Energy-aware embedded classifier design for real-time emotion analysis.

Detection and classification of human emotions from multiple bio-signals has a wide variety of applications. Though electronic devices are available in the market today that acquire multiple body signals, the classification of human emotions in real-time, adapted to the tight energy budgets of wearable embedded systems is a big challenge. In this paper we present an embedded classifier for real-time emotion classification. We propose a system that operates at different energy budgeted modes, depending on the available energy, where each mode is constrained by an operating energy bound. The classifier has an offline training phase where feature selection is performed for each operating mode, with an energy-budget aware algorithm that we propose. Across the different operating modes, the classification accuracy ranges from 95% - 75% and 89% - 70% for arousal and valence respectively. The accuracy is traded off for less power consumption, which results in an increased battery life of up to 7.7 times (from 146.1 to 1126.9 hours).

Different strokes for different folks.

A 42-year-old woman, presented to our emergency room with chief complaints of acute onset of dyspnea, left-sided chest pain, and respiratory distress. On arrival, she had blood pressure of 90/50 mm Hg, respiratory rate of 40/min, and oxygen saturation of 95% breathing on 10 L oxygen. She was in significant respiratory distress. On examination, she was found to have diminished breath sounds on the left side of chest. Her chest radiograph showed left massive pleural effusion causing contralateral shift of mediastinum. Diagnostic pleural aspiration was done; results were consistent with hemothorax. Her multidetector computed tomography chest showed features of left-sided hemothorax (high attenuation) along with a 2 cm-enhancing lobulated opacity in the left lingular lobe, suggestive of pulmonary arteriovenous malformation (PAVM). She was successfully treated with surgical resection of PAVM.