Automatic Detection of Cognitive Impairment with Virtual Reality
<p>A snapshot from VStore from the perspective of a participant navigating. This frame presents a critical decision point in the VR supermarket where a participant has visibility of six items in the n-item (<math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>12</mn> </mrow> </semantics></math>) shopping list. Employing the classical traveling salesman problem (TSP) requires visibility of all locations to travel to ahead of starting the journey. At this location in VStore, the participant enters a six-point TSP that they must decide how to approach by taking into consideration that half of the items on the shopping list are visible.</p> "> Figure 2
<p>Schematic outlining rotational field of view and items included that hesitation score and spatial features employ. Blue path represents optimal solution to TSP. Please note that this figure is for illustrative purposes and does not reflect the actual proportions in the VStore environment.</p> "> Figure 3
<p>(<b>a</b>) Non-patient path taken where darker blue presents more time spent at a location and connections between nodes represent the path followed. Red is the optimal route for <span class="html-italic">n</span>-point (<math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>) TSP. (<b>b</b>) Psychosis patient path taken where darker blue presents more time spent at a location and connections between nodes represent the path followed. Red is the optimal route for <span class="html-italic">n</span>-point (<math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>) TSP.</p> "> Figure 4
<p>Ordinary least-squares linear regression with hesitation score at 95% confidence interval bounds against (<b>a</b>) age; (<b>b</b>) IQ; and (<b>c</b>) cogstate composite score. The red line is the regression line of fitted predictors and the crosses are the true values compared to this.</p> "> Figure 5
<p>Ordinary least-squares linear regression with hesitation score score at <math display="inline"><semantics> <mrow> <mn>95</mn> <mo>%</mo> </mrow> </semantics></math> confidence interval bounds against (<b>a</b>) DET; (<b>b</b>) OCL; (<b>c</b>) ONB; and (<b>d</b>) IDN. The red line is the regression line of fitted predictors and the crosses are the true values compared to this.</p> ">
Abstract
:1. Introduction
2. Related Work
2.1. Pen and Paper Methods
2.2. VR Cognitive Assessments
2.3. Cognitive State Features
3. VR Study
3.1. VStore
3.2. Clinical Study
3.3. Procedure
4. Proposed Methodology
4.1. Feature Extraction
- VStore accounts for slow walkers as it is a VR simulation, one command to a controller is equivalent to one human stride. Hence pace of movement through this environment is better constrained to decision making as opposed to physical ability. This is one of the key advantages of VR cognitive assessments, they also enable large-scale spatial navigation to be assessed.
- The virtual supermarket shop floor consists of four shopping aisles with further shelving spanning the perimeter of the store. In some positions of the VR environment, participants can see what is on many other shelves of the aisles without having to walk down an aisle to find them. This is particularly significant at the fridges where participants can see six items on the list at once. This stage of the VR environment can be modelled on Traveling Salesman Problem (TSP). Any VR based cognitive assessment that requires decision making between simultaneously visible landmarks can also be modelled on TSP. The participant paths taken may provide insight on executive function, specifically, goal-directed motor function.
- Aisles are also labelled with the category of items they contain whether a participant can see the item or not, introducing bias to the aisles a participant explores. This is not simply an explore-exploit feedback system but one that invites strategic and optimal route planning, and cognition permitting. This observation can be translated to sophisticated VR cognitive assessments that compound behaviours in decisions made.
4.1.1. Spatial Features
Route Optimality Score
Proportional Distance Score
4.1.2. Execution Error Score
4.1.3. Hesitation Score
5. Cognitive Score Prediction
6. Experimental Results
6.1. Cognitive Scores
6.2. Participant Path
6.3. Hesitation Score Residual Plot
6.4. Data Tables
7. Discussion
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Feature | F-stat | p (F-stat) | |
---|---|---|---|
Age | 0.380 | 14.270 | |
IQ | 0.057 | 1.399 | 0.240 |
Cog Comp | 0.224 | 6.713 | |
DET | 0.071 | 1.775 | 0.140 |
OCL | 0.044 | 1.075 | 0.374 |
ONB | 0.070 | 1.752 | 0.145 |
IDN | 0.050 | 1.256 | 0.293 |
Feature | F-stat | p (F-stat) | |
---|---|---|---|
Age | 0.173 | 2.561 | 0.05 |
IQ | 0.252 | 4.1118 | 0.00591 |
Cog Comp | 0.328 | 5.986 | 0.000528 |
DET | 0.369 | 7.158 | 0.000128 |
OCL | 0.364 | 6.997 | 0.000154 |
ONB | 0.359 | 6.872 | 0.000179 |
IDN | 0.364 | 7.009 | 0.000152 |
Cognitive Score | Feature | Coef | Std Error | t | |||
---|---|---|---|---|---|---|---|
Age | |||||||
Cst. | 0.0055 | 0.082 | 0.067 | 0.947 | −0.210 | 0.221 | |
PD | −0.2079 | 0.097 | −2.154 | 0.034 | −0.462 | 0.046 | |
RO | −0.0758 | 0.086 | −0.882 | 0.38 | −0.302 | 0.150 | |
ExErr | 0.2283 | 0.093 | 2.456 | 0.016 | −0.016 | 0.473 | |
Hes | 0.6878 | 0.095 | 7.260 | 0.000 | 0.439 | 0.937 | |
IQ | |||||||
Cst. | 0.0178 | 0.100 | 0.179 | 0.858 | −0.244 | 0.280 | |
PD | 0.0182 | 0.117 | 0.155 | 0.877 | −0.290 | 0.326 | |
RO | 0.0326 | 0.104 | 0.312 | 0.756 | −0.242 | 0.307 | |
ExErr | −0.0489 | 0.113 | −0.433 | 0.666 | −0.346 | 0.248 | |
Hes | 0.2175 | 0.115 | 1.891 | 0.062 | −0.085 | 0.520 | |
Cog Comp | |||||||
Cst. | −0.0087 | 0.092 | −0.095 | 0.925 | −0.250 | 0.233 | |
PD | 0.1214 | 0.108 | 1.125 | 0.264 | −0.163 | 0.405 | |
RO | 0.1457 | 0.096 | 1.515 | 0.133 | −0.107 | 0.339 | |
ExErr | −0.1960 | 0.104 | −1.884 | 0.063 | −0.470 | 0.078 | |
Hes | −0.4994 | 0.106 | −4.710 | 0.000 | −0.778 | −0.221 | |
DET | |||||||
Cst. | −0.0051 | 0.100 | −0.051 | 0.959 | −0.269 | 0.259 | |
PD | 0.1101 | 0.118 | 0.933 | 0.353 | −0.200 | −0.421 | |
RO | 0.0717 | 0.105 | 0.682 | 0.497 | −0.205 | 0.348 | |
ExErr | 0.0028 | 0.114 | 0.024 | 0.981 | −0.296 | 0.302 | |
Hes | −0.2828 | 0.116 | −2.439 | 0.017 | −0.588 | −0.022 | |
OCL | |||||||
Cst. | −0.0053 | 0.102 | −0.052 | 0.958 | −0.273 | 0.262 | |
PD | −0.0158 | 0.120 | −0.132 | 0.895 | −0.331 | 0.299 | |
RO | 0.0815 | 0.107 | 0.765 | 0.446 | −0.199 | 0.362 | |
ExErr | 0.0616 | 0.115 | 0.534 | 0.595 | −0.242 | 0.365 | |
Hes | −0.1599 | 0.118 | −1.361 | 0.177 | −0.469 | 0.149 | |
ONB | |||||||
Cst. | 0.0002 | 0.101 | 0.002 | 0.998 | −0.264 | 0.265 | |
PD | −0.0694 | 0.118 | −0.587 | 0.558 | −0.380 | 0.241 | |
RO | 0.0877 | 0.105 | 0.833 | 0.407 | −0.189 | 0.364 | |
ExErr | −0.0229 | 0.114 | −0.201 | 0.841 | −0.322 | 0.277 | |
Hes | −0.2101 | 0.116 | −1.811 | 0.073 | −0.515 | 0.095 | |
IDN | |||||||
Cst. | 0.0043 | 0.102 | 0.042 | 0.966 | −0.263 | 0.271 | |
PD | −0.2123 | 0.119 | −1.778 | 0.079 | −0.526 | 0.102 | |
RO | −0.0981 | 0.106 | −0.922 | 0.359 | −0.378 | 0.182 | |
ExErr | 0.1759 | 0.115 | 1.529 | 0.130 | −0.127 | 0.478 | |
Hes | 0.0441 | 0.117 | 0.376 | 0.708 | −0.264 | 0.352 |
Cognitive Score | Feature | Coef | Std Error | t | |||
---|---|---|---|---|---|---|---|
Age | |||||||
Cst. | −0.0302 | 0.128 | −0.237 | 0.814 | −0.372 | 0.312 | |
PD | 0.0975 | 0.134 | 0.727 | 0.471 | −0.262 | 0.457 | |
RO | −0.1477 | 0.133 | −1.111 | 0.272 | −0.504 | 0.209 | |
ExErr | −0.0108 | 0.130 | −0.083 | 0.934 | −0.358 | 0.337 | |
Hes | 0.3869 | 0.129 | 3.000 | 0.004 | 0.041 | 0.733 | |
IQ | |||||||
Cst. | −0.0249 | 0.123 | −0.203 | 0.840 | −0.354 | 0.304 | |
PD | 0.0553 | 0.129 | 0.428 | 0.670 | −0.291 | 0.304 | |
RO | −0.1328 | 0.128 | −1.038 | 0.304 | −0.476 | 0.210 | |
ExErr | −0.1719 | 0.125 | −1.377 | 0.175 | −0.506 | 0.163 | |
Hes | −0.4051 | 0.124 | −3.263 | 0.002 | −0.738 | −0.072 | |
Cog Comp | |||||||
Cst. | −0.0011 | 0.118 | −0.009 | 0.993 | −0.318 | 0.316 | |
PD | −0.1767 | 0.124 | −1.423 | 0.161 | −0.509 | 0.156 | |
RO | 0.1673 | 0.123 | −1.359 | 0.180 | −0.163 | 0.497 | |
ExErr | −0.1785 | 0.120 | −1.487 | 0.144 | −0.500 | 0.143 | |
Hes | −0.4937 | 0.119 | −4.135 | 0.000 | −0.814 | −0.174 | |
DET | |||||||
Cst. | −0.0130 | 0.114 | −0.114 | 0.910 | −0.318 | 0.292 | |
PD | 0.1073 | 0.120 | 0.896 | 0.375 | −0.214 | −0.428 | |
RO | −0.1295 | 0.119 | −1.092 | 0.280 | −0.448 | 0.188 | |
ExErr | −0.1019 | 0.116 | −0.880 | 0.383 | −0.412 | 0.208 | |
Hes | −0.5543 | 0.115 | −4.815 | 0.000 | −0.863 | −0.246 | |
OCL | |||||||
Cst. | −0.0159 | 0.114 | −0.139 | 0.890 | −0.322 | 0.290 | |
PD | 0.1104 | 0.120 | 0.921 | 0.362 | −0.211 | 0.432 | |
RO | −0.1513 | 0.119 | −1.273 | 0.209 | −0.470 | 0.167 | |
ExErr | −0.1219 | 0.116 | −1.051 | 0.298 | −0.433 | 0.189 | |
Hes | −0.5335 | 0.115 | −4.626 | 0.000 | −0.843 | −0.224 | |
ONB | |||||||
Cst. | −0.0142 | 0.115 | −0.124 | 0.902 | −0.321 | 0.293 | |
PD | 0.1389 | 0.120 | 1.153 | 0.254 | −0.184 | 0.426 | |
RO | −0.1284 | 0.119 | −1.075 | 0.287 | −0.448 | 0.192 | |
ExErr | −0.1131 | 0.116 | −0.971 | 0.336 | −0.424 | 0.199 | |
Hes | −0.5399 | 0.116 | −4.662 | 0.000 | −0.850 | −0.230 | |
IDN | |||||||
Cst. | −0.0137 | 0.114 | −0.120 | 0.905 | −0.320 | 0.292 | |
PD | 0.1235 | 0.120 | 1.028 | 0.309 | −0.198 | 0.445 | |
RO | −0.1351 | 0.119 | −1.135 | 0.262 | −0.454 | 0.184 | |
ExErr | −0.1088 | 0.116 | −0.937 | 0.353 | −0.420 | 0.202 | |
Hes | −0.5447 | 0.115 | −4.718 | 0.000 | −0.854 | −0.235 |
Feature | F-stat | p (F-stat) | |
---|---|---|---|
Age | 0.128 | 9.035 | 0.00407 |
IQ | 0.201 | 13.09 | 0.000673 |
Cog Comp | 0.262 | 18.47 | |
DET | 0.335 | 26.24 | |
OCL | 0.319 | 24.39 | |
ONB | 0.318 | 24.22 | |
IDN | 0.325 | 25.04 |
Cognitive Score | Feature | Coef | Std Error | t | |||
---|---|---|---|---|---|---|---|
Age | |||||||
Cst. | −0.0327 | 0.126 | −0.260 | 0.796 | −0.369 | 0.303 | |
Hes | 0.3743 | 0.125 | 3.006 | 0.004 | 0.041 | 0.707 | |
IQ | |||||||
Cst. | −0.0229 | 0.123 | −0.186 | 0.853 | −0.352 | 0.306 | |
Hes | −0.4414 | 0.122 | −3.618 | 0.001 | −0.768 | −0.115 | |
Cog Comp | |||||||
Cst. | 0.0074 | 0.120 | −0.061 | 0.951 | −0.314 | 0.329 | |
Hes | −0.5116 | 0.119 | −4.298 | 0.000 | −0.830 | −0.193 | |
DET | |||||||
Cst. | −0.0137 | 0.113 | −0.121 | 0.904 | −0.317 | 0.289 | |
Hes | −0.5756 | 0.112 | −5.122 | 0.000 | −0.876 | −0.275 | |
OCL | |||||||
Cst. | −0.0163 | 0.114 | −0.142 | 0.887 | −0.322 | 0.290 | |
Hes | −0.5603 | 0.113 | −4.938 | 0.000 | −0.864 | −0.257 | |
ONB | |||||||
Const. | −0.0155 | 0.115 | −0.135 | 0.893 | −0.322 | 0.291 | |
Hes | −0.5594 | 0.114 | −4.921 | 0.000 | −0.863 | −0.255 | |
IDN | |||||||
Cst. | −0.0147 | 0.114 | −0.129 | 0.898 | −0.320 | 0.291 | |
Hes | −0.5661 | 0.113 | −5.004 | 0.000 | −0.869 | −0.264 |
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Mannan, F.A.; Porffy, L.A.; Joyce, D.W.; Shergill, S.S.; Celiktutan, O. Automatic Detection of Cognitive Impairment with Virtual Reality. Sensors 2023, 23, 1026. https://doi.org/10.3390/s23021026
Mannan FA, Porffy LA, Joyce DW, Shergill SS, Celiktutan O. Automatic Detection of Cognitive Impairment with Virtual Reality. Sensors. 2023; 23(2):1026. https://doi.org/10.3390/s23021026
Chicago/Turabian StyleMannan, Farzana A., Lilla A. Porffy, Dan W. Joyce, Sukhwinder S. Shergill, and Oya Celiktutan. 2023. "Automatic Detection of Cognitive Impairment with Virtual Reality" Sensors 23, no. 2: 1026. https://doi.org/10.3390/s23021026