Lower limb ankle exoskeletons have been used to improve walking efficiency and assist the elderly and patients with motor dysfunction in daily activities or rehabilitation training, while the assistance patterns may influence the wearer’s lower limb muscle activities and coordination patterns. In this paper, we aim to evaluate the effects of different ankle exoskeleton assistance patterns on wearer’s lower limb muscle activities and coordination patterns. A tethered ankle exoskeleton with nine assistance patterns that combined with differenet actuation timing values and torque magnitude levels was used to assist human walking. Lower limb muscle surface electromyography signals were collected from 7 participants walking on a treadmill at a speed of 1.25 m/s. Results showed that the soleus muscle activities were significantly reduced during assisted walking. In one assistance pattern with peak time in 49% of stride and peak torque at 0.7 N·m/kg, the soleus muscle activity was decreased by (38.5 ± 10.8)%. Compared with actuation timing, the assistance torque magnitude had a more significant influence on soleus muscle activity. In all assistance patterns, the eight lower limb muscle activities could be decomposed to five basic muscle synergies. The muscle synergies changed little under assistance with appropriate actuation timing and torque magnitude. Besides, co-contraction indexs of soleus and tibialis anterior, rectus femoris and semitendinosus under exoskeleton assistance were higher than normal walking. Our results are expected to help to understand how healthy wearers adjust their neuromuscular control mechanisms to adapt to different exoskeleton assistance patterns, and provide reference to select appropriate assistance to improve walking efficiency.
Emotion can reflect the psychological and physiological health of human beings, and the main expression of human emotion is voice and facial expression. How to extract and effectively integrate the two modes of emotion information is one of the main challenges faced by emotion recognition. In this paper, a multi-branch bidirectional multi-scale time perception model is proposed, which can detect the forward and reverse speech Mel-frequency spectrum coefficients in the time dimension. At the same time, the model uses causal convolution to obtain temporal correlation information between different scale features, and assigns attention maps to them according to the information, so as to obtain multi-scale fusion of speech emotion features. Secondly, this paper proposes a two-modal feature dynamic fusion algorithm, which combines the advantages of AlexNet and uses overlapping maximum pooling layers to obtain richer fusion features from different modal feature mosaic matrices. Experimental results show that the accuracy of the multi-branch bidirectional multi-scale time sensing dual-modal emotion recognition model proposed in this paper reaches 97.67% and 90.14% respectively on the two public audio and video emotion data sets, which is superior to other common methods, indicating that the proposed emotion recognition model can effectively capture emotion feature information and improve the accuracy of emotion recognition.
Exploring the functional network during the interaction between emotion and cognition is an important way to reveal the underlying neural connections in the brain. Sparse Bayesian network (SBN) has been used to analyze causal characteristics of brain regions and has gradually been applied to the research of brain network. In this study, we got theta band and alpha band from emotion electroencephalogram (EEG) of 22 subjects, constructed effective networks of different arousal, and analyzed measurements of complex network including degree, average clustering coefficient and characteristic path length. We found that: ① compared with EEG signal of low arousal, left middle temporal extensively interacted with other regions in high arousal, while right superior frontal interacted less; ② average clustering coefficient was higher in high arousal and characteristic path length was shorter in low arousal.
Objective
To investigate the status quo and influencing factors of depression and anxiety in postoperative patients with thoracic neoplasms.
Methods
The general information questionnaire and Huaxi emotional-distress index scale (HEI) were adopted to survey 70 patients after surgery of thoracic neoplasms at the thoracic nursing outpatients from September to November 2016. There were 43 males and 27 females with age of 18-78 (56.20±11.34) years.
Results
The prevalence rate of depression and anxiety among postoperative patients with thoracic neoplasms was 50.0%, and moderate to severe negative emotions predominated. There was significant difference in educational levels, postoperative hospitalization and postoperative complications (P<0.05), while no significant difference in age, gender, disease types, complicated diseases, surgical procedures, pathological stages and hospitalization expenditures between patients with unhealthy emotions and normal emotions (P>0.05).
Conclusion
There is a high prevalence rate of negative emotion among postoperative patients with thoracic neoplasms. Educational levels, postoperative hospitalization and postoperative complications are important factors for negative emotion.
Focused on the world-wide issue of improving the accuracy of emotion recognition, this paper proposes an electroencephalogram (EEG) signal feature extraction algorithm based on wavelet packet energy entropy and auto-regressive (AR) model. The auto-regressive process can be approached to EEG signal as much as possible, and provide a wealth of spectral information with few parameters. The wavelet packet entropy reflects the spectral energy distribution of the signal in each frequency band. Combination of them gives a better reflect of the energy characteristics of EEG signals. Feature extraction and fusion are implemented based on kernel principal component analysis. Six emotional states from a public multimodal database for emotion analysis using physiological signals (DEAP) are recognized. The results show that the recognition accuracy of the proposed algorithm is more than 90%, and the highest recognition accuracy is 99.33%. It indicates that this algorithm can extract the feature of EEG emotion well, and it is a kind of effective emotion feature extraction algorithm, providing support to emotion recognition.
ObjectiveTo investigate the negative emotions of patients before cardiac surgery in West China Hospital in order to analyze the related factors.MethodsThe Huaxi emotional-distress index (HEI), a screening tool for mood disorders developed by the Mental Health Center of West China Hospital, was used for preoperative psychological evaluation of 1 968 adult patients hospitalized in cardiac surgery from March 2016 to July 2014. There were 835 males and 1 133 females at age of 49±13 years.Results Fifty-one patients (2.6%) had negative emotions, among whom 6 patients were screened for suicide risk. After intervention, none of them had serious consequences caused by adverse emotions, such as automatic discharge from hospital, avoidance of surgery and suicide.ConclusionThis study found that most of the cardiac surgery patients in West China Hospital have good psychological status before surgery, and a few suffered from negative emotions. “Huaxi emotional-distress index” is simple, effective and worth promoting.
Objective To explore the association between behavioral, emotional problems and life events among adolescents, and to determine which factors of life events correlate most highly with the behavioral, emotional problems. Method A total of 1 325 adolescents were investigated with Youth Self-Report (YSR) of Achenbach’s behavior checklist and Adolescent Self-Rating Life Events Checklist (ASLEC), and the data were analyzed with canonical correlation analysis. Results Canonical correlation was statistically significant. The correlation coefficients of the first pair of canonical variables in the male and female group were 0.631 3 and 0.621 1, respectively, and the cumulative proportion of the first two pairs of canonical variables was above 0.95. In the first pair of canonical variables, the loadings of anxious/depressed, interpersonal sensitivity and study pressure were higher, while in the second pair, withdrawal and punishment were the most important factors. Conclusions The effects of life events on emotional problems mainly contributed to interpersonal sensitivity and study pressure.
ObjectiveTo summarize research progress of the effect of knee flexion position on postoperative blood loss and knee range of motion (ROM) after total knee arthroplasty (TKA).MethodsThe relevant literature at home and abroad was reviewed and summarized from mechanism, research status, progress, and clinical outcome. The differences of clinical results caused by different positions, flexion angles, and keeping time were compared.ResultsKeeping knee flexion after TKA can reduce postoperative blood loss through the angle change of blood vessels and increase knee early ROM by improving flexion muscle strength. When the flexion angle of the knee is large and the flexion position is keeping for a long time, the postoperative blood loss and the knee ROM can be significantly improved. However, the amount of blood loss and ROM are not further improved in the patients with keeping knee flexion for more than 24 hours compared with less than 24 hours.ConclusionKeeping knee flexion after TKA is a simple and effective method to reduce postoperative blood loss and improve knee ROM. However, the optimal knee flexion angle and time are needed to be further explored.
At present, the potential hazards of infrasound on heart health have been identified in previous studies, but a comprehensive review of its mechanisms is still lacking. Therefore, this paper reviews the direct and indirect effects of infrasound on cardiac function and explores the mechanisms by which it may induce cardiac abnormalities. Additionally, in order to further study infrasound waves and take effective preventive measures, this paper reviews the mechanisms of cardiac cell damage caused by infrasound exposure, including alterations in cell membrane structure, modulation of electrophysiological properties, and the biological effects triggered by neuroendocrine pathways, and assesses the impact of infrasound exposure on public health.
