Design and Fabrication of an Integrated Hollow Concave Cilium MEMS Cardiac Sound Sensor.

In light of a need for low-frequency, high sensitivity and broadband cardiac murmur signal detection, the present work puts forward an integrated MEMS-based heart sound sensor with a hollow concave ciliary micro-structure. The advantages of a hollow MEMS structure, in contrast to planar ciliated micro-structures, are that it reduces the ciliated mass and enhances the operating bandwidth. Meanwhile, the area of acoustic-wave reception is enlarged by the concave architecture, thereby enhancing the sensitivity at low frequencies. By rationally designing the acoustic encapsulation, the loss of heart acoustic distortion and weak cardiac murmurs is reduced. As demonstrated by experimentation, the proposed hollow MEMS structure cardiac sound sensor has a sensitivity of up to -206.9 dB at 200 Hz, showing 6.5 dB and 170 Hz increases in the sensitivity and operating bandwidth, respectively, in contrast to the planar ciliated MEMS sensor. The SNR of the sensor is 26.471 dB, showing good detectability for cardiac sounds.

Design and Realization of MEMS Heart Sound Sensor with Concave, Racket-Shaped Cilium.

The biomedical acoustic signal plays an important role in clinical non-invasive diagnosis. In view of the deficiencies in early diagnosis of cardiovascular diseases, acoustic properties of S1 and S2 heart sounds are utilized. In this paper, we propose an integrated concave cilium MEMS heart sound sensor. The concave structure enlarges the area for receiving sound waves to improve the low-frequency sensitivity, and realizes the low-frequency and high-sensitivity characteristics of an MEMS heart sound sensor by adopting a reasonable acoustic package design, reducing the loss of heart sound distortion and faint heart murmurs, and improving the auscultation effect. Finally, experimental results show that the integrated concave ciliated MEMS heart sound sensor's sensitivity reaches -180.6 dB@500 Hz, as compared with the traditional bionic ciliated MEMS heart sound sensor; the sensitivity is 8.9 dB higher. The sensor has a signal-to-noise ratio of 27.05 dB, and has good heart sound detection ability, improving the accuracy of clinical detection methods.

How do training experience and geographical origin of a runner affect running biomechanics?

BACKGROUND: Several studies compared African runners with runners from other places with difference ethnicities to identify biomechanical factors that may contribute to their extraordinary running performance. However, most studies only assessed runners at the elite level. Whether the performance difference was a result of nature or nurture remains unclear. RESEARCH QUESTIONS: This case study aimed to assess the effect of geographical origin and the effect of training on running biomechanics. METHODS: We recruited twenty male runners from two regions (Asian and Africa) at two performance levels (elite and recreational), and asked them to run on an instrumented treadmill at 12 km∙h-1. We measured running kinetics and kinematics parameters, and focused on the parameters that have been shown associated with running performance. We used Friedman test to compare the effect of geographical origin and training on running biomechanics. RESULTS: Compared to recreational runners, elite runners applied higher amount of ground reaction force in both vertical and anterior-posterior directions (P <  0.05, Cohen's d = 1.63-2.03), together with a longer aerial time (P =  0.039, Cohen's d = 1.11). On the other hand, African runners expressed higher vertical stiffness than Asian runners (P =  0.027, Cohen's d = 0.98). However, the increased vertical stiffness in African runners did not lead to a higher vertical loading rate (P >  0.555, Cohen's d < 0.3), which could be a result of a lower footstrike angle during landing (P =  0.012, Cohen's d = 1.36). SIGNIFICANCE: For elite runners, the higher amount of ground reaction force might facilitate a longer aerial time, but could also lead to higher amount of mechanical energy loss. African runners expressed higher vertical stiffness and higher step rate, which might lead to a lower CoM vertical displacement, and furthermore reduce mechanical energy loss.

Classification of runners' performance levels with concurrent prediction of biomechanical parameters using data from inertial measurement units.

Identification of runner's performance level is critical to coaching, performance enhancement and injury prevention. Machine learning techniques have been developed to measure biomechanical parameters with body-worn inertial measurement unit (IMU) sensors. However, a robust method to classify runners is still unavailable. In this paper, we developed two models to classify running performance and predict biomechanical parameters of 30 subjects. We named the models RunNet-CNN and RunNet-MLP based on their architectures: convolutional neural network (CNN) and multilayer perceptron (MLP), respectively. In addition, we examined two validation approaches, subject-wise (leave-one-subject-out) and record-wise. RunNet-MLP classified runner's performance levels with an overall accuracy of 97.1%. Our results also showed that RunNet-CNN outperformed RunNet-MLP and gradient boosting decision tree in predicting biomechanical parameters. RunNet-CNN showed good agreement (R2 > 0.9) with the ground-truth reference on biomechanical parameters. The prediction accuracy for the record-wise method was better than the subject-wise method regardless of biomechanical parameters or models. Our findings showed the viability of using IMUs to produce reliable prediction of runners' performance levels and biomechanical parameters.