The continuous monitoring of blood pressure (BP) has been found to significantly predict the risk of severe cardiovascular disease. Pulse arrival time (PAT), generally extracted from synchronized photoplethysmogram (PPG) and electrocardiogram (ECG) signals, is widely adopted in noninvasive blood pressure studies. However, motion artifact and physical activities introduce different levels of noise to the ECG and PPG signals in wearable devices, resulting in large fluctuations in PAT-based BP estimation, which may confuse and mislead users. We explored the potential of Kalman filter to enhance the stability of continuous BP estimation. We developed an Android application collecting data from the wearable device via Bluetooth technology, two Kalman filters were designed and implemented to evaluate the systolic blood pressure (SBP) and pulse pressure (PP) separately, whose gains were adjusted automatically by signal quality indicators. Validation experiments were performed on 6 volunteers to ensure the effectiveness of Kalman filters, and the preliminary results compared with a standard commercial sphygmomanometer showed that our approach can achieve higher stability than the method without Kalman filtering.

This paper presents a design and a contrast test of an ultra-low power wireless health monitoring system capable of measuring a subject's ECG (Electrocardiography), respiration, and body temperature. The system is based on the BLE (Bluetooth Low Energy) technology which is most valued for its ultra-low power consumption. Compared to our former design using MSP430 MCU and Bluetooth 2.1, this new design is much more highly integrated and can reduce power consumption significantly, which is generally a vital problem should be considered in WSN (Wireless Sensor Network) issues. The new system can save as much as nearly 75% power consumption than the former design when working in the same mode with the sampling rate at 250 Hz, which means that the battery life can extend to 107 hours compared to 26 hours of the former one, both using a 3.7 V lithium polymer battery with the capacity of 1100 mAh. Meanwhile, the new design can connect 3 nodes simultaneously with a single PC or smart phone wirelessly; this number may increase with the new BLE stack will be released in the future.

Distribution mismatch between training and test data can greatly deteriorate the performance of language recognition. Some effective methods for compensation have been proposed, such as nuisance attribute projection (NAP). In real-world applications, there are often sufficient training samples from a different domain and only a limited number of labeled training samples from target domain, performance of a system will be degraded and needs to be further improved. In this paper, we introduce transfer learning to solve this problem. We propose a novel transfer learning algorithm referred to as simplified domain transfer multiple kernel learning (SDTMKL). Our aim is to discover a good representation of feature space that minimizes the distribution mismatch between samples from the source and target domains. Robust models can be learned in this suitable feature space. Results on a NIST language recognition task show that the SDTMKL method is quite effective and can further improve system performance when combined with NAP.

In second language learning, unawareness of the differences between correct and incorrect pronunciations is one of the largest obstacles for mispronunciation correction. In order to make the feedback more discriminatively perceptible, this paper presents a novel method for corrective feedback generation, namely, exaggerated feedback, for language learning. To produce exaggeration effect, the neutral audio and visual speech are both exaggerated and then re-synthesized synchronously based on the audiovisual synthesis technology. The audio speech exaggeration is realized by adjusting the acoustic features related to duration, pitch and energy of the speech according to different phonemes conditions. The visual speech exaggeration is realized by increasing the range of articulatory movement and slowing down the movement around the key actions. The results show that our methods can effectively generate bimodal exaggeration effect for feedback provision and make them more distinctive to be perceived.

Articulatory features describe how articulators are involved in making sounds. Speakers often use a more exaggerated way to pronounce accented phonemes, so articulatory features can be helpful in pitch accent detection. Instead of using the actual articulatory features obtained by direct measurement of articulators, we use the posterior probabilities produced by multi-layer perceptrons (MLPs) as articulatory features. The inputs of MLPs are frame-level acoustic features pre-processed using the split temporal context-2 (STC-2) approach. The outputs are the posterior probabilities of a set of articulatory attributes. These posterior probabilities are averaged piecewise within the range of syllables and eventually act as syllable-level articulatory features. This work is the first to introduce articulatory features into pitch accent detection. Using the articulatory features extracted in this way, together with other traditional acoustic features, can improve the accuracy of pitch accent detection by about 2%.

Cardiovascular disease (CVD) is the leading cause of global death. The Electrocardiogram (ECG) is the most widely adopted tool that measures the electrical activities of the heart from the body surface. However, heart rhythm irregularities and other heat attack problems cannot always be detected on a standard resting ECG machine, since they may not occur during an individual's recording session. Common Holter-based portable ECG detection systems lack the capability to provide real-time feedback and has allergic and discomfort problems cased by wet Ag/AgCl electrodes. In this paper, we seek to establish a cell phone-based real-time monitoring system for CVD. To improve the comfort during long term ECG monitoring, a noncontact ECG detection system based on capacitive coupling was designed, fabricated and tested. Capacitive sensing of ECG allows unobtrusively monitoring of the heart, thus has a lot of advantage over the conventional wet Ag/AgCl electrodes, such as zero preparation time, insensitive to skin conditions and has the potential to be embedded in the clothes etc.