reported as
parameters to represent compensatory behavior.
The Torso PCA Frame model was first evaluated by parametrizing its distribution in
the assessment session as attributes to predict normal and compensatory behavior in artificial
stroke movement setting. Healthy participants were fitted with elbow brace to limit
arm-forearm coordination which may artificially induce compensatory torso movements to
complete the task.They performed gross movements typical in stroke assessment and their
torso distribution over the session were recorded. Results show that the accuracy of the Torso
PCA Frame model was at 98.7% and were suitable as parameters to delineate compensation
in that setting.
To perform comparison with clinically accepted data, the Torso PCA Frame model was
then evaluated by comparing the torso angle with marker-based clinical model and Kinect’s
intrinsic chest orientation to assess the torso movement. Healthy participants were recruited
to perform circle tracing (CT) and point-to-point (PTP) planar tasks in simultaneous setting
of marker-based and marker-less system. Results showed that the torso angles computed
using Torso PCA Frame model were insignificantly different to clinical measures in PTP task
(0.103±0.881° in forward bending, 1.631±1.456° in lateral flexion and −3.488±2.765°
in axial rotation) but forward bending was significantly different in CT task (3.700±0.473°).
Extended evaluation also shows that the mean of axial rotation angles were significantly
similar across both tasks (F2,18 =1.800, p=.194 in PTP task and F2,18 =1.876, p=.182 ) in
marker-based and marker-less setting.
Torso PCA frame model was evaluated afterwards against healthy participants which
were fitted with elbow brace and strapped across the chest to emulate the limited coordination
of stroke patients. Five participants were randomly chosen to emulate this behavior and the
results showed that the forward bending angles were significantly different between normal
and artificial stroke participants in PTP task (−7.532±4.171° ,p=.001) but not in CT task
(−.261±4.172° , p=.899). To investigate the usability of Torso PCA Frame model to detect torso compensation, the
Torso PCA model was evaluated in stroke participants to assess their movements performing
circle and point-to-point tracing. Results showed that forward bending and lateral flexion
were significantly different between normal and stroke patients in both tasks (−12.130±
4.211° , p<.0005 and11.008±9.468° , p=.024 respectively), while only forward bending was
significantly different in CT task (−4.770±4.221° , p=.028). These results were different
from the outcome in artificial stroke movement setting. Nevertheless, the artificial stroke
movement setting was accurate to identify excessive forward bending in PTP task and was
eminent to show that the model was able to enunciate typical compensatory behavior in
stroke patients.
To enunciate genuine motor recovery on the current end-point movement quality assessment,
measure of compensatory movement is essential. By aggregating existing kinematic
parameters derived from the result of the review with the proposed torso compensatory assessment,
a new kinematic assessment scheme which represent the four underlying problems of
stroke patient’s movement was proposed. Along with the independent torso angles computed,
the kinematic parameters outline not only the extent of motor recovery but also the extent of
compensatory movements.
To evaluate the effectiveness of the new kinematic assessment scheme, the pool of
kinematic parameters derived from the result of the review along with the torso angles
obtained from the Torso PCA Frame model were used to form kinematic attributes in
supervised learning algorithm. Results showed that the accuracy of the aggregated model
was at 93.33% and were suitable as parameters to delineate normal participant from stroke
patients.
In summary, the development of Torso PCA frame model can be valuable for automated
setting as the setup was simple and the results obtained were comparable to clinically
computed angles within the planar assessment setting ubiquitously implemented in automated
rehabilitation. This research has collaborated with Laura Ferguson Rehabilitation Center,
Auckland for the assessment of stroke patients. The outcome of this research includes an
extensive review of kinematic assessment in upper limb movement after stroke which was
published in peer-reviewed SCI journal. The proposed torso model which is used as the basis
to measure compensatory movement was also presented at an international conference. |
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| Item Description: | Accompanied by CD : CDR 18804. |
| Physical Description: | various paging : color illustrations, charts, photographs ; 30 cm + 1 computer disc (12 cm) |
| Bibliography: | Includes references. |