This paper reviews the applications of accelerometers on the detection of physiological acoustic signals such as heart sounds, respiratory sounds, and gastrointestinal sounds. These acoustic signals contain a rich reservoir of vital physiological and pathological information. Accelerometer-based systems enable continuous, mobile, low-cost, and unobtrusive monitoring of physiological acoustic signals and thus can play significant roles in the emerging mobile healthcare. In this review, we first briefly explain the operation principle of accelerometers and specifications that are important for mobile healthcare. Applications of accelerometer-based monitoring systems are then presented. Next, we review a variety of accelerometers which have been reported in literatures for physiological acoustic sensing, including both commercial products and research prototypes. Finally, we discuss some challenges and our vision for future development.

The bean bug, Riptortus pedestris, is a major pest of soybeans. In order to assess the critical stages of soybean damage by R. pedestris, we tested the damage to soybeans at different growth stages (R2, R4, and R6) caused by five densities of R. pedestris (1, 2, 3, 4, and 5) through a field cage experiment. The results show that the R4 stage was the most sensitive stage in terms of suffering R. pedestris injury damage, followed by the R6 stage and then the R2 stage. The number of stay green leaves was 7.04 per plant, the abortive pod rate of the soybeans was 56.36%, and the abortive seed rate of the soybeans was 46.69%. The dry weight of the soybeans was 14.20 g at the R4 stage; these values of R4 were significantly higher than at the R2 and R6 stages. However, the dry weight of soybean seed was 4.27 g and the nutrient transfer rate was 27.01% in the R4 stage; these values were significantly lower than in the R2 and R6 stages. The number of stay green leaves, abortive pod rates, and abortive seed rates were all increased significantly with increasing pest density at each stage of soybean growth. However, the nutrient transfer rate was significantly decreased with the increase in the pest density. Soybean nutrition factors changed after they suffered R. pedestris injury; the lipid content of the soybean seed decreased and the lipid content of the soybean plant increased compared to controls, when tested with a density of five R. pedestris in the R4 stage. These results will be beneficial to the future management of R. pedestris in soybean fields.

Phonocardigraphy (PCG) is the graphical representation of heart sounds. The PCG signal contains useful information about the functionality and the condition of the heart. It also provides an early indication of potential cardiac abnormalities. Extracting cardiac information from heart sounds and detecting abnormal heart sounds to diagnose heart diseases using the PCG signal can play a vital role in remote patient monitoring. In this paper, we have combined different signal processing techniques and a deep learning method to denoise, compress, segment, and classify PCG signals effectively and accurately. First, the PCG signal is denoised and compressed by using a multi-resolution analysis based on the Discrete Wavelet Transform (DWT). Then, a segmentation algorithm, based on the Shannon energy envelope and zerocrossing, is applied to segment the PCG signal into four major parts: the first heart sound (S1), the systole interval, the second heart sound (S2), and the diastole interval. Finally, Mel-scaled power spectrogram and Mel-frequency cepstral coefficients (MFCC) are employed to extract informative features from the PCG signal, which are then fed into a classifier to classify each PCG signal into a normal or an abnormal signal by using a deep learning approach. For the classification, a 5-layer feed-forward Deep Neural Network (DNN) model is used, and overall testing accuracy of around 97.10% is achieved. Besides providing valuable information regarding heart condition, this signal processing approach can help cardiologists take appropriate and reliable steps toward diagnosis if any cardiovascular disorder is found in the initial stage.

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Automated heart sound signal quality assessment is a necessary step for reliable analysis of heart sound signal. An unavoidable processing step for this objective is the heart sound segmentation, which is still a challenging task from a technical viewpoint. In this study, ten features are defined to evaluate the quality of heart sound signal without segmentation. The ten features come from kurtosis, energy ratio, frequency-smoothed envelope, and degree of sound periodicity, where five of them are novel in signal quality assessment. We have collected a total of 7893 recordings from open public heart sound databases and performed manual annotation for each recording as gold standard quality label. The signal quality is classified based on two schemes: binary classification (“unacceptable” and “acceptable”) and triple classification (“unacceptable”, “good,” and “excellent”). Sequential forward feature selection shows that the feature “the degree of periodicity” gives an accuracy rate of 73.1% in binary SVM classification. The top five features dominate the classification performance and give an accuracy rate of 92%. The binary classifier has excellent generalization ability since the accuracy rate reaches to ( 90.4 ± 0.5 ) % even if 10% of the data is used to train the classifier. The rate increases to ( 94.3 ± 0.7 ) % in 10-fold validation. The triple classification has an accuracy rate of ( 85.7 ± 0.6 ) % in 10-fold validation. The results verify the effectiveness of the signal quality assessment, which could serve as a potential candidate as a preprocessing in future automatic heart sound analysis in clinical application.