Synthesis of sEMG Signals for Hand Gestures Using a 1DDCGAN.

The emergence of modern prosthetics controlled by bio-signals has been facilitated by AI and microchip technology innovations. AI algorithms are trained using sEMG produced by muscles during contractions. The data acquisition procedure may result in discomfort and fatigue, particularly for amputees. Furthermore, prosthetic companies restrict sEMG signal exchange, limiting data-driven research and reproducibility. GANs present a viable solution to the aforementioned concerns. GANs can generate high-quality sEMG, which can be utilised for data augmentation, decrease the training time required by prosthetic users, enhance classification accuracy and ensure research reproducibility. This research proposes the utilisation of a one-dimensional deep convolutional GAN (1DDCGAN) to generate the sEMG of hand gestures. This approach involves the incorporation of dynamic time wrapping, fast Fourier transform and wavelets as discriminator inputs. Two datasets were utilised to validate the methodology, where five windows and increments were utilised to extract features to evaluate the synthesised sEMG quality. In addition to the traditional classification and augmentation metrics, two novel metrics-the Mantel test and the classifier two-sample test-were used for evaluation. The 1DDCGAN preserved the inter-feature correlations and generated high-quality signals, which resembled the original data. Additionally, the classification accuracy improved by an average of 1.21-5%.

Precise prediction of launch speed for athletes in the aerials event of freestyle skiing based on deep transfer learning.

Automatically obtaining the launch speed are powerful guarantees for athletes in the aerials event of freestyle skiing to achieve good results. In most of the published studies describing athletes getting high scores, the assisting sliding distance depends entirely on the coach and even the athlete's own experience, which may not be optimal. The main goal of the present paper is to use an acquisition system and develop an artificial neural network (ANN) model to automatically obtain the corresponding relationship between assisting sliding distance and speed. The influence of snow friction coefficient, wind speed, wind direction, slope, height and weight can be simulated in the Unity3D engine. The influence of temperature, humidity and tilt angle needs to be measured in real world by professional testers which is strenuous. The neural network is first trained by sufficient simulation data to obtain the encoded feature. Then, the information learned in simulation environment is transferred to another network. The second network uses the data taken from twenty professional testers. Compared with the model without transfer learning, the performance of proposed method has significant improvement. The mean squared error for the testing set is 0.692. It is observed that the speed predicted by the designed deep transfer learning (DTL) model is in good agreement with the experimental measurement results. The results indicate that the proposed transfer learning method is an efficient model to be used as a tool for predicting the assisting sliding distance and launch speed for athletes in the aerials event of freestyle skiing.

Non-invasive dual attention TCN for electromyography and motion data fusion in lower limb ambulation prediction.

Objective.Recent technological advances show the feasibility of fusing surface electromyography (sEMG) signals and movement data to predict lower limb ambulation intentions. However, since the invasive fusion of different signals is a major impediment to improving predictive performance, searching for a non-invasive (NI) fusion mechanism for lower limb ambulation pattern recognition based on different modal features is crucial.Approach. We propose an end-to-end sequence prediction model with NI dual attention temporal convolutional networks (NIDA-TCNs) as a core to elegantly address the essential deficiencies of traditional decision models with heterogeneous signal fusion. Notably, the NIDA-TCN is a weighted fusion of sEMG and inertial measurement units with time-dependent effective hidden information in the temporal and channel dimensions using TCN and self-attentive mechanisms. The new model can better discriminate between walking, jumping, downstairs, and upstairs four lower limb activities of daily living.Main results. The results of this study show that the NIDA-TCN models produce predictions that significantly outperform both frame-wise and TCN models in terms of accuracy, sensitivity, precision, F1 score, and stability. Particularly, the NIDA-TCN with sequence decision fusion (NIDA-TCN-SDF) models, have maximum accuracy and stability increments of 3.37% and 4.95% relative to the frame-wise model, respectively, without manual feature-encoding and complex model parameters.Significance. It is concluded that the results demonstrate the validity and feasibility of the NIDA-TCN-SDF models to ensure the prediction of daily lower limb ambulation activities, paving the way to the development of fused heterogeneous signal decoding with better prediction performance.

Comparative metabolome analysis unravels a close association between dormancy release and metabolic alteration induced by low temperature in lily bulbs.

KEY MESSAGE: The correlation between dormancy release and metabolic metabolic changes in lily bulbs during low temperature storage was investigated. Low temperature is a major environmental factor required for dormancy release in lily bulbs. Although great advances in plant metabolomics have been achieved, knowledge about the molecular basis of lily bulb metabolomes at different developmental stages in response to low temperature is still limited. In this work, the dormancy release, vegetative growth, flowering, metabolic profile and gene expression in the less dormant cultivar Lilium longiforum × Oriental hybrid 'Triumphator' (T) and the more dormant cultivar Lilium Asiatic hybrid 'Honesty' (H) were compared. Exposure to low temperature (LT) successfully promoted stem elongation, floral transition and flowering of both T and H bulbs. However, exposure to room temperature (RT) restricted stalk elongation of both T and H bulbs, and prohibited floral transition and flowering of H bulbs. Correspondingly, higher antioxidant enzyme activity and total primary metabolite contents were observed in the apical bud of T bulbs. Gene expression analysis revealed that expressions of LiFT, LiFLK, LiSOC1 and LiCBF were decreased, whereas the expression of LiSVP and LiFLC were increased, in the apical bud of H bulbs under RT storage condition. Our findings reveal that the growth and dormancy breaking of lily bulbs are closely associated with the metabolic changes in the apical buds during postharvest storage.

The changes in pyroptosis-related inflammatory factors in the peripheral blood of patients with Henoch-Schonlein purpura.

BACKGROUND: Henoch-Schonlein purpura (HSP) is a common capillary allergic bleeding disease. To explore the variation of pyroptosis-related inflammatory factors level in the peripheral blood of patients with HSP. METHODS: A total of 87 HSP patients treated in our hospital from June 2020 to March 2021 were selected and divided into the renal impairment group (n=29) and the non-renal impairment group (n=58) according to the presence of hematuria and proteinuria. A total of 50 healthy individuals from the hospital were selected as the control group. The renal impairment and non-renal impairment groups were treated with a regular regimen of compound glycyrrhizin tablets and glucocorticoids, respectively. Serum interleukin (IL)-18, IL-1ß, and peripheral caspase-1-positive cells were compared pre- and post-treatment among the three groups. RESULTS: The pre-treatment serum IL-1ß levels in the renal impairment and non-renal impairment groups were significantly higher than that in the control group (P<0.01). After treatment, the IL-1ß level in the non-renal impairment group was not significantly different from that in the control group (P>0.05). However, the IL-1ß level in the renal impairment group post-treatment was significantly higher than that in the other two groups (P<0.01). The positive rate of caspase-1 expression in peripheral blood before treatment in the renal impairment group and non-renal impairment group was significantly higher than that in the control group (P<0.01). After treatment, the positive rate of caspase-1 expression in the non-renal impairment group was comparable to that in the control group (P>0.05), whereas the rate in the renal impairment group was significantly higher than that in the other two groups (P<0.01). After treatment, the serum IL-1ß levels and caspase-1 positive rate in HSP patients who were responsive to treatment (as assessed by hematuria or proteinuria levels after treatment) were lower than that in patients who were unresponsive to treatment P<0.001), but not significantly different to the control group (P>0.05). CONCLUSIONS: The levels of serum IL-1ß and caspase-1 changed in response to alterations in the disease condition and treatment response in HSP patients, which suggested that pyroptosis-related inflammatory factors may have potential application value in predicting disease progression and efficacy of hormone therapy.