Connected graph used for routing simulated patients with topographical disorientation

<p><b>Copyright information:</b></p><p>Taken from "A flexible routing scheme for patients with topographical disorientation"</p><p>http://www.jneuroengrehab.com/content/4/1/44</p><p>Journal of NeuroEngineering and Rehabilitation 2007;4():44-44.</p><p>Published online 28 Nov 2007</p><p>PMCID:PMC2219962.</p><p></p

Validating an infrared thermal switch as a novel access technology

Background Recently, a novel single-switch access technology based on infrared thermography was proposed. The technology exploits the temperature differences between the inside and surrounding areas of the mouth as a switch trigger, thereby allowing voluntary switch activation upon mouth opening. However, for this technology to be clinically viable, it must be validated against a gold standard switch, such as a chin switch, that taps into the same voluntary motion. Methods In this study, we report an experiment designed to gauge the concurrent validity of the infrared thermal switch. Ten able-bodied adults participated in a series of 3 test sessions where they simultaneously used both an infrared thermal and conventional chin switch to perform multiple trials of a number identification task with visual, auditory and audiovisual stimuli. Participants also provided qualitative feedback about switch use. User performance with the two switches was quantified using an efficiency measure based on mutual information. Results User performance (p = 0.16) and response time (p = 0.25) with the infrared thermal switch were comparable to those of the gold standard. Users reported preference for the infrared thermal switch given its non-contact nature and robustness to changes in user posture. Conclusions Thermal infrared access technology appears to be a valid single switch alternative for individuals with disabilities who retain voluntary mouth opening and closing

Infrared thermography as an access pathway for individuals with severe motor impairments

Background: People with severe motor impairments often require an alternative access pathway, such as a binary switch, to communicate and to interact with their environment. A wide range of access pathways have been developed from simple mechanical switches to sophisticated physiological ones. In this manuscript we report the inaugural investigation of infrared thermography as a non-invasive and non-contact access pathway by which individuals with disabilities can interact and perhaps eventually communicate. Methods: Our method exploits the local temperature changes associated with mouth opening/closing to enable a highly sensitive and specific binary switch. Ten participants (two with severe disabilities) provided examples of mouth opening and closing. Thermographic videos of each participant were recorded with an infrared thermal camera and processed using a computerized algorithm. The algorithm detected a mouth open-close pattern using a combination of adaptive thermal intensity filtering, motion tracking and morphological analysis. Results: High detection sensitivity and low error rate were achieved for the majority of the participants (mean sensitivity of all participants: 88.5% ± 11.3; mean specificity of all participants:99.4% ± 0.7). The algorithm performance was robust against participant motion and changes in the background scene.Conclusion: Our findings suggest that further research on the infrared thermographic access pathway is warranted. Flexible camera location, convenience of use and robustness to ambient lighting levels, changes in background scene and extraneous body movements make this a potential new access modality that can be used night or day in unconstrained environments.</p

A comparison of accuracies for the proposed method (solid line), SPM (dashed line), PPF (dash-dotted line) and EMD (dotted line).

<p>(a) and (b) represent NMSE in the A-P and S-I directions, respectively, while generating new versions of , and with each new realization of eqn. (23). (c) and (d) represent NMSE in the A-P and S-I directions, respectively, while keeping constant and obtaining a new version of for each new realization of eqn. (23).</p

A single run of 6-fold intrasession cross-validation and intersession “pseudo-cross-validation”.

<p>In this example, Session A is the training session, and the remaining four sessions are for testing only. Training and test session data are all divided randomly into six sets. For each of the six folds, a classifier is trained on five sets of the training session data (a different set is left out at each fold). For the intrasession cross-validation, the resulting classifier is then tested on the remaining set from the training session; for the intersession “pseudo-cross-validation”, the classifier is tested on a single set from each of the four test sessions (a different set at each fold).</p

Example trial cues and timing diagram.

<p>In this example, the user would perform mental arithmetic during intervals ii) and iv) to select options A and B, and would remain in the “no-control” state during all other intervals. In viii), the participant would verify his/her response via the controls shown.</p

Comparison of the scaling exponent, , before and after low-frequency components.

<p>Comparison of the scaling exponent, , before and after low-frequency components.</p

Across-participant least squares mean classification accuracy for the intra- and intersession cross-validation procedures, plotted against training sample size, n.

<p>Results shown are for the case of 6-fold cross validation, which corresponds to a training sample size of 80. Bars indicate 95% confidence intervals.</p

Classifier Training Conditions.

<p><i>n_total</i> denotes the overall size of the training set.</p

Intersession variability in spatiotemporal response characteristics.

<p>Histograms show frequency of feature selection during 25 runs of 6-fold cross-validation (feature subset dimensionality of 5) for each of the 5 sessions for Participant #1. Features (all representing slopes of regression lines fit to the concentration signals) are indicated by a combination of location and time window. Note that no distinction is made between and HHb in the histogram, but for this participant, features were selected approximately two times as often as HHb features (on average across sessions). This is reflective of the overall trend; on average across participants and sessions, features were selected 1.75 times as often as HHb features.</p