In this study, based on inspiration drawn from origami and the suction mechanism of leeches, a dry electrode is developed for reliable blood pressure (BP) monitoring. The leech-inspired suction mechanism generated a local soft vacuum facilitating appropriate contact with the human skin. Subsequently, an electrocardiogram (ECG) sensor, termed a leech-inspired origami (LIO) sensor, was constructed using the developed dry electrode. The LIO with a sensing robot system ensures reliable ECG signals with a signal-to-noise ratio of 21.7 ± 0.56 dB. From the paired detection of ECG and photoplethysmography (PPG) through human–robot interaction, BP monitoring was demonstrated. The average difference of the systolic BP between that estimated by the sensing robot and that monitored by the sphygmomanometer was 0.03 mmHg, indicating the reliable BP monitoring ability of the sensing robot. The LIO sensing system inspired by origami and leech behaviors makes BP sensing tools feasible, which in turn would further the development of a remote healthcare monitoring robotic system.

We present a novel approach for using the pattern position distribution as features to detect software failure. In this approach, we divide an execution sequence into several sections and compute the pattern distribution in each section. The distribution of all patterns is then used as features to train a classifier. This approach outperforms conventional frequency based methods by more effectively identifying software failures occurring through misused software patterns. Comparative experiments show the effectiveness of our approach.

No abstract

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