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2022 ◽  
Vol 15 ◽  
Author(s):  
Xiangxin Li ◽  
Yue Zheng ◽  
Yan Liu ◽  
Lan Tian ◽  
Peng Fang ◽  
...  

Surface electromyogram-based pattern recognition (sEMG-PR) has been considered as the most promising method to control multifunctional prostheses for decades. However, the commercial applications of sEMG-PR in prosthetic control is still limited due to the ambient noise and impedance variation between electrodes and skin surface. In order to reduce these issues, a force-myography-based pattern recognition method was proposed. In this method, a type of polymer-based flexible film sensors, the piezoelectrets, were used to record the rate of stress change (RSC) signals on the muscle surface of eight able-bodied subjects for six hand motions. Thirteen time domain features and four classification algorithms of linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM) were adopted to decode the RSC signals of different motion classes. In addition, the optimal feature set, classifier, and analysis window length were investigated systematically. Results showed that the average classification accuracy was 95.5 ± 2.2% by using the feature combination of root mean square (RMS) and waveform length (WL) for the classifier of KNN, and the analysis window length of 300 ms was found to obtain the best classification performance. Moreover, the robustness of the proposed method was investigated, and the classification accuracies were observed above 90% even when the white noise ratio increased to 50%. The work of this study demonstrated the effectiveness of RSC-based pattern recognition method for motion classification, and it would provide an alternative approach for the control of multifunctional prostheses.


2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Christopher Wing ◽  
Nicolas H. Hart ◽  
Callum McCaskie ◽  
Petar Djanis ◽  
Fadi Ma’ayah ◽  
...  

Abstract Background Australian Football is a fast paced, intermittent sport, played by both male and female populations. The aim of this systematic review was to compare male and female Australian Football players, competing at elite and sub-elite levels, for running performance during Australian Football matches based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Methods Medline, SPORTDiscus, and Web of Science searches, using search terms inclusive of Australian Football, movement demands and microsensor technology, returned 2535 potential manuscripts, of which 33 were included in the final analyses. Results Results indicated that male athletes performed approximately twice the total running distances of their female counterparts, which was likely due to the differences in quarter length (male elite = 20 min, female elite = 15 min (plus time-on). When expressed relative to playing time, the differences between males and females somewhat diminished. However, high-speed running distances covered at velocities > 14.4 km·h−1 (> 4 m·s−1) were substantially greater (≥ 50%) for male than female players. Male and female players recorded similar running intensities during peak periods of play of shorter duration (e.g., around 1 min), but when the analysis window was lengthened, females showed a greater decrement in running performance. Conclusion These results suggest that male players should be exposed to greater training volumes, whereas training intensities should be reasonably comparable across male and female athletes.


2021 ◽  
Vol 16 (11) ◽  
pp. T11008
Author(s):  
M.J. Lee ◽  
B.R. Ko ◽  
S. Ahn

Abstract A real-time Data Acquisition (DAQ) system for the CULTASK axion haloscope experiment was constructed and tested. The CULTASK is an experiment to search for cosmic axions using resonant cavities, to detect photons from axion conversion through the inverse Primakoff effect in a few GHz frequency range in a very high magnetic field and at an ultra low temperature. The constructed DAQ system utilizes a Field Programmable Gate Array (FPGA) for data processing and Fast Fourier Transformation. This design along with a custom Ethernet packet designed for real-time data transfer enables 100% DAQ efficiency, which is the key feature compared with a commercial spectrum analyzer. This DAQ system is optimally designed for RF signal detection in the axion experiment, with 100 Hz frequency resolution and 500 kHz analysis window. The noise level of the DAQ system averaged over 100,000 measurements is around -111.7 dBm. From a pseudo-data analysis, an improvement of the signal-to-noise ratio due to repeating and averaging the measurements using this real-time DAQ system was confirmed.


Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6460
Author(s):  
Marco Marini ◽  
Nicola Vanello ◽  
Luca Fanucci

Within the field of Automatic Speech Recognition (ASR) systems, facing impaired speech is a big challenge because standard approaches are ineffective in the presence of dysarthria. The first aim of our work is to confirm the effectiveness of a new speech analysis technique for speakers with dysarthria. This new approach exploits the fine-tuning of the size and shift parameters of the spectral analysis window used to compute the initial short-time Fourier transform, to improve the performance of a speaker-dependent ASR system. The second aim is to define if there exists a correlation among the speaker’s voice features and the optimal window and shift parameters that minimises the error of an ASR system, for that specific speaker. For our experiments, we used both impaired and unimpaired Italian speech. Specifically, we used 30 speakers with dysarthria from the IDEA database and 10 professional speakers from the CLIPS database. Both databases are freely available. The results confirm that, if a standard ASR system performs poorly with a speaker with dysarthria, it can be improved by using the new speech analysis. Otherwise, the new approach is ineffective in cases of unimpaired and low impaired speech. Furthermore, there exists a correlation between some speaker’s voice features and their optimal parameters.


PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257761
Author(s):  
Muhammad Abdul Hakim Muhamad ◽  
Rozaimi Che Hasan ◽  
Najhan Md Said ◽  
Jillian Lean-Sim Ooi

Integrating Multibeam Echosounder (MBES) data (bathymetry and backscatter) and underwater video technology allows scientists to study marine habitats. However, use of such data in modeling suitable seagrass habitats in Malaysian coastal waters is still limited. This study tested multiple spatial resolutions (1 and 50 m) and analysis window sizes (3 × 3, 9 × 9, and 21 × 21 cells) probably suitable for seagrass-habitat relationships in Redang Marine Park, Terengganu, Malaysia. A maximum entropy algorithm was applied, using 12 bathymetric and backscatter predictors to develop a total of 6 seagrass habitat suitability models. The results indicated that both fine and coarse spatial resolution datasets could produce models with high accuracy (>90%). However, the models derived from the coarser resolution dataset displayed inconsistent habitat suitability maps for different analysis window sizes. In contrast, habitat models derived from the fine resolution dataset exhibited similar habitat distribution patterns for three different analysis window sizes. Bathymetry was found to be the most influential predictor in all the models. The backscatter predictors, such as angular range analysis inversion parameters (characterization and grain size), gray-level co-occurrence texture predictors, and backscatter intensity levels, were more important for coarse resolution models. Areas of highest habitat suitability for seagrass were predicted to be in shallower (<20 m) waters and scattered between fringing reefs (east to south). Some fragmented, highly suitable habitats were also identified in the shallower (<20 m) areas in the northwest of the prediction models and scattered between fringing reefs. This study highlighted the importance of investigating the suitable spatial resolution and analysis window size of predictors from MBES for modeling suitable seagrass habitats. The findings provide important insight on the use of remote acoustic sonar data to study and map seagrass distribution in Malaysia coastal water.


2021 ◽  
pp. 1-46
Author(s):  
Hamid Karimi-Rouzbahani ◽  
Mozhgan Shahmohammadi ◽  
Ehsan Vahab ◽  
Saeed Setayeshi ◽  
Thomas Carlson

Abstract How does the human brain encode visual object categories? Our understanding of this has advanced substantially with the development of multivariate decoding analyses. However, conventional electroencephalography (EEG) decoding predominantly uses the mean neural activation within the analysis window to extract category information. Such temporal averaging overlooks the within-trial neural variability that is suggested to provide an additional channel for the encoding of information about the complexity and uncertainty of the sensory input. The richness of temporal variabilities, however, has not been systematically compared with the conventional mean activity. Here we compare the information content of 31 variability-sensitive features against the mean of activity, using three independent highly varied data sets. In whole-trial decoding, the classical event-related potential (ERP) components of P2a and P2b provided information comparable to those provided by original magnitude data (OMD) and wavelet coefficients (WC), the two most informative variability-sensitive features. In time-resolved decoding, the OMD and WC outperformed all the other features (including the mean), which were sensitive to limited and specific aspects of temporal variabilities, such as their phase or frequency. The information was more pronounced in the theta frequency band, previously suggested to support feedforward visual processing. We concluded that the brain might encode the information in multiple aspects of neural variabilities simultaneously such as phase, amplitude, and frequency rather than mean per se. In our active categorization data set, we found that more effective decoding of the neural codes corresponds to better prediction of behavioral performance. Therefore, the incorporation of temporal variabilities in time-resolved decoding can provide additional category information and improved prediction of behavior.


2021 ◽  
Vol 263 (4) ◽  
pp. 2555-2566
Author(s):  
Roland Sottek ◽  
Thiago Lobato

The Discrete Fourier Transform (DFT) is the standard technique for performing spectral analysis. It is used in the form of the well-known fast implementation (FFT) in almost all areas that deal with signal processing. However, the DFT algorithm has some limitations in terms of its resolution in time and frequency: the higher the time resolution, the lower the frequency resolution, and vice versa. The product of time (analysis duration) and analysis bandwidth (frequency resolution) is a constant. DFT results depend on the analysis window used (type and duration), although the physical signal properties do not change. The High-Resolution Spectral Analysis (HSA) method, published at the ASST '90, considers the window influence through spectral deconvolution and thus leads to a much lower time-bandwidth product, correlating better with human perception. Recently, variants of the HSA have been used for a psychoacoustic standard (roughness). Additionally, HSA is planned for a new model of fluctuation strength. This paper describes the improvements made to the HSA algorithm as well as its robustness against noise, and compares application results for both methods: HSA and DFT.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Joseph M. Fabian ◽  
Steven D. Wiederman

AbstractDragonflies visually detect prey and conspecifics, rapidly pursuing these targets via acrobatic flights. Over many decades, studies have investigated the elaborate neuronal circuits proposed to underlie this rapid behaviour. A subset of dragonfly visual neurons exhibit exquisite tuning to small, moving targets even when presented in cluttered backgrounds. In prior work, these neuronal responses were quantified by computing the rate of spikes fired during an analysis window of interest. However, neuronal systems can utilize a variety of neuronal coding principles to signal information, so a spike train’s information content is not necessarily encapsulated by spike rate alone. One example of this is burst coding, where neurons fire rapid bursts of spikes, followed by a period of inactivity. Here we show that the most studied target-detecting neuron in dragonflies, CSTMD1, responds to moving targets with a series of spike bursts. This spiking activity differs from those in other identified visual neurons in the dragonfly, indicative of different physiological mechanisms underlying CSTMD1’s spike generation. Burst codes present several advantages and disadvantages compared to other coding approaches. We propose functional implications of CSTMD1’s burst coding activity and show that spike bursts enhance the robustness of target-evoked responses.


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