Muscle synergy has been applied to comprehend how the central nervous system (CNS) controls movements for decades. However, it is not clear about the motion control mechanism and the relationship between motions and muscle synergies. In this paper, we designed two experiments to corroborate the hypothesis: (1) motions can be decomposed to motion primitives, which are driven by muscle synergy primitives and (2) variations of motion primitives in direction and scale are modulated by activation coefficients rather than muscle synergy primitives. Surface electromyographic (EMG) signals were recorded from nine muscles of the upper limb. Nonnegative matrix factorization (NMF) was applied to extract muscle synergy vectors and corresponding activation coefficients. We found that synergy structures of different movement patterns were similar (α=0.05). The motion modulation indexes (MMI) among movement patterns in reaching movements showed apparent differences. Merging coefficients and reconstructed similarity of synergies between simple motions and complex motions were significant. This study revealed the motion control mechanism of the CNS and provided a rehabilitation and evaluation method for patients with motor dysfunction in exercise and neuroscience.

Remaining useful life (RUL) plays an important role in prognostic and health management to reduce maintenance costs and avoid possible accidents. Massive multi‐sensor data makes it challenging to extract degradation features and predict RUL. This paper develops a novel RUL prediction framework consisted of a multi‐sensor fusion model and a hybrid prediction model. HI curves are constructed by synthesizing multiple metrics including time correlation, monotonicity, robustness, and consistency, making our data fusion method different from existing methods considering only one metric. In many practical situations, the initial degradation levels of obtained HI curves are different, and treating all curves without classification will result in errors in RUL prediction. The K‐means method is used to partition degradation paths into discrete states and the obtained HI curves are grouped by their initial states and then trained separately. A hybrid prediction model combining Long Short‐Term Memory (LSTM) network and Support Vector Regression (SVR) is developed to predict the RUL. The dataset of turbofan engines is used to verify the proposed method. The results show that the proposed method performs better than many existing methods.

Objective. Due to the variability of human movements, muscle activations vary among trials and subjects. However, few studies investigated how data organization methods for addressing variability impact the extracted muscle synergies. Approach. Fifteen healthy subjects performed a large set of upper limb multi-directional point-to-point reaching movements. Then, the study extracted muscle synergies under different data settings and investigated how data structure prior to synergy extraction, namely concatenation, averaging, and single trial, the number of considered trials, and the number of reaching directions affected the number and components of muscle synergies. Main results. The results showed that the number and components of synergies were significantly affected by the data structure. The concatenation method identified the highest number of synergies, and the averaging method usually found a smaller number of synergies. When the concatenated trials or reaching directions was lower than a minimum value, the number of synergies increased with the increase of the number of trials or reaching directions; however, when the number of trials or reaching directions reached a threshold, the number of synergies was usually constant or with less variation even when novel directions and trials were added. Similarity analysis also showed a slight increase when the number of trials or reaching directions was lower than a threshold. This study recommends that at least five trials and four reaching directions and the concatenation method are considered in muscle synergies analysis during upper limb tasks. Significance. This study makes the researchers focus on the variability analysis induced by the diseases rather than the techniques applied for synergies analysis and promotes applications of muscle synergies in clinical scenarios.

Soft electroactive materials including dielectric elastomer (DE) and polyacrylamide (PAM) hydrogel have recently been investigated, which can provide exciting opportunities for optical imaging and biomedical engineering. We propose a tunable liquid lens based on PAM hydrogels, and the miniature lens is also composed of a dielectric elastomer actuator (DEA) and an ionic liquid enclosed. When a biconvex lens is fabricated, a bubble needs to be voided by controlling the pressure. The lens DEA based on PAM electrodes has various resistances that decrease with the stretch. However, it is a constant of 0.49 Ω for the DEA coupling carbon grease electrodes. In a high voltage-driven state, the curvature radius of the lens increased. As a result, the focal length was tuned and enlarged. Computational models are derived for the soft-actuated liquid lens, which improves the existing related theory by detail. Especially, the relationship between voltage and focal length is deduced and verified by experiments. The computational models and experimental phenomena are consistent. Moreover, an increase in pre-stretch and voltage produces a wider tenability range. This study opens the soft electroactive biconvex lenses in potential optical healthcare rehabilitation and optical visual identification applications.

In the complex manufacturing system, multi-channel sensor data are usually recorded for fault detection and diagnosis. The most existing multi-channel data processing methods adopt the tensor analysis technology, which are difficult to describe the temporal and spatial structure of the multi-channel data effectively. The obstacles of multi-channel data analysis are the temporal correlation in the time-series data of the single-channel and the spatial correlation between different channels. In this paper, a novel deep Convolutional Neural Network (CNN) model is proposed for the multi-channel data fusion and intelligent fault diagnosis. Firstly, features of the multi-channel data are extracted from multiple scales. Then, the extracted features are fused through the multi-layer neural network. Finally, the classifier of failure modes is established by using the improved Softmax function. The fault diagnosis performance of the proposed model is evaluated and compared with other common methods in both the simulation study and real-world case study. Results show that the proposed methodology has superior fault diagnosis performance for the multi-channel data.