Effect of sparsity-aware time–frequency analysis on dynamic hand gesture classification with radar micro-Doppler signatures

Dynamic hand gesture recognition is of great importance in human-computer interaction. In this study, the authors investigate the effect of sparsity-driven time-frequency analysis on hand gesture classification. The time-frequency spectrogram is first obtained by sparsity-driven time-frequency analysis. Then three empirical micro-Doppler features are extracted from the time-frequency spectrogram and a support vector machine is used to classify six kinds of dynamic hand gestures. The experimental results on measured data demonstrate that, compared to traditional time-frequency analysis techniques, sparsity-driven time-frequency analysis provides improved accuracy and robustness in dynamic hand gesture classification

Dynamic Hand Gesture Classification Based on Radar Micro-Doppler Signatures

Dynamic hand gesture recognition is of great importance for human-computer interaction. In this paper, we present a method to discriminate the four kinds of dynamic hand gestures, snapping fingers, flipping fingers, hand rotation and calling, using a radar micro-Doppler sensor. Two micro-Doppler features are extracted from the time-frequency spectrum and the support vector machine is used to classify these four kinds of gestures. The experimental results on measured data demonstrate that the proposed method can produce a classification accuracy higher than 88.56%

Multi-source adversarial transfer learning based on similar source domains with local features

Transfer learning leverages knowledge from other domains and has been successful in many applications. Transfer learning methods rely on the overall similarity of the source and target domains. However, in some cases, it is impossible to provide an overall similar source domain, and only some source domains with similar local features can be provided. Can transfer learning be achieved? In this regard, we propose a multi-source adversarial transfer learning method based on local feature similarity to the source domain to handle transfer scenarios where the source and target domains have only local similarities. This method extracts transferable local features between a single source domain and the target domain through a sub-network. Specifically, the feature extractor of the sub-network is induced by the domain discriminator to learn transferable knowledge between the source domain and the target domain. The extracted features are then weighted by an attention module to suppress non-transferable local features while enhancing transferable local features. In order to ensure that the data from the target domain in different sub-networks in the same batch is exactly the same, we designed a multi-source domain independent strategy to provide the possibility for later local feature fusion to complete the key features required. In order to verify the effectiveness of the method, we made the dataset "Local Carvana Image Masking Dataset". Applying the proposed method to the image segmentation task of the proposed dataset achieves better transfer performance than other multi-source transfer learning methods. It is shown that the designed transfer learning method is feasible for transfer scenarios where the source and target domains have only local similarities.Comment: Submitted to Information Fusio

New Challenges Regarding the Intervention of Musculoskeletal Risk in Truck Service Garages

Background: The automotive industry is heavily affected by sick leaves caused by the handling of loads and using postures that produce musculoskeletal disorders. Research is needed to analyse their causes and find possible solutions to eliminate or mitigate these risks. Objective: Our objective was to analyse the level of musculoskeletal risk in the different work tasks performed by truck and bus mechanics. Our intention is also to analyse whether postural training and feedback can help reduce risk. Methods: The rapid entire body assessment (REBA) was used to assess the postures performed by 35 mechanics from eight branches throughout Spain. The participants were subsequently divided randomly into two groups (experimental group and control group). The experimental group (EG) was given training and feedback on their postures and the control group (CG) was not offered any type of intervention. A few months after the initial assessment, their postural load in the usual tasks was re-evaluated. Results: An overall average REBA Score: 10.49 ± 1.33. The main risk was found in the trunk and arms with sustained above-the-head postures. EG’s second results are significantly improved compared to the first (p = 0.026 *). Conclusions: These jobs have a high-risk level of musculoskeletal disorders. The course of action presented with postural training and feedback has shown satisfactory results. Nevertheless, given the size of the sample, further research will be needed to delve deeper into this possibility as a future line of intervention.Depto. de Psicología Social, del Trabajo y DiferencialFac. de PsicologíaTRUEpu

Does Postural Feedback Reduce Musculoskeletal Risk?: A Randomized Controlled Trial

Background: There is a high prevalence of musculoskeletal disorders among personnel working in the healthcare sector, mainly among nursing assistants and orderlies. Objective: The objective is to analyze the effectiveness of a multi-component intervention that included postural feedback in reducing musculoskeletal risk. Method: A total of 24 nursing assistants and orderlies in a hospital setting were randomly assigned to an intervention group or a control group. After collecting sociodemographic information, a selection of tasks was made and assessed using the REBA (rapid entire body assessment) method. A multi-component intervention was designed combining theoretical and practical training, including feedback on the postures performed by the professionals involved, especially those involving high musculoskeletal risk. This program was applied only to participants in the intervention group. Subsequently, eight months after the first assessment and intervention, the second assessment was carried out using the same method and process as in the first evaluation. Results: The results indicate that the musculoskeletal risk in the second assessment in the intervention group was significantly reduced. However, no significant changes were observed in the control group. Conclusion: The multi-component intervention applied can significantly reduce the musculoskeletal risk of nursing assistants and orderlies. In addition, it is a low-cost intervention with great applicability.Depto. de Psicología Social, del Trabajo y DiferencialFac. de PsicologíaTRUERegional Health Departmentpu

Reform and Practice of Analog Circuits

In the new century, education has become the focus of the reform. At present, cross, penetration and integration between basic courses are the key to improve the quality of teaching and the overall quality of students. University of Electronic Science and Technology of China (UESTC) combines "circuit analysi" and "fundamentals of analog circuits" as one course "electronic circuit", the curriculum reform follows the principles of strengthening the foundation, updating the structure, penetrating the interdisciplinary and simplifying the courses. This paper discusses the principles and ideas of reforms related to the "electronic circuit": the results show that the teaching can broaden the knowledge and vision of students, as a result, the students can better adapt to the requirements of learning and challenge of the new era

Differences in the Gut Microbiota Establishment and Metabolome Characteristics Between Low- and Normal-Birth-Weight Piglets During Early-Life

Low-birth-weight (LBW) piglets are at a high-risk for postnatal growth failure, mortality, and metabolic disorders later in life. Early-life microbial exposure is a potentially effective intervention strategy for modulating the health and metabolism of the host. Yet, it has not been well elucidated whether the gut microbiota development in LBW piglets is different from their normal littermates and its possible association with metabolite profiles. In the current study, 16S rRNA gene sequencing and metabolomics was used to investigate differences in the fecal microbiota and metabolites between LBW and normal piglets during early-life, including day 3 (D3), 7 (D7), 14 (D14), 21 (D21, before weaning), and 35 (D35, after birth). Compared to their normal littermates, LBW piglets harbored low proportions of Faecalibacterium on D3, Flavonifractor on D7, Lactobacillus, Streptococcus, and Prevotella on D21, as well as Howardella on D21 and D35. However, the abundance of Campylobacter on D7 and D21, Prevotella on D14 and D35, Oscillibacter and Moryella on D14 and D21, and Bacteroides on D21 was significantly higher in LBW piglets when compared with normal piglets. The results of the metabolomics analysis suggested that LBW significantly affected fecal metabolites involved in fatty acid metabolism (e.g., linoleic acid, α-linolenic acid, and arachidonic acid), amino acid metabolism (e.g., valine, phenylalanine, and glutamic acid), as well as bile acid biosynthesis (e.g., glycocholic acid, 25-hydroxycholesterol, and chenodeoxycholic acid). Spearman correlation analysis revealed a significant negative association between Campylobacter and N1-acetylspermine on D7, Moryella and linoleic acid on D14, Prevotella and chenodeoxycholic acid on D21, and Howardella and phenylalanine on D35, respectively. Collectively, LBW piglets have a different gut bacterial community structure when compared with normal-birth-weight (NBW) piglets during early-life, especially from 7 to 21 days of age. Also, a distinctive metabolic status in LBW piglets might be partly associated with the altered intestinal microbiota. These findings may further elucidate the factors potentially associated with the impaired growth and development of LBW piglets and facilitate the development of nutritional interventions

Prognostic Value of Nicotinamide N-Methyltransferase Expression in Patients With Solid Tumors: A Systematic Review and Meta-Analysis

Background: Nicotinamide N-methyltransferase (NNMT) is an enzyme that catalyzes N-methylation of pyridine-containing compounds. NNMT is upregulated in many types of solid tumors, suggesting the potential for its use as a tumor biomarker. However, the prognostic value of NNMT in solid tumors is still unclear. We therefore conducted a meta-analysis to investigate the association between NNMT expression and survival in patients with solid tumors.Methods: We focused on patients with solid tumors, using high NNMT expression levels as the intervention and low NNMT expression levels as the comparison, according to Patient, Intervention, Comparison, and Outcome (PICO) guidelines. Electronic databases (up to June 7, 2018) were comprehensively searched to collect relevant cohort studies regarding the associations between NNMT expression levels and survival outcomes (overall survival [OS], disease-specific survival [DSS] including cancer-specific survival [CSS], and time to tumor progression [TTP] including disease-free survival [DFS], progression-free survival [PFS], and metastasis-free survival [MeFS]). Publication biases were also examined. All analyses were performed using STATA 12.0 software.Results: A total of 3340 patients with solid tumors from nine published studies were included. The combined hazard ratio (HR) identified high NNMT expression levels as a poor prognostic predictor of OS (HR = 1.67, 95% CI = 1.23–2.26). However, NNMT levels had no significant association with DSS (HR = 1.47, 95% CI = 0.95–2.28) and TTP (HR = 1.13, 95%CI = 0.39–3.25).Conclusion: High NNMT expression levels may be a poor prognostic biomarker for patients with solid tumors

NeuroSim Simulator for Compute-in-Memory Hardware Accelerator: Validation and Benchmark

Compute-in-memory (CIM) is an attractive solution to process the extensive workloads of multiply-and-accumulate (MAC) operations in deep neural network (DNN) hardware accelerators. A simulator with options of various mainstream and emerging memory technologies, architectures, and networks can be a great convenience for fast early-stage design space exploration of CIM hardware accelerators. DNN+NeuroSim is an integrated benchmark framework supporting flexible and hierarchical CIM array design options from a device level, to a circuit level and up to an algorithm level. In this study, we validate and calibrate the prediction of NeuroSim against a 40-nm RRAM-based CIM macro post-layout simulations. First, the parameters of a memory device and CMOS transistor are extracted from the foundry’s process design kit (PDK) and employed in the NeuroSim settings; the peripheral modules and operating dataflow are also configured to be the same as the actual chip implementation. Next, the area, critical path, and energy consumption values from the SPICE simulations at the module level are compared with those from NeuroSim. Some adjustment factors are introduced to account for transistor sizing and wiring area in the layout, gate switching activity, post-layout performance drop, etc. We show that the prediction from NeuroSim is precise with chip-level error under 1% after the calibration. Finally, the system-level performance benchmark is conducted with various device technologies and compared with the results before the validation. The general conclusions stay the same after the validation, but the performance degrades slightly due to the post-layout calibration