Journal of Mechanics in Medicine and Biology

Background: Knee joint stability is enhanced by ligamentus structures such as anterior cruciate (ACL), posterior cruciate (PCL), medial collateral (MCL) and lateral collateral ligaments (LCL). Rupture of ACL is the most common knee injuries, especially in sport related activities. The aim of this study is to evaluate the stress developed in knee joint structures in various degrees of knee flexion in ACL ruptured compared to normal condition.

Method: CT scan images of knee joint were used to create 3d model of knee joint by use of Mimics software. Abaqus software was used to evaluate the stress developed in knee joint in normal and in ACL reconstructed conditions in various degrees of knee flexion.

Results: The stress developed in ACL and other knee joint structures increased significantly by increase in knee joint flexion. The stress of knee joint structures (especially in ACL) in ACL reconstructed condition was more than that of normal condition.

Conclusion: It is recommended to immobilize the knee joint in extension up to $15^{\circ }$ of knee flexion in those with ACL injuries. The stress of ACL increased due to an increase in tibia translation associated with knee flexion.

Studies have shown that rehabilitation training with the unaffected side guiding affected side is more consistent with the natural movement pattern of human upper limb compared with unilateral rehabilitation training, which is conducive to improve rehabilitation effect of the affected limb motor function. In this paper, a bilateral end-effector upper limb rehabilitation robot (BEULRR) based on two modern commercial manipulators is developed first, then the kinematics, reachability, and dexterity analysis of BEULRR are performed, respectively. Finally, a bilateral symmetric training protocol with the unaffected side guiding the affected side is proposed and evaluated through healthy human subject experiment testing based on BEULRR. The simulation results show that the developed BEULRR could perform spatial rehabilitation training and its rehabilitation training workspace can fully cover the physiological workspace of human upper limb. The preliminary experiment results from the healthy human subject show that the BEULRR system could provide reliable bilateral symmetric training protocol. These simulation and experiment results demonstrated that the developed BEULRR system could be used in bilateral rehabilitation training application, and also show that the BEULRR system has the potential to be applied to clinical rehabilitation training in the further step. In the close future, the proposed BEULRR and bilateral symmetric training protocol are planned to be applied in elderly volunteers and patients with upper limb motor dysfunction for further evaluating.

In order to investigate and analyze the caries status of permanent first molars (PFMs) in children aged 10 to 12 years in Southern China and to analyze the related factors, from January to August 2019, a multi-stage, stratified, cluster, and random sampling method had been used to select 6208 children from 22 schools in Guangzhou city for oral health examinations and questionnaires. The total caries rate of the PFMs was 39.40%, total DMFT score was $0.85\pm 0.28$, the filling rate of caries was 3.38%, and the rate of fissure sealant was 5.89%. The percentage of PFM caries was statistically significant between female and boys, urban and rural areas, only-children and non-only children, and between ethnic minorities and Han ethnic groups ($P<0.05$). The difference between urban and rural areas was statistically significant ($P<0.05$). Multivariate logistic regression analysis showed that the number of daily toothbrushes, the number of desserts eaten, milk drinking before bedtime, being the only-child, and the parents’ level of education were the independents influencing the factors of for developing dental caries. The incidence of dental caries in the PFMs of children aged 10–12 years in Southern China is high, and the rate of fissure sealant and the rate of dental fillings are low. There are a lot of differences between urban and rural areas, where children’s oral health knowledge is poor, and poor oral hygiene behaviors and dietary habits exist.

A great increase in the number of cardiovascular cases has been a cause of serious concern for the medical experts all over the world today. In order to achieve valuable risk stratification for patients, early prediction of heart health can benefit specialists to make effective decisions. Heart sound signals help to know about the condition of heart of a patient. Motivated by the success of cepstral features in speech signal classification, authors have used here three different cepstral features, viz. Mel-frequency cepstral coefficients (MFCCs), gammatone frequency cepstral coefficients (GFCCs), and Mel-spectrogram for classifying phonocardiogram into normal and abnormal. Existing research has explored only MFCCs and Mel-feature set extensively for classifying the phonocardiogram. However, in this work, the authors have used a fusion of GFCCs with MFCCs and Mel-spectrogram, and achieved a better accuracy score of 0.96 with sensitivity and specificity scores as 0.91 and 0.98, respectively. The proposed model has been validated on the publicly available benchmark dataset PhysioNet 2016.

In order to improve the accuracy and precision of online learning-based collision detection methods, an online active ensemble learning for robot collision detection (OAELRCD) is proposed in this paper. The OAELRCD consists of two key components: (1) an ensemble learning method to combine several base classifiers in order to improve the accuracy and precision of collision detection, (2) an active learning algorithm to reduce the number of training samples in order to realize online training and learning when the environment changes. We evaluate the proposed OAELRCD on one robot arm in dynamic environments with moving workspace obstacles, showing that the proposed OAELRCD outperforms state-of-the-art online learning-based method and geometric collision checkers. Compared to the state-of-the-art online learning-based method for robot collision detection in dynamic environments, the proposed OAELRCD provides noticeable improvements in TPR, AUC, Accuracy and TNR. Compared to state-of-the-art geometric collision checkers, with the proposed OAELRCD, collision checks are faster.

The purpose of this study is to suggest sound biofeedback, which is a new technique of early stress relief effect by observing change in the heart rate variability (HRV). The sound biofeedback imitating heart rate of the comfortable and stress state is termed parasympathetic stimulation sound (PSS) and sympathetic stimulation sound (SSS), respectively. Twelve subjects were selected without previous history of cardiovascular diseases and mental illness, such as arrhythmia, myocardial infarction, depression and panic disorder. To confirm the changes in the low-frequency (LF), high-frequency (HF) and LF/HF values of HRV as stress evaluation indicators, the HRV of subjects was measured by photoplethysmogram. Signals were processed using the peak detect algorithm, and fast Fourier transform. Results were obtained using power specific densities. During the PSS stimulation, the LF/HF tended to decrease generally. On the other hand, during the SSS stimulation, LF/HF tended to increase. The LF/HF Mean change value ($P<0.05$) using the PSS stimulation is similar to the effect of Transcutaneous Vagal Nerve Stimulation (tVNS). In addition, the quantitative effect of sound biofeedback was confirmed by judging changes in the parasympathetic and sympathetic nerves in the autonomic nervous system (ANS) through $Z$-score normalized data. These experimental results suggest that sound biofeedback has the same stimulation location and clinical applicability as tVNS. As a result, sound biofeedback may be used as a new method for stress reduction.

Aortic fenestration (AF) uses puncture and a dilation balloon to create a tear in the intimal flap, which can directly relieve ischemia syndrome and reduce hypertension in the false lumen. The selection of a dilation balloon as well as the area of the created tear applied in reality depend on clinical experience, so we aim to provide a quantitative guidance and reference for doctors to better plan the treatment of aortic fenestration. In this study, the area of the created tear was virtually enlarged to at least 10 different values for four cases including one ideal case, and a computational fluid dynamic approach was applied to simulate blood flows in the aorta. The area ratio (AR) between the created tear and entry tear was introduced to express the enlargement of the created tear. The quantitative hemodynamic results indicate that the AR should be controlled to be larger than 7.0, but not too big to obtain the best treatment for acute aortic dissection (AD) case. Additionally, we assessed that AR might also be a risk factor for the prediction of dissection propagation.

Ankle sprain is very common in sports, and a commonly suggested etiology is the delayed peroneal muscle reaction time. Recent studies showed the successful attempts to deliver electrical stimulation to the peroneal muscles externally to initiate contraction before it could react, however, the success relies on a workable method to detect ankle sprain injury in time. This study presented a fast Fourier transform and polynomial fitting method with dorsal foot kinematics data for quick ankle sprain detection. Five males performed 100 simulated ankle sprain and 250 common sporting motion trials. Eight gyrometers recorded the three-dimensional angular velocities at 500Hz. Data were trimmed with a 0.11s window size, the suggested duration of preinjury phase in ankle sprain, and were transformed from time to frequency domain by fast Fourier transform and fitted with a fifth-order polynomial. First-order coefficients from polynomial fitting on frequency space were obtained. The method achieved 97.0% sensitivity and 91.4% specificity in identifying simulated sprains, vertical jump–landing, cutting, stepping-down, running, and walking motions, with vertical jump–landing excluded due to its relatively low specificity (67.3%). The method can be used to detect ankle sprain in sports with mainly floor movements and minimal vertical jump–landing motion.

Early classification and diagnosis of lung diseases is essential to increase the best chance of patient recovery and survival. Using deep learning to make it possible, the key is how to improve the robustness of the deep learning model and the accuracy of lung image classification. In order to classify the five lung diseases, we used transfer learning to improve and fine-tune the fully connected layer of VGG16, and improve the cross entropy loss function, combined with the gradient boosting decision tree (GBDT), to establish a deep learning model called a classifier. The model was trained using the ChestX-ray14 dataset. On the test set, the classification accuracy of our model for the five lung diseases was 82.43%, 95.37%, 82.11%, 79.81%, 78.13%, which is better than the best published results. The F1 value is 0.456 (95% CI 0.415, 0.496). The robustness of the model exceeds CheXNet and average performance of doctors. This study clarified that the model has strong robustness and effectiveness in classifying five lung diseases.