Interpretation Of Signals Research Articles

Potential of a New, Flexible Electrode sEMG System in Detecting Electromyographic Activation in Low Back Muscles during Clinical Tests: A Pilot Study on Wearables for Pain Management.

While low back pain (LBP) is the leading cause of disability worldwide, its clinical objective assessment is currently limited. Part of this syndrome arises from the abnormal sensorimotor control of back muscles, involving increased muscle fatigability (i.e., assessed with the Biering-Sorensen test) and abnormal muscle activation patterns (i.e., the flexion-extension test). Surface electromyography (sEMG) provides objective measures of muscle fatigue development (median frequency drop, MDF) and activation patterns (RMS amplitude change). This study therefore assessed the sensitivity and validity of a novel and flexible sEMG system (NSS) based on PEVA electrodes and potentially embeddable in textiles, as a tool for objective clinical LBP assessment. Twelve participants wearing NSS and a commercial laboratory sEMG system (CSS) performed two clinical tests used in LBP assessment (Biering-Sorensen and flexion-extension). Erector spinae muscle activity was recorded at T12-L1 and L4-L5. NSS showed sensitivity to sEMG changes associated with fatigue development and muscle activations during flexion-extension movements (p < 0.05) that were similar to CSS (p > 0.05). Raw signals showed moderate cross-correlations (MDF: 0.60-0.68; RMS: 0.53-0.62). Adding conductive gel to the PEVA electrodes did not influence sEMG signal interpretation (p > 0.05). This novel sEMG system is promising for assessing electrophysiological indicators of LBP during clinical tests.

Machine Learning and Graph Signal Processing Applied to Healthcare: A Review.

Signal processing is a very useful field of study in the interpretation of signals in many everyday applications. In the case of applications with time-varying signals, one possibility is to consider them as graphs, so graph theory arises, which extends classical methods to the non-Euclidean domain. In addition, machine learning techniques have been widely used in pattern recognition activities in a wide variety of tasks, including health sciences. The objective of this work is to identify and analyze the papers in the literature that address the use of machine learning applied to graph signal processing in health sciences. A search was performed in four databases (Science Direct, IEEE Xplore, ACM, and MDPI), using search strings to identify papers that are in the scope of this review. Finally, 45 papers were included in the analysis, the first being published in 2015, which indicates an emerging area. Among the gaps found, we can mention the need for better clinical interpretability of the results obtained in the papers, that is not to restrict the results or conclusions simply to performance metrics. In addition, a possible research direction is the use of new transforms. It is also important to make new public datasets available that can be used to train the models.

Very-high-energy γ-Ray Emission from Young Massive Star Clusters in the Large Magellanic Cloud

The Tarantula Nebula in the Large Magellanic Cloud is known for its high star formation activity. At its center lies the young massive star cluster R136, providing a significant amount of the energy that makes the nebula shine so brightly at many wavelengths. Recently, young massive star clusters have been suggested to also efficiently produce very high-energy cosmic rays, potentially beyond PeV energies. Here, we report the detection of very-high-energy γ-ray emission from the direction of R136 with the High Energy Stereoscopic System, achieved through a multicomponent, likelihood-based modeling of the data. This supports the hypothesis that R136 is indeed a very powerful cosmic-ray accelerator. Moreover, from the same analysis, we provide an updated measurement of the γ-ray emission from 30 Dor C, the only superbubble detected at TeV energies presently. The γ-ray luminosity above 0.5 TeV of both sources is (2–3) × 1035 erg s−1. This exceeds by more than a factor of 2 the luminosity of HESS J1646−458, which is associated with the most massive young star cluster in the Milky Way, Westerlund 1. Furthermore, the γ-ray emission from each source is extended with a significance of >3σ and a Gaussian width of about 30 pc. For 30 Dor C, a connection between the γ-ray emission and the nonthermal X-ray emission appears likely. Different interpretations of the γ-ray signal from R136 are discussed.

The usage of interference effects in single Guided Ultrasonic Wave time signals for damage identification

Guided Ultrasonic Waves (GUW) interact with structural inhomogenities in multiple ways, e.g. mode conversions, mode scattering, oscillation phase shifts and reflections. These effects do occur at specimen edges and at damage on and in the specimen’s area. These effects at structural boundaries make the signal interpretation more complex. On the other hand they can lead to additional information in recorded time signals when caused by damage. This study aims at the usage of interference effects in single time signals caused by damage in the structure. This is investigated changing the location of the measurements points in relation to the damage position. Two cases are taken into account: once the damage lies in the direct measurement patch between actuator and measurement point and once the damage lies outside the measurement path and its effect on the recorded time signal is investigated. In the experimental setup, a rectangular Aluminum plate is used. Its dimension is chosen to make sure that edge reflections do not superimpose with the regarded wave packages within the time signals. A Laser-Doppler-Vibrometer allows the non-contact recording of several measurement points and is not wavelength-dependent as e.g. PZT-transducers. This allows the measurement to be unaffected by the transfer behavior of the transducers. The relationship between the specific mode wavelength reflected by the damage and the measurement path length provides information about the expected level of interference at the considered measurement point. The anticipation includes the indirect identification of damage off the direct measurement path through the observed interference effects, as well as variations in interference caused by the shift of the measurement point locations. The amplitude and phase changes serve as a measure of the interference and are obtained using the Hilbert transform.

AI Agents in Intracortical Brain-Computer Interfaces: A Comprehensive Review and Proposed Integration Approach

Intracortical brain-computer interfaces (iBCIs) such as those demonstrated by Neuralink have shown significant potential in enabling direct communication between the human brain and external devices. However, the complexity and high dimensionality of neural data pose challenges in interpreting and translating brain activity into meaningful commands. This paper presents a comprehensive review of the current state of iBCIs, including advanced signal acquisition and decoding techniques, and explores the limitations of traditional approaches in achieving seamless brain-machine interaction. We propose a novel approach that leverages advanced AI agents, equipped with capabilities such as reflection, hierarchical planning, and decision-making, as an interface between the brain and iBCIs. By incorporating these advanced AI techniques, we aim to enhance the interpretation of neural signals, improve the efficiency of task execution, and provide a more intuitive and adaptable user experience to achieve goal-oriented outcomes from thoughts. The proposed approach is discussed in detail, highlighting its potential benefits and the challenges that need to be addressed. We conclude by outlining future research directions and the prospects of integrating advanced AI agents with iBCIs for various applications, including neurorehabilitation, assistive technologies, and human augmentation

The recognition and the initial reaction to children's signals after experiencing maltreatment during childhood

AbstractMaltreatment during childhood has many long‐lasting effects. Victims often become parents who experience parenting difficulties, therefore contributing to the intergenerational cycle of maltreatment. This paper highlights the consequences of severe trauma during childhood on two processes critical for a sensitive response, namely the recognition of and the initial reaction to emotional signals. We argue that most interventions focus on the observed parental response to children's behaviors or on parental interpretation of children's signals, that is, the attribution of meaning or intent to the signal. Interventions should better integrate research indicating that a history of childhood maltreatment could impair the capacity to correctly identify the emotion and the immediate reaction to these signals. By failing to acknowledge these long‐lasting consequences of childhood maltreatment, scholars and practitioners might never achieve true success in intervening with families in the hope of breaking the intergenerational cycle of maltreatment.

Spiking neural networks for physiological and speech signals: a review.

The integration of Spiking Neural Networks (SNNs) into the analysis and interpretation of physiological and speech signals has emerged as a groundbreaking approach, offering enhanced performance and deeper insights into the underlying biological processes. This review aims to summarize key advances, methodologies, and applications of SNNs within these domains, highlighting their unique ability to mimic the temporal dynamics and efficiency of the human brain. We dive into the core principles of SNNs, their neurobiological underpinnings, and the computational advantages they bring to signal processing, particularly in handling the temporal and spatial complexities inherent in physiological and speech data. Comparative analyses with conventional neural network models are presented to underscore the superior efficiency, lower power consumption, and higher temporal resolution of SNNs. The review further explores challenges and future prospects, highlighting the potential of SNNs to revolutionize wearable healthcare monitoring systems, neuroprosthetic devices, and natural language processing technologies. By providing a comprehensive overview of current strategies, this review aims to inspire innovative approaches in the field, fostering advances in real-time and energy-efficient processing of complex biological signals.

Direct (D)-Wave Monitoring Enhancement With Subdural Electrode Placement: A Case Series.

Direct-wave (D-wave) neuromonitoring is a direct measure of corticospinal tract integrity that detects potential injury during spinal cord surgery. Epidural placement of electrodes used for D-wave measurements can result in high electrical impedances resulting in substantial signal noise that can compromise signal interpretation. Subdural electrode placement may offer a solution. Medical records for consecutive patients with epidural and subdural D-wave monitoring were reviewed. Demographic and clinical information including preoperative and postoperative motor strength were recorded. Neuromonitoring charts were reviewed to characterize impedances and signal amplitudes of D-waves recorded epidurally (before durotomy) and subdurally (following durotomy). Nonparametric statistics were used to compare epidural and subdural D-waves. Ten patients (50% women, median age 50.5 years) were analyzed, of which five patients (50%) were functionally independent (modified McCormick grade ≤ II) preoperatively. D-waves were successfully acquired by subdural electrodes in eight cases and by epidural electrodes in three cases. Subdural electrode placement was associated with lower impedance values ( P = 0.011) and a higher baseline D-wave amplitude ( P = 0.007) relative to epidural placement. No association was observed between D-wave obtainability and functional status, and no adverse events relating to subdural electrode placement were encountered. Subdural electrode placement allows successful D-wave acquisition with accurate monitoring, clearer waveforms, and a more optimal signal-to-noise ratio relative to epidural placement. For spinal surgeries where access to the subdural compartment is technically safe and feasible, surgeons should consider subdural placement when monitoring D-waves to optimize clinical interpretation.

An Event Detection Mechanism with Deep Feature Extraction and Optimal Loss Function Based XGBoost Classifier

Purpose: Interoceptions are a combination of sensation, integration, and interpretation of internal bodily signals. Interoceptions are bidirectionally related to the human being mental and physiological health, and well-being. Sleep and different interoceptive modalities are proven to share common relations. Heartbeat Evoked Potential (HEP) is known as a robust readout to interoceptive processes. In this study, we focused on the relation between HEP modulations and sleep-related disorders. Materials and Methods: We investigated four different sleep-related disorders, including insomnia, rapid eye movement behavior disorder, periodic limb movements and nocturnal frontal lobe epilepsy, and provided HEP signals of multiple Electroencephalogram (EEG) channels over the right hemisphere to compare these disorders with the control group. Here, we investigated and compared the results of 35 subjects, including seven subjects for the control group and seven subjects for each of above-mentioned sleep disorders. Results: By comparing HEP responses of the control group with sleep-related patients’ groups, statistically significant HEP differences were detected over right hemisphere EEG channels, including FP2, F4, C4, P4, and O2 channels. These significant differences were also observed over the grand average HEP amplitude activity of channels over the right hemisphere in the sleep-related disorders as well. Conclusion: Our results between the control group and groups of patients suffering from sleep-related disorders demonstrated that during different stages of sleep, HEPs show significant differences over multiple right hemisphere EEG channels. Interestingly, by comparing different sleep disorders with each other, we observed that each of these HEP differences’ patterns over specific channels and during certain sleep stages bear considerable resemblances to each other.

Investigation of Heartbeat Evoked Potential (HEP) Response During Different Stages of Sleep in Sleep Disorders

Purpose: Interoceptions are a combination of sensation, integration, and interpretation of internal bodily signals. Interoceptions are bidirectionally related to the human being mental and physiological health, and well-being. Sleep and different interoceptive modalities are proven to share common relations. Heartbeat Evoked Potential (HEP) is known as a robust readout to interoceptive processes. In this study, we focused on the relation between HEP modulations and sleep-related disorders. Materials and Methods: We investigated four different sleep-related disorders, including insomnia, rapid eye movement behavior disorder, periodic limb movements and nocturnal frontal lobe epilepsy, and provided HEP signals of multiple Electroencephalogram (EEG) channels over the right hemisphere to compare these disorders with the control group. Here, we investigated and compared the results of 35 subjects, including seven subjects for the control group and seven subjects for each of above-mentioned sleep disorders. Results: By comparing HEP responses of the control group with sleep-related patients’ groups, statistically significant HEP differences were detected over right hemisphere EEG channels, including FP2, F4, C4, P4, and O2 channels. These significant differences were also observed over the grand average HEP amplitude activity of channels over the right hemisphere in the sleep-related disorders as well. Conclusion: Our results between the control group and groups of patients suffering from sleep-related disorders demonstrated that during different stages of sleep, HEPs show significant differences over multiple right hemisphere EEG channels. Interestingly, by comparing different sleep disorders with each other, we observed that each of these HEP differences’ patterns over specific channels and during certain sleep stages bear considerable resemblances to each other.

Studying Different Mechanisms of Seismo-to-Acoustic Coupling Using Ground Motion Local to Seismoacoustic Sensors

ABSTRACT The correlation between seismic and acoustic signals can be used to quantify the influence of earthquake ground motion on the output signals of infrasound sensors and thus evaluate the performance of infrasound sensors under field conditions. However, there have been relatively few studies, and earthquakes considered in those studies are large magnitude events. There remains a need to quantify correlation mechanisms at high frequencies using small earthquakes. In this study, we utilize a collocated seismoacoustic station BJT and a six-element infrasound array DQS to study different coupling mechanisms for infrasound sensors. From 2019 to 2021, 11 and 37 small magnitude earthquakes were detected by BJT and DQS, respectively. Seismoacoustic signals from these small magnitude earthquakes are used to compute the seismoacoustic spectral ratio and the observed spectral ratios are then compared with theoretical estimates. For local seismic-to-acoustic coupling (local infrasound), the highly local pressure field induced by high-frequency ground motion and the separation between seismoacoustic sensors reduce seismic and acoustic signal correlation, which prevents the use of small earthquakes in current studies. The seismic sensitivity of the infrasound sensor, MB3a is shown to be 30f0.75 for ground acceleration or 60πf1.75 for ground velocity and may be an important source of high-frequency noise for an infrasound station using this sensor. Furthermore, an empirical relation is developed to constrain the characteristics of earthquakes that can generate coupled signals on the infrasound sensor. Our study complements previous work and provides insight into the improved interpretation of infrasound signals and methods of seismoacoustic station calibration.

Applications of Ground-Penetrating Radar in Asteroid and Comet Exploration

Nowadays, asteroid and comet exploration is one of the most important components of deep space exploration. Through asteroid and comet exploration missions, it is possible to reveal the history of the formation and evolution of the solar system, to understand the origin and evolution of the planets, and to improve scientific models and instruments. As a payload with the advantages of non-destructive, penetrating, and polarizing characteristics, ground-penetrating radar (GPR) has been widely used in lunar and Mars exploration, and will play an important role in planned asteroid and comet exploration missions. In this study, statistics on asteroid and comet exploration missions, scientific results, and space-based ground-penetrating radar (SB-GPR) utilization are presented for the three phases to date. According to the statistics, SB-GPR will play an important role in future Phase 2 and 3 missions. The focus of this study is on analyzing the mission flow, SB-GPR parameters, scientific objectives, and scientific results of the missions that have carried SB-GPR and those that are planned to carry SB-GPR, including the Hera, Rosetta, Castalia, and Tianwen-2 missions. On this basis, the development trends of asteroid and comet exploration missions, as well as the future development trends of SB-GPR design and signal interpretation, are discussed.

Residual and bidirectional LSTM for epileptic seizure detection.

Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming. To tackle this open challenge, we introduce a straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, for detecting epileptic seizures using EEG signals. Firstly, a one-dimensional residual neural network (ResNet) is tailored to adeptly extract the local spatial features of EEG signals. Subsequently, the acquired features are input into a bidirectional long short-term memory (BiLSTM) layer to model temporal dependencies. These output features are further processed through two fully connected layers to achieve the final epileptic seizure detection. The performance of ResBiLSTM is assessed on the epileptic seizure datasets provided by the University of Bonn and Temple University Hospital (TUH). The ResBiLSTM model achieves epileptic seizure detection accuracy rates of 98.88-100% in binary and ternary classifications on the Bonn dataset. Experimental outcomes for seizure recognition across seven epilepsy seizure types on the TUH seizure corpus (TUSZ) dataset indicate that the ResBiLSTM model attains a classification accuracy of 95.03% and a weighted F1 score of 95.03% with 10-fold cross-validation. These findings illustrate that ResBiLSTM outperforms several recent deep learning state-of-the-art approaches.

AI-enhanced EEG signal interpretation: A novel approach using texture analysis with random forests

We hypothesize that the Gray-Level Co-occurrence Matrix (GLCM) and the Run-Length Matrix (RLM) techniques can effectively quantify discrete changes in EEG signals, and that the features extracted from these matrices can be utilized to train a Random Forest (RF) model. Our contribution includes the development of a robust code in sci-kit learn for a hypothetical model that, after adequate training and testing, could be used to detect and remove artifacts as well as differentiate between physiological and pathological EEG signals. Moreover, our approach envisions the RF model as a powerful tool capable of differentiating between normal and abnormal EEG signals. This approach could lead to the development of more potent AI tools that enhance clinical decision-making in neurology and psychiatry.

Diffusion Analysis of Intracranial Epidermoid, Head and Neck Epidermal Inclusion Cyst, and Temporal Bone Cholesteatoma.

Intracranial epidermoid tumors, temporal bone cholesteatomas, and head and neck epidermoid inclusion cysts are typically slow-growing, benign conditions arising from ectodermal tissue. They exhibit increased signal on DWI. While much of the imaging literature describes these lesions as showing diffusion restriction, we aimed to investigate these qualitative signal intensities and interpretations of restricted diffusion with respect to normal brain structures. This study aimed to quantitatively evaluate the ADC values and histogram features of these lesions. This retrospective study included children with histologically confirmed diagnoses of intracranial epidermoid tumors, temporal bone cholesteatomas, or head and neck epidermoid inclusion cysts. Lesions were segmented, and voxelwise calculation of ADC values was performed along with histogram analysis. ADC calculations were validated with a second analysis software to ensure accuracy. Normal brain ROIs-including the cerebellum, white matter, and thalamus-served as normal comparators. Correlational analysis and Bland-Altman plots assessed agreement among software tools for ADC calculations. Differences in the distribution of values between the lesions and normal brain tissues were assessed using the Wilcoxon rank sum and Kruskal-Wallis tests. Forty-eight pathology-proved cases were included in this study. Among them, 13 (27.1%) patients had intracranial epidermoid tumors, 14 (29.2%) had head and neck epidermoid inclusion cysts, and 21 (43.7%) had temporal bone cholesteatomas. The mean age was 8.67 (SD, 5.30) years, and 27 (52.9%) were female. The intraclass correlation for absolute agreement for lesional ADC between the 2 software tools was 0.997 (95% CI, 0.995-0.998). The intracranial epidermoid tumor, head and neck epidermoid inclusion cyst, and temporal bone cholesteatoma median ADC values were not significantly different (973.7 versus 875.7 versus 933.2 ×10-6 mm2/s, P = .265). However, the ADCs of the 3 types of lesions were higher than those of 3 normal brain tissue types (933 versus 766, × 10-6 mm2/s, P < .0001). The ADC values of intracranial epidermoid tumors, temporal bone cholesteatomas, and head and neck epidermoid inclusion cysts are higher than those of normal brain regions. It is not accurate to simply classify these lesions as exhibiting restricted diffusion or reduced diffusivity without considering the tissue used for comparison. The observed hyperintensity on DWI compared with the brain is likely attributable to a relatively higher contribution of the T2 shinethrough effect.

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens with known, unchangeable defect properties, which are usually complicated to fabricate. It consist of a shape memory polymer foil with temperature-dependent Young’s modulus and ultrasound attenuation. This open a possibility to generate a reconfigurable defect by projecting a heating laser in the form of a short line on the specimen surface. Ultrasound is generated by a laser pulse at one fixed position and detected by a laser vibrometer at another fixed position for 64 different defect positions and 3 different configurations of the specimen. The obtained diversified datasets are used to optimize the neural network architecture for the interpretation of ultrasound signals. We study the performance of the model in cases of reduced and dissimilar training datasets. In our first study, we classify the specimen configurations with the defect position being the disturbing parameter. The model shows high performance on a dataset of signals obtained at all the defect positions, even if trained on a completely different dataset containing signals obtained at only few defect positions. In our second study, we perform precise defect localization. The model becomes robust to the changes in the specimen configuration when a reduced dataset, containing signals obtained at two different specimen configurations, is used for the training process. This work highlights the potential of the demonstrated machine learning algorithm for industrial quality control. High-volume products (simulated by a reconfigurable specimen in our work) can be rapidly tested on the production line using this single-point and contact-free laser ultrasonic method.

Quantitative analysis and machine learning-based interpretation of EEG signals in coma and brain-death diagnosis

Quantitative analysis and machine learning-based interpretation of EEG signals in coma and brain-death diagnosis

Detection and interpretation of the time-varying seasonal signals in China with multi-geodetic measurements

The time-varying periodic variations in Global Navigation Satellite System (GNSS) stations affect the reliable time series analysis and appropriate geophysical interpretation. In this study, we apply the singular spectrum analysis (SSA) method to characterize and interpret the periodic patterns of GNSS deformations in China using multiple geodetic datasets. These include 23-year observations from the Crustal Movement Observation Network of China (CMONOC), displacements inferred from the Gravity Recovery and Climate Experiment (GRACE), and loadings derived from Geophysical models (GM). The results reveal that all CMONOC time series exhibit seasonal signals characterized by amplitude and phase modulations, and the SSA method outperforms the traditional least squares fitting (LSF) method in extracting and interpreting the time-varying seasonal signals from the original time series. The decrease in the root mean square (RMS) correlates well with the annual cycle variance estimated by the SSA method, and the average reduction in noise amplitudes is nearly twice as much for SSA filtered results compared with those from the LSF method. With SSA analysis, the time-varying seasonal signals for all the selected stations can be identified in the reconstructed components corresponding to the first ten eigenvalues. Moreover, both RMS reduction and correlation analysis imply the advantages of GRACE solutions in explaining the GNSS periodic variations, and the geophysical effects can account for 71% of the GNSS annual amplitudes, and the average RMS reduction is 15%. The SSA method has proved to be useful for investigating the GNSS time-varying seasonal signals. It could be applicable as an auxiliary tool in the improvement of non-linear variations investigations.

Collapsing domain wall networks: impact on pulsar timing arrays and primordial black holes

Unstable domain wall (DW) networks in the early universe are cosmologically viable and can emit a large amount of gravitational waves (GW) before annihilating. As such, they provide an interpretation for the recent signal reported by Pulsar Timing Array (PTA) collaborations. A related important question is whether such a scenario also leads to significant production of Primordial Black Holes (PBH).We investigate both GW and PBH production using 3D numerical simulations in an expanding background, with box sizes up to N = 3240,including the annihilation phase. We find that:i) the network decays exponentially, i.e. the false vacuum volume drops as ∼ exp(-η 3), with η the conformal time;ii) the GW spectrum is larger than traditional estimates by more than one order of magnitude, due to a delay between DW annihilation and the sourcing of GWs. We then present a novel semi-analytical method to estimate the PBH abundances: rare false vacuum pockets of super-Hubble size collapse to PBHs if their energy density becomes comparable to the background when they cross the Hubble scale. Smaller (but more abundant) pockets will instead collapse only if they are close to spherical. This introduces very large uncertainties in the final PBH abundance. The first phenomenological implication is that the DW interpretation of the PTA signal is compatible with observational constraints on PBHs, within the uncertainties. Second, in a different parameter region, the dark matter can be entirely in the form of asteroid-mass PBHs from the DW collapse. Remarkably, this would also lead to a GW background in the observable range of LIGO-Virgo-KAGRA and future interferometers, such as LISA and Einstein Telescope.

A Numerical Model for Analyzing Mechanical Slippage Effect on Crosswell Distributed Fiber-Optic Strain Measurements During Fracturing

Summary Distributed fiber-optic (FO) strain measurements have become a widely used method for monitoring and diagnosing far-field strain induced during hydraulic fracturing (HF). However, field observations have revealed that the mechanical decoupling between the fiber and the rock often happens. Despite this phenomenon, much of the current research assumes that the fiber cable remains mechanically coupled to the rock formation. To improve the accuracy and reliability of field data interpretation, it is essential to better understand the effect of fiber decoupling on strain responses. The objective of this study is to simulate strain responses while taking into account the impact of fiber slippage. In this study, we developed a numerical model that allows for accurate prediction and analysis of strain responses of crosswell distributed FO experiencing mechanical decoupling. Our model first uses the 3D displacement discontinuity method to calculate displacement fields induced by hydraulic fractures. The model then accounts for the occurrence of slippage and calculates the fiber displacement distribution with slippage effect. Additionally, the model can predict fiber strain distribution in cases where the stick-slip behavior is present. To better understand the impact of friction on strain and strain-rate signals, we conduct a sensitivity analysis. This allows us to explore the effects of different friction levels on the signals, which can be critical in achieving more accurate and reliable results. Our model results reveal several key observations. First, when fiber slippage is detected, we typically observe a broadening of the extension zone near the fracture on the strain/strain rate waterfall plots, which cannot provide a well-defined fracture domain corridor. The simulated strain rate pattern matches the field observation. Additionally, the size of the decoupling zone increases as the friction between the fiber and formation decreases. Also, the decoupling generates negative cumulative strain change after a certain period of closure. Furthermore, when the dynamic friction is smaller than the static friction, sudden strain releases appearing as sticking portions were visible in the strain/strain-rate waterfall plots, indicating the occurrence of stick-slip behavior in the fiber. We also observed that the sudden strain releases in the strain rate waterfall plot occur more frequently when the maximum fiber strain gradient is smaller. Moreover, the longer sticking portion was observed when the dynamic/static friction ratio was smaller. The key innovation of this study is the development of a forward numerical model that simulates strain responses while taking into account the coupling effect between fiber and formation. To the best of our knowledge, this is the first time that such a model has been presented. Overall, our study provides an important contribution to the field of HF monitoring by enabling a more accurate and reliable interpretation of strain signals, which can ultimately improve the safety and effectiveness of HF operations.