Robotic Assessment System for Spasticity in Patients with Acquired Brain Injury

241 Pages·2015·2.22 MB·English

Robotic Assessment System for Spasticity in Patients with
Acquired Brain Injury
by
Nitin Seth
A Thesis
presented to
The University of Guelph
In partial fulfilment of requirements
for the degree of
Doctor of Philosophy
in
Engineering
Guelph, Ontario, Canada
(cid:13)c Nitin Seth, September, 2015
ABSTRACT
Nitin Seth Advisor:
University of Guelph, 2015 Dr. Hussein A. Abdullah
Spasticity is a symptom of upper motor neuron (UMN) syndrome that commonly
affects individuals suffering the effects of stroke, multiple-sclerosis, spinal cord in-
jury, or acquired brain injury. Current clinical standards and methods available for
assessing and quantifying the effects of the UMN syndrome are considered to lack
sensitivity and do not properly reflect the condition of the patient.
This work discusses the analysis and quantification of spasticity in patients with
acquired brain injury. A sensor integrated robotic system was developed in close con-
sultations with physiotherapists to assist in the spasticity assessment and monitoring
of individuals receiving care. Resistive force measurements have been obtained from
individuals undergoing flexion and extension of the elbow joint in the sagittal plane.
Repetitions were performed at progressively increasing speeds in an effort to capture
the velocity dependent resistance to passive motion that is commonly attributable to
spasticity.
The goal of this research was to collect multidimensional healthy control and
clinical data to provide insight into patients’ condition and their evaluations. An
analysis was performed on the force, position, and time data along with other metrics
developed to assist in assessment. These variables and metrics were compared against
the traditional spasticity evaluation scale, the Modified Ashworth Scale (MAS), to
demonstrate that similar effects are being captured while providing more information
regarding the individual. The healthy control data are presented as a reference for
clinicians to observe what quantified baseline data “looks like”, including quantiles
that may be used for patient tracking or monitoring.
Results demonstrate that the system is capable of detecting the effects of spas-
ticity while relating it to the MAS. This study helps uncover the nature of the MAS
scale and illustrates that individuals who scored 0 on the MAS scale are closely re-
lated to healthy individuals but still distinct. The multidimensional aspect of the
data is leveraged to differentiate different levels of the MAS. Although MAS 0’s and
healthy individuals present similar data, the multidimensional nature allows intensive
comparison techniques such as dynamic time warping, to distinguish between the two
accurately.
iv
To family and friends... all of you.
“If I have seen further [than you and Descartes] it is by standing upon
the shoulders of Giants” - Sir Isaac Newton in a 1675 letter to Robert
Hooke
v
ACKNOWLEDGEMENTS
Along with family and friends, there are several individuals whom I would
like to sincerely thank for their help and support through this project.
Thanks to my advisor, Dr. Hussein Abdullah, and my committee members
Dr. Brian Allen and Dr. Karen Gordon for the opportunity to work on
a project such as this as well as for the guidance, patience, and valuable
feedback through the years. I really appreciate all your efforts.
I would also like to thank Denise Johnson for all the help from start to
finish. Thanks also to the large team of supporters at Hamilton Health
Sciences including Bonnie Buchko, Brooke Biggs, Erica Moyer, Neenah
Navasero, Jess Temesy, Sue Barreca, and Chantel Lount. You were all
a great help, especially in the early stages. I’d also specifically like to
thank John Zsofcsin, Barb Ansley, and Dr. Shanker Nesathurai for the
opportunity to perform our study and supporting our research.
To everyone in the Robotics Lab, Lab Manager Cole Tarry, Mike Sta-
chowsky, Greg Jackson, Tom Hummel, Patrick Wspanialy, and Matt Veres;
thanks for always being a great team and ensuring that my time here would
be memorable. I would also like to acknowledge the important efforts and
contributions of Michael Mohan, Craig Duvall, Melissa Jones, Thomas
Shoniker, and Oana Burlacu for the enormous of amount of work to help
make this project happen and for being great people to work with. You
were all fantastic.
Special thanks also to Lucy Cremasco, Sue Shaw, Ken Graham, Dr. Gra-
ham Taylor, Dr. Gordon Hayward, Dr. Medhat Moussa, and Dr. Doug
Joy as well as the rest of the faculty and staff at the University of Guelph’s
School of Engineering for their help and for creating a great atmosphere
for us students.
Lastly, I would like to thank everyone who took the time to volunteer for
our study to help ensure it was that much better.
I wish everyone the best.
vi
Table of Contents
Acknowledgements v
List of Tables x
List of Figures xii
1 Background and Overview 1
1.1 Spasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Spasticity Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.1 Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Robotic Spasticity Assessment . . . . . . . . . . . . . . . . . . . . . . 4
1.3.1 Knowledge Gain . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3.2 System Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Eligibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Research Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4.1 Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.5 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 Rehabilitation Research 13
2.1 Aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3.2 Use of Robotics in Treatment . . . . . . . . . . . . . . . . . . 18
2.3.3 Use of Robotics in Assessment . . . . . . . . . . . . . . . . . . 28
Role of Robotics . . . . . . . . . . . . . . . . . . . . . . . . . 29
Relevant Studies . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.4 Tone and Spasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.4.1 Spasticity Management . . . . . . . . . . . . . . . . . . . . . . 42
vii
2.4.2 Tone Assessment Devices . . . . . . . . . . . . . . . . . . . . . 43
2.4.3 Robotic Spasticity Assessment System . . . . . . . . . . . . . 52
3 Methods 55
3.1 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.2 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.2.1 Set-up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.2.2 Physical Interface . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.2.3 Software interface . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.2.4 Robot Specification . . . . . . . . . . . . . . . . . . . . . . . . 64
3.2.5 Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.3 Study Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.3.1 Healthy Control Data Collection . . . . . . . . . . . . . . . . . 69
3.3.2 Clinical Trial . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
Clinical Data Collection . . . . . . . . . . . . . . . . . . . . . 70
Inclusion Criteria . . . . . . . . . . . . . . . . . . . . . . . . . 71
Exclusion Criteria . . . . . . . . . . . . . . . . . . . . . . . . . 71
Data Collection and Outcome measures . . . . . . . . . . . . . 71
Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Safety Measures . . . . . . . . . . . . . . . . . . . . . . . . . . 75
3.3.3 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . 75
3.4 Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
3.4.1 Data Representation . . . . . . . . . . . . . . . . . . . . . . . 77
3.4.2 T-test for Comparing Healthy vs Patient Data . . . . . . . . . 78
3.4.3 Fisher’s Linear Discriminate Analysis . . . . . . . . . . . . . . 78
3.4.4 Statistical Model . . . . . . . . . . . . . . . . . . . . . . . . . 81
3.4.5 “Mechanical” Inspired Reparameterization . . . . . . . . . . . 82
Solving for Constants . . . . . . . . . . . . . . . . . . . . . . . 83
3.4.6 Relation back to MAS Scales . . . . . . . . . . . . . . . . . . 84
3.5 Dynamic Time Warping . . . . . . . . . . . . . . . . . . . . . . . . . 85
3.5.1 K-Nearest Neighbour Algorithm . . . . . . . . . . . . . . . . . 89
3.6 Research Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
3.6.1 Healthy vs Patient Data . . . . . . . . . . . . . . . . . . . . . 90
3.6.2 Velocity Dependent Resistance . . . . . . . . . . . . . . . . . . 91
3.6.3 Relation to MAS . . . . . . . . . . . . . . . . . . . . . . . . . 91
3.6.4 Directional Information for Classification . . . . . . . . . . . . 92
4 Baseline Profiles 93
4.1 Background and Previous Work . . . . . . . . . . . . . . . . . . . . . 93
4.1.1 Contribution of Healthy Data . . . . . . . . . . . . . . . . . . 94
4.1.2 Research Question . . . . . . . . . . . . . . . . . . . . . . . . 95
Healthy Individuals Model . . . . . . . . . . . . . . . . . . . . 97
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4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4.2.1 Slow Speed Setting . . . . . . . . . . . . . . . . . . . . . . . . 99
4.2.2 Fast Speed Setting . . . . . . . . . . . . . . . . . . . . . . . . 104
4.2.3 Confidence Intervals on Average Force . . . . . . . . . . . . . 109
4.2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
4.3 Significance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
5 Quantitative Clinical Data for Tone and Spasticity: Elbow Flex-
ors/Extensors 116
5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.2 Contribution to the Field . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.3 Associated Research Questions . . . . . . . . . . . . . . . . . . . . . . 118
5.3.1 Descriptive Data . . . . . . . . . . . . . . . . . . . . . . . . . 118
5.3.2 Differentiation between Healthy Individuals . . . . . . . . . . 119
5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.4.1 Force Traces . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
5.4.2 Distributions and Cumulative Percentiles . . . . . . . . . . . . 125
5.4.3 Confidence Intervals on Average Force . . . . . . . . . . . . . 131
5.4.4 Mean Force of Healthy Individuals verses MAS 0 Patients. . . 132
5.4.5 Fisher’s Linear Discriminate Analysis (LDA) . . . . . . . . . . 133
5.4.6 Dynamic Time Warping . . . . . . . . . . . . . . . . . . . . . 134
5.4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
5.5 Significance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
6 Velocity-Dependent Resistance in Clinical Data 144
6.1 Background and Previous Work . . . . . . . . . . . . . . . . . . . . . 144
6.2 Contribution to Field . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
6.3 Associated Research Questions . . . . . . . . . . . . . . . . . . . . . . 146
6.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
6.4.1 Force Model Results . . . . . . . . . . . . . . . . . . . . . . . 150
6.4.2 b-Model Results . . . . . . . . . . . . . . . . . . . . . . . . . . 155
6.4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
6.5 Significance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
7 Relationship of Biomechanical Force Data to MAS 167
7.1 Background and Previous Work . . . . . . . . . . . . . . . . . . . . . 167
7.1.1 Contribution to Field . . . . . . . . . . . . . . . . . . . . . . . 168
7.1.2 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . 169
7.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
7.2.1 Force Testing Model . . . . . . . . . . . . . . . . . . . . . . . 171
7.2.2 Slope Testing Model . . . . . . . . . . . . . . . . . . . . . . . 176
7.2.3 K-Means Clustering of Metrics . . . . . . . . . . . . . . . . . . 179
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7.2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
7.3 Significance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
8 Conclusions and Recommendations 193
8.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
8.1.1 The Importance of Healthy Baselines . . . . . . . . . . . . . . 194
Associated Contributions/ publications . . . . . . . . . . . . . 194
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
8.1.2 Velocity Dependent Resistance . . . . . . . . . . . . . . . . . . 197
Associated Contributions/ Publications . . . . . . . . . . . . . 197
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
8.1.3 Robotic Force Data and the Modified Ashworth Scale . . . . . 199
Associated Contributions/ Publications . . . . . . . . . . . . . 199
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
8.2 Recommendations for Future Work . . . . . . . . . . . . . . . . . . . 202
8.2.1 Arm Trajectory . . . . . . . . . . . . . . . . . . . . . . . . . . 203
8.2.2 Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . 203
8.2.3 Arm Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . 205
8.2.4 Visualizations . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
8.2.5 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
40 Newtons Limit/ MAS 4 . . . . . . . . . . . . . . . . . . . . 207
Reproducibility Study . . . . . . . . . . . . . . . . . . . . . . 207
Joint Angles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
Physiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
References 210
A Derivation of Least Squares 225
B Dynamic Time Warping Algorithm 226
C K-means Algorithm 228
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List of Tables
2.1 Motions and Measurements . . . . . . . . . . . . . . . . . . . . . . . 35
2.2 Metrics and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.3 MAS Force Reading Results . . . . . . . . . . . . . . . . . . . . . . . 46
3.1 F5 Robot Specifications . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.2 Discriminators for LDA . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.1 Guelph: Fixed Effects . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.2 Guelph: Least Square Means . . . . . . . . . . . . . . . . . . . . . . . 104
4.3 HHSC: Fixed Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
4.4 HHSC: Least Square Means . . . . . . . . . . . . . . . . . . . . . . . 109
4.5 Guelph: Confidence Intervals on Force . . . . . . . . . . . . . . . . . 110
4.6 HHSC: Confidence Intervals on Force . . . . . . . . . . . . . . . . . . 110
4.7 Healthy Percentiles Flexion . . . . . . . . . . . . . . . . . . . . . . . 112
5.1 Clinical:Confidence Intervals on Force . . . . . . . . . . . . . . . . . . 131
5.2 Stats for t-test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
5.3 LDA Weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
5.4 Fx: Clinical Percentiles . . . . . . . . . . . . . . . . . . . . . . . . . . 137
5.5 Fy: Clinical Percentiles . . . . . . . . . . . . . . . . . . . . . . . . . . 138
5.6 Fz: Clinical Percentiles . . . . . . . . . . . . . . . . . . . . . . . . . . 139
5.7 Ftot: Clinical Percentiles . . . . . . . . . . . . . . . . . . . . . . . . . 140
6.1 MAS Bicep Model Fixed Effects . . . . . . . . . . . . . . . . . . . . . 151
6.2 MAS Tricep Model Fixed Effects . . . . . . . . . . . . . . . . . . . . 151
6.3 MAS Bicep Model Interactions LS Means . . . . . . . . . . . . . . . . 152
6.4 MAS Tricep Model Interactions LS Means . . . . . . . . . . . . . . . 152
6.5 MAS Bicep Slopes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
6.6 MAS Tricep Slopes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
6.7 b-Model with Flexion/Extension . . . . . . . . . . . . . . . . . . . . . 156
6.8 b-Model 6.2 Reduced . . . . . . . . . . . . . . . . . . . . . . . . . . . 157