Detection of workplace sedentary behavior using thermal sensors.

Sedentary behavior has been linked to leading causes of morbidity and mortality, including cancer, cardiovascular disease and diabetes. Those who work in office workplaces are susceptible to higher levels of sedentary behavior during the working day. This paper introduces a novel approach to the detection of sedentary behavior through the use of a thermal sensor mounted on the ceiling above a busy workspace. This solution was found to more accurately record 7 out of 10 activity metrics in comparison to self-assessment, when compared to chair pressure sensor recordings.

The effects of electrode misplacement on clinicians' interpretation of the standard 12-lead electrocardiogram.

INTRODUCTION: This study investigates how a particular incorrect electrode configuration affects the 12-lead Electrocardiogram (ECG). METHODS: A correct and an incorrect 12-lead ECG were extracted from a 192-lead BSPM. This was done for 232 BSPMs yielding 464 12-lead ECGs. The particular incorrect ECG involved displacing electrodes V1 and V2 in the second intercostal space whilst also offsetting the remaining electrodes. These ECGs were examined in two stages: (a) analysis of the effects of electrode misplacement on signal morphology and (b) analysis of how often the incorrect electrode configuration changed the diagnosis of two clinicians in a random sample of 75 patients. RESULTS: According to the Root Mean Square Error (RMSE) of the difference between PQRST intervals in the correct and incorrect ECGs, lead V2 is the most affected lead (mean: 185 µV ± 82 µV), followed by lead V4 (mean: 114 µV ± 59 µV) and lead V1 (mean: 100 µV ± 47 µV). It was found that if the incorrect electrode configuration is applied, there is a 17% to a 24% chance the diagnostic interpretation will be different. Quantified using Similarity Coefficient (SC) leads V1 and V2 were found to be more alike when misplaced in the second intercostal space. The average SC between these leads when correctly placed was 0.08 (± 0.65), however when incorrectly placed, the average SC was 0.43 (± 0.3). CONCLUSION: There is a reasonable chance this particular incorrect electrode configuration will change the diagnosis of the 12-lead ECG. This highlights the importance of developing algorithms to detect electrode misplacement along with better education regarding ECG acquisition.

An intelligent framework for the classification of the 12-lead ECG.

An intelligent framework has been proposed to classify an unknown 12-Lead electrocardiogram into one of a possible number of mutually exclusive and combined diagnostic classes. The framework segregates the classification problem into a number of bi-dimensional classification problems, requiring individual bi-group classifiers for each individual diagnostic class. The bi-group classifiers were generated employing Neural Networks (NN), combined with a combination framework containing an Evidential Reasoning framework to accommodate for any conflicting situations between the bi-group classifiers. A number of different feature selection techniques were investigated with the aim of generating the most appropriate input vector for the bi-group classifiers. It was found that by reducing the original input feature vector, the generalisation ability of the classifiers, when exposed to unseen data, was enhanced and subsequently this reduced the computational requirements of the network itself. The entire framework was compared with a conventional approach to NN classification and a rule based classification approach. The framework attained a significantly higher level of classification in comparison with the other methods; 80.0% compared with 66.7% for the rule based technique and 68.00% for the conventional neural approach.