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Passive acoustics are widely used to monitor the presence of dolphins in the marine environment. This study aims to introduce a low-cost and homemade approach for assembling a complete underwater microphone (i.e., the hydrophone), employing cheap and easy to obtain components. The hydrophone was assembled with two piezo disks connected in a balanced configuration and encased in a plastic container filled with plastic foam. The hydrophone's performance was validated by direct comparison with the commercially available AS-1 hydrophone (Aquarian Hydrophones, Anacortes, U.S.) on different underwater acoustic signals: artificial acoustic signals (ramp and multitone signals) and various dolphin vocalizations (whistle, echolocation clicks, and burst pulse signals). The sensitivity of the device's performance to changes in the emission source position was also tested. The results of the validation procedure on both artificial signals and real dolphin vocalizations showed that the significant cost savings associated with cheap technology had a minimal effect on the recording device's performance within the frequency range of 0–35 kHz. At this stage of experimentation, the global cost of the hydrophone could be estimated at a few euros, making it extremely price competitive when compared to more expensive commercially available models. In the future, this effective and low-cost technology would allow for continuous monitoring of the presence of free-ranging dolphins, significantly lowering the total cost of autonomous monitoring systems. This would permit broadening the monitored areas and creating a network of recorders, thus improving the acquisition of data. [ABSTRACT FROM AUTHOR]

Background: Muscle co-contraction plays a significant role in motion control. Available detection methods typically only provide information in the time domain. The current investigation proposed a novel approach for muscle co-contraction detection in the time–frequency domain, based on continuous wavelet transform (CWT). Methods: In the current study, the CWT-based cross-energy localization of two surface electromyographic (sEMG) signals in the time–frequency domain, i.e., the CWT coscalogram, was adopted for the first time to characterize muscular co-contraction activity. A CWT-based denoising procedure was applied for removing noise from the sEMG signals. Algorithm performances were checked on synthetic and real sEMG signals, stratified for signal-to-noise ratio (SNR), and then validated against an approach based on the acknowledged double-threshold statistical algorithm (DT). Results: The CWT approach provided an accurate prediction of co-contraction timing in simulated and real datasets, minimally affected by SNR variability. The novel contribution consisted of providing the frequency values of each muscle co-contraction detected in the time domain, allowing us to reveal a wide variability in the frequency content between subjects and within stride. Conclusions: The CWT approach represents a relevant improvement over state-of-the-art approaches that provide only a numerical co-contraction index or, at best, dynamic information in the time domain. The robustness of the methodology and the physiological reliability of the experimental results support the suitability of this approach for clinical applications. [ABSTRACT FROM AUTHOR]

Background: Muscular-activity timing is useful information that is extractable from surface EMG signals (sEMG). However, a reference method is not available yet. The aim of this study is to investigate the reliability of a novel machine-learning-based approach (DEMANN) in detecting the onset/offset timing of muscle activation from sEMG signals. Methods: A dataset of 2880 simulated sEMG signals, stratified for signal-to-noise ratio (SNR) and time support, was generated to train a hidden single-layer fully-connected neural network. DEMANN's performance was evaluated on simulated sEMG signals and two different datasets of real sEMG signals. DEMANN was validated against different reference algorithms, including the acknowledged double-threshold statistical algorithm (DT). Results: DEMANN provided a reliable prediction of muscle onset/offset in simulated and real sEMG signals, being minimally affected by SNR variability. When directly compared with state-of-the-art algorithms, DEMANN introduced relevant improvements in prediction performances. Conclusions: These outcomes support DEMANN's reliability in assessing onset/offset events in different motor tasks and the condition of signal quality (different SNR), improving reference-algorithm performances. Unlike other works, DEMANN's adopts a machine learning approach where a neural network is trained by only simulated sEMG signals, avoiding the possible complications and costs associated with a typical experimental procedure, making this approach suitable to clinical practice. [ABSTRACT FROM AUTHOR]