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Mobile, low-cost, and energy-aware operation of Artificial Intelligence (AI) computations in smart circuits and autonomous robots will play an important role in the next industrial leap in intelligent automation and assistive devices. Neuromorphic hardware with spiking neural network (SNN) architecture utilizes insights from biological phenomena to offer encouraging solutions. Previous studies have proposed reinforcement learning (RL) models for SNN responses in the rat hippocampus to an environment where rewards depend on the context. The scale of these models matches the scope and capacity of small embedded systems in the framework of Internet-of-Bodies (IoB), autonomous sensor nodes, and other edge applications. Addressing energy-efficient artificial learning problems in such systems enables smart micro-systems with edge intelligence. A novel bio-inspired RL system architecture is presented in this work, leading to significant energy consumption benefits without foregoing real-time autonomous processing and accuracy requirements of the context-dependent task. The hardware architecture successfully models features analogous to synaptic tagging, changes in the exploration schemes, synapse saturation, and spatially localized task-based activation observed in the brain. The design has been synthesized, simulated, and tested on Intel MAX10 Field-Programmable Gate Array (FPGA). The problem-based bio-inspired approach to SNN edge architectural design results in 25X reduction in average power compared to the state-of-the-art for a test with real-time context learning and 30 trials. Furthermore, 940x lower energy consumption is achieved due to improvement in the execution time.
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The use of over-the-counter analgesics (OTCA) has been found to alter various aspects of emotional processing and has been linked to increased anxiety and depression symptoms. Attentional bias is an aspect of emotional processing that is closely related to anxiety and depression. Although OTCA and attentional bias have both been linked to anxiety and depression, the potential links between OTCA usage and attentional bias are not yet investigated. The present study aimed to determine whether the frequency of OTCA usage is associated with differences in attentional bias by comparing response-based measures of attentional bias in 62 women aged 19-30â years. The findings showed that the small group reporting high OTCA usage demonstrated more orientation avoidance to fearful stimuli than those reporting no or low usage. Based on these preliminary findings, further research on attentional bias and its relationship to high OTCA usage is recommended.
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Brain-computer interface (BCI) systems include signal acquisition, preprocessing, feature extraction, classification, and an application phase. In fNIRS-BCI systems, deep learning (DL) algorithms play a crucial role in enhancing accuracy. Unlike traditional machine learning (ML) classifiers, DL algorithms eliminate the need for manual feature extraction. DL neural networks automatically extract hidden patterns/features within a dataset to classify the data. In this study, a hand-gripping (closing and opening) two-class motor activity dataset from twenty healthy participants is acquired, and an integrated contextual gate network (ICGN) algorithm (proposed) is applied to that dataset to enhance the classification accuracy. The proposed algorithm extracts the features from the filtered data and generates the patterns based on the information from the previous cells within the network. Accordingly, classification is performed based on the similar generated patterns within the dataset. The accuracy of the proposed algorithm is compared with the long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). The proposed ICGN algorithm yielded a classification accuracy of 91.23 ± 1.60%, which is significantly (p < 0.025) higher than the 84.89 ± 3.91 and 88.82 ± 1.96 achieved by LSTM and Bi-LSTM, respectively. An open access, three-class (right- and left-hand finger tapping and dominant foot tapping) dataset of 30 subjects is used to validate the proposed algorithm. The results show that ICGN can be efficiently used for the classification of two- and three-class problems in fNIRS-based BCI applications.
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Algoritmos , Interfaces Cérebro-Computador , Aprendizado Profundo , Redes Neurais de Computação , Espectroscopia de Luz Próxima ao Infravermelho , Humanos , Espectroscopia de Luz Próxima ao Infravermelho/métodos , Masculino , Adulto , Feminino , Adulto Jovem , Encéfalo/fisiologia , Encéfalo/diagnóstico por imagemRESUMO
In this study, we explore the potential of using functional near-infrared spectroscopy (fNIRS) signals in conjunction with modern machine-learning techniques to classify specific anatomical movements to increase the number of control commands for a possible fNIRS-based brain-computer interface (BCI) applications. The study focuses on novel individual finger-tapping, a well-known task in fNIRS and fMRI studies, but limited to left/right or few fingers. Twenty-four right-handed participants performed the individual finger-tapping task. Data were recorded by using sixteen sources and detectors placed over the motor cortex according to the 10-10 international system. The event's average oxygenated Δ HbO and deoxygenated Δ HbR hemoglobin data were utilized as features to assess the performance of diverse machine learning (ML) models in a challenging multi-class classification setting. These methods include LDA, QDA, MNLR, XGBoost, and RF. A new DL-based model named "Hemo-Net" has been proposed which consists of multiple parallel convolution layers with different filters to extract the features. This paper aims to explore the efficacy of using fNRIS along with ML/DL methods in a multi-class classification task. Complex models like RF, XGBoost, and Hemo-Net produce relatively higher test set accuracy when compared to LDA, MNLR, and QDA. Hemo-Net has depicted a superior performance achieving the highest test set accuracy of 76%, however, in this work, we do not aim at improving the accuracies of models rather we are interested in exploring if fNIRS has the neural signatures to help modern ML/DL methods in multi-class classification which can lead to applications like brain-computer interfaces. Multi-class classification of fine anatomical movements, such as individual finger movements, is difficult to classify with fNIRS data. Traditional ML models like MNLR and LDA show inferior performance compared to the ensemble-based methods of RF and XGBoost. DL-based method Hemo-Net outperforms all methods evaluated in this study and demonstrates a promising future for fNIRS-based BCI applications.
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This study seeks to explore the correlation between cortical activation and the Infinity Walk pattern, examining how the influence of foot overpronation and footwear may impact motor control. Functional near-infrared spectroscopy (fNIRS), a portable and user-friendly neuroimaging technique, was used to measure hemodynamical changes in six individuals with non-critical pronation degrees. Participants perform the Infinity Walk under various footwear conditions while wearing an fNIRS portable imaging device. Results indicate a consistent hemodynamic pattern in both hemispheres during the Infinity Walk, with no significant differences observed across subjects and footwear conditions in the prefrontal cortex (PFC), pre-motor area, the supplementary motor cortex (PMA & SMC), the primary motor cortex (PMC), and Wernicke's area (WA). The impact of pronation and footwear on motor control remains inconclusive due to inconsistent hemodynamic patterns. Notably, the activation patterns in Broca's area (BA) and the temporal gyrus (TG) differ significantly from other brain regions. The balanced hemodynamic responses in the bilateral hemispheres may be attributed to the Infinity Walk's inherent walking pattern. These findings indicate a need for further investigation into the Infinity Walk to examine the similarities and distinctions in activation patterns within specific brain regions. Additionally, the impact of pronation necessitates more substantial experimental research to establish a correlation between pronation and cortical hemodynamics.
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Gait and balance are an intricate interplay between the brain, nervous system, sensory organs, and musculoskeletal system. They are greatly influenced by the type of footwear, walking patterns, and surface. This exploratory study examines the effects of the Infinity Walk, pronation, and footwear conditions on brain effective connectivity patterns. A continuous-wave functional near-infrared spectroscopy device collected data from five healthy participants. A highly computationally efficient connectivity model based on the Grange causal relationship between the channels was applied to data to find the effective relationship between inter- and intra-hemispheric brain connectivity. Brain regions of interest (ROI) were less connected during the barefoot condition than during other complex walks. Conversely, the highest interconnectedness between ROI was observed while wearing flat insoles and medially wedged sandals, which is a relatively difficult type of footwear to walk in. No statistically significant (p-value <0.05) effect on connectivity patterns was observed during the corrected pronated posture. The regions designated as motoric, sensorimotor, and temporal became increasingly connected with difficult walking patterns and footwear conditions. The Infinity Walk causes effective bidirectional connections between ROI across all conditions and both hemispheres. Due to its repetitive pattern, the Infinity Walk is a good test method, particularly for neuro-rehabilitation and motoric learning experiments.
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Marcha , Caminhada , Humanos , Caminhada/fisiologia , Marcha/fisiologia , Encéfalo , Postura , Análise Espectral , SapatosRESUMO
Biodegradable magnesium-based implants offer mechanical properties similar to natural bone, making them advantageous over nonbiodegradable metallic implants. However, monitoring the interaction between magnesium and tissue over time without interference is difficult. A noninvasive method, optical near-infrared spectroscopy, can be used to monitor tissue's functional and structural properties. In this paper, we collected optical data from an in vitro cell culture medium and in vivo studies using a specialized optical probe. Spectroscopic data were acquired over two weeks to study the combined effect of biodegradable Mg-based implant disks on the cell culture medium in vivo. Principal component analysis (PCA) was used for data analysis. In the in vivo study, we evaluated the feasibility of using the near-infrared (NIR) spectra to understand physiological events in response to magnesium alloy implantation at specific time points (Day 0, 3, 7, and 14) after surgery. Our results show that the optical probe can detect variations in vivo from biological tissues of rats with biodegradable magnesium alloy "WE43" implants, and the analysis identified a trend in the optical data over two weeks. The primary challenge of in vivo data analysis is the complexity of the implant interaction near the interface with the biological medium.
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Ligas , Magnésio , Ratos , Animais , Magnésio/química , Ligas/química , Espectroscopia de Luz Próxima ao Infravermelho , Implantes Absorvíveis , Modelos Animais , Teste de MateriaisRESUMO
Maintaining body balance, whether static or dynamic, is critical in performing everyday activities and developing and optimizing basic motor skills. This study investigates how a professional alpine skier's brain activates on the contralateral side during a single-leg stance. Continuous-wave functional near-infrared spectroscopy (fNIRS) signals were recorded with sixteen sources and detectors over the motor cortex to investigate brain hemodynamics. Three different tasks were performed: barefooted walk (BFW), right-leg stance (RLS), and left-leg stance (LLS). The signal processing pipeline includes channel rejection, the conversation of raw intensities into hemoglobin concentration changes using modified Beer-Lambert law, baseline zero-adjustments, z-normalization, and temporal filtration. The hemodynamic brain signal was estimated using a general linear model with a 2-gamma function. Measured activations (t-values) with p-value <0.05 were only considered as statistically significant active channels. Compared to all other conditions, BFW has the lowest brain activation. LLS is associated with more contralateral brain activation than RLS. During LLS, higher brain activation was observed across all brain regions. The right hemisphere has comparatively more activated regions-of-interest. Higher ΔHbO demands in the dorsolateral prefrontal, pre-motor, supplementary motor cortex, and primary motor cortex were observed in the right hemisphere relative to the left which explains higher energy demands for balancing during LLS. Broca's temporal lobe was also activated during both LLS and RLS. Comparing the results with BFW- which is considered the most realistic walking condition-, it is concluded that higher demands of ΔHbO predict higher motor control demands for balancing. The participant struggled with balance during the LLS, showing higher ΔHbO in both hemispheres compared to two other conditions, which indicates the higher requirement for motor control to maintain balance. A post-physiotherapy exercise program is expected to improve balance during LLS, leading to fewer changes to ΔHbO.
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Magnesium (Mg) alloys possess unique properties that make them ideal for use as biodegradable implants in clinical applications. However, reports on the in vivo assessment of these alloys are insufficient. Thus, monitoring the degradation of Mg and its alloys in vivo is challenging due to the dynamic process of implant degradation and tissue regeneration. Most current works focus on structural remodeling, but functional assessment is crucial in providing information about physiological changes in tissues, which can be used as an early indicator of healing. Here, we report continuous wave near-infrared spectroscopy (CW NIRS), a non-invasive technique that is potentially helpful in assessing the implant-tissue dynamic interface in a rodent model. The purpose of this study was to investigate the effects on hemoglobin changes and tissue oxygen saturation (StO2) after the implantation of Mg-alloy (WE43) and titanium (Ti) implants in rats' femurs using a multiwavelength optical probe. Additionally, the effect of changes in the skin on these parameters was evaluated. Lastly, combining NIRS with photoacoustic (PA) imaging provides a more reliable assessment of tissue parameters, which is further correlated with principal component analysis.
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Implantes Absorvíveis , Espectroscopia de Luz Próxima ao Infravermelho , Ratos , Animais , Ligas , Magnésio , Análise de Componente PrincipalRESUMO
In high-speed wireless communication, visible light communication is considered an emerging and cutting-edge technology. A light-emitting diode can serve both as an illumination source in an environment and as a data transmitter. Nevertheless, plenty of complications stand in the way of developing VLC technology, including the low response time of waveguides and detectors and the field of view dependence of such devices. To cover those challenges, one approach is to develop a superior optical antenna that does not have a low response time related to phosphorescence materials and should also support concentrating light from the surroundings with a wide field of view. This research paper presents an optimized cylindrical optical antenna with benefits, such as affordable cost, fast response time due to high-efficient nanomaterials, and a wide field of view (FOV). The proposed structure avoids the need for intricate tracking systems and active pointing to the source, but it can also be integrated into portable devices. For the analysis of nanomaterials' characteristics, finite difference time domain simulations are used, and Monte-Carlo raytracing is used to study the proposed optical antenna. It was found that the antenna's optical efficiency varies from 1 to 29% depending on the size and the number of nanomaterials inside. Compared to other works, this paper shows higher efficiencies and wider FOV.
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The outbreak of the COVID-19 virus has faced the world with a new and dangerous challenge due to its contagious nature. Hence, developing sensory technologies to detect the coronavirus rapidly can provide a favorable condition for pandemic control of dangerous diseases. In between, because of the nanoscale size of this virus, there is a need for a good understanding of its optical behavior, which can give an extraordinary insight into the more efficient design of sensory devices. For the first time, this paper presents an optical modeling framework for a COVID-19 particle in the blood and extracts its optical characteristics based on numerical computations. To this end, a theoretical foundation of a COVID-19 particle is proposed based on the most recent experimental results available in the literature to simulate the optical behavior of the coronavirus under varying physical conditions. In order to obtain the optical properties of the COVID-19 model, the light reflectance by the structure is then simulated for different geometrical sizes, including the diameter of the COVID-19 particle and the size of the spikes surrounding it. It is found that the reflectance spectra are very sensitive to geometric changes of the coronavirus. Furthermore, the density of COVID-19 particles is investigated when the light is incident on different sides of the sample. Following this, we propose a nanosensor based on graphene, silicon, and gold nanodisks and demonstrate the functionality of the designed devices for detecting COVID-19 particles inside the blood samples. Indeed, the presented nanosensor design can be promoted as a practical procedure for creating nanoelectronic kits and wearable devices with considerable potential for fast virus detection.
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Visible light communication (VLC) is a versatile enabling technology for following high-speed wireless communication because of its broad unlicensed spectrum. In this perspective, white light-emitting diodes (LED) provide both illumination and data transmission simultaneously. To accomplish a VLC system, receiver antennas play a crucial role in receiving light signals and guiding them toward a photodetector to be converted into electrical signals. This paper demonstrates an optical receiver antenna based on luminescent solar concentrator (LSC) technology to exceed the conservation of etendue and reach a high signal-to-noise ratio. This optical antenna is compatible with all colors of LEDs and achieves an optical efficiency of 3.75%, which is considerably higher than the similar reported antenna. This antenna is fast due to the small attached photodetector-small enough that it can be adapted for electronic devices-which does not need any tracking system. Moreover, numerical simulation is performed using a Monte Carlo ray-tracing model, and results are extracted in the spectral domain. Finally, the fate of each photon and the chromaticity diagram of the collected photons' spectra are specified.
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A comprehensive study has been conducted on quantum dot reflective semiconductor optical amplifiers (QD-RSOAs) with optical pumps (OPs). Moreover, few studies have been completed on OP-based QD-RSOAs. A comparison is made between them and QD-RSOAs with electrical pumps (EPs) in this study. It is shown that the dynamical properties of the device can significantly develop in the optical pumping version. The optical properties are studied for both methods. Moreover, by solving the coupled differential rate and signal propagation equations, the operation of the device in the pulse mode is investigated. Finally, it is proven that OP QD-RSOAs can perform significantly better in applications such as fast all-optical signal processing and wavelength division multiplexing in passive optical networks.
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BACKGROUND: This study investigated the impact of COVID-19 restrictions on ambulatory activity and health-related quality of life (HR-QoL) in people with a lower limb amputation (LLA) in Norway. We hypothesized that the restrictions would negatively affect HR-QoL and decrease prosthetic wear time and ambulatory activity in participants with already limited mobility. METHODS: Twenty individuals with LLA (age and time since amputation 56.2 ± 11.9 and 22.3 ± 20.1 years, respectively) participated. Ambulatory activity (stepwatch: prosthetic wear time; steps per day; minutes of low-intensity (1-15 steps min-1), moderate-intensity (16-40 steps min-1), and high-intensity ambulation (>40 steps min-1)) and HR-QoL (EQ-5D-5L) data were collected prepandemic and 8-12 months later during pandemic restrictions. Semistructured interviews identified personal experiences of coping with restrictions. RESULTS: Prosthetic wear time decreased significantly (-1.0 ± 1.5 hours day-1, p < 0.05). Steps per day (440 ± 1481), moderate-intensity and high-intensity ambulation (3.7 ± 23.4, and 4.8 ± 13.9 minutes day-1, respectively), and EQ-5D-5L index (.02 ± .10) increased, whereas low-intensity ambulation decreased (-1.5 ± 16.1 minutes day-1), all nonsignificant changes. Qualitative analysis identified three themes related to coping with restrictions: (1) personal situation, (2) a prosthetic user's perspective, and (3) mindset. CONCLUSIONS: Increased time spent at home might explain the decreased prosthetic wear time. Contrary to the hypothesis, participants did not decrease their physical activity, and the declined low-intensity ambulation was offset by increased moderate-intensity and high-intensity ambulation. A positive mindset, intrinsic motivation, and health awareness may be important factors for maintaining ambulatory activity and HR-QoL in people with LLA.
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Membros Artificiais , COVID-19 , Adulto , Amputação Cirúrgica , Humanos , Extremidade Inferior/cirurgia , Qualidade de Vida , CaminhadaRESUMO
Magnesium (Mg)-based degradable alloys have attracted substantial attention for tissue engineering applications due to their biodegradability and potential for avoiding secondary removal surgeries. However, insufficient data in the existing literature regarding Mg's corrosion and gas formation after implantation have delayed its wide clinical application. Since the surface properties of degradable materials constantly change after contact with body fluid, monitoring the behaviour of Mg in phantoms or buffer solutions could provide some information about its physicochemical surface changes over time. Through surface analysis and spectroscopic analysis, we aimed to investigate the structural and functional properties of degradable disks. Since bubble formation may lead to inflammation and change pH, monitoring components related to acidosis near the cells is essential. To study the bubble formation in cell culture media, we used a newly developed Mg alloy (based on Mg, zinc, and calcium), pure Mg, and commercially available grade 2 Titanium (Ti) disks in Dulbecco's Modified Eagle Medium (DMEM) solution to observe their behaviour over ten days of immersion. Using surface analysis and the information from near-infrared spectroscopy (NIRS), we concluded on the conditions associated with the medical risks of Mg alloy disintegration. NIRS is used to investigate the degradation behaviour of Mg-based disks in the cell culture media, which is correlated with the surface analysis where possible.
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Ligas , Magnésio , Ligas/química , Corrosão , Magnésio/química , Teste de Materiais , Microscopia Eletrônica de Varredura , Espectrometria por Raios X , Propriedades de SuperfícieRESUMO
In this paper, a high-resolution full-color transparent monitor is designed and fabricated using the synthesized quantum dots for the first time. For this purpose, about 100 compounds that had the potential to emit blue, green, and red lights were selected, and simulation was performed using the discrete dipole approximation (DDA) method, in which the shell layer was selected to be SiO2 or TiO2 in the first step. Among the simulated compounds with SiO2 or TiO2 shells, Se/SiO2 and BTiO3/SiO2 were selected as blue light emitters with high intensity and narrow bandwidth. Accordingly, CdSe/SiO2 nanoparticles were selected as green light emitters and Au/TiO2 for the red light. As the surface of the nanoparticles in their optical properties is important, reactivation of the nanoparticles' surface is required to reach the high-intensity peak and resolution. To this end, in the second step, the surface of Se and CdSe nanoparticles reacted with ethanolamine, which can make a strong bond with cadmium atoms. The band structure and optical properties were obtained by the density functional theory (DFT) method. The Se/Ethanolamine and CdSe/Ethanolamine were experimentally synthesized to evaluate the theoretical results, and their optical properties were measured. To fabricate a transparent monitor, Se/Ethanolamine, CdSe/SiO2, and Au/TiO2 nanoparticles were dispersed in polyvinyl alcohol (PVA) solved in water and deposited on the glass by the doctor blading technique. Finally, high-resolution videos and images were displayed on the fabricated monitor.
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Background: Lower limb amputation (LLA) alters the sensorimotor control systems. Despite the self-reports of increased attention during mobility, the interaction between mobility and cognitive control mechanisms is not fully understood.Objective: Concurrently evaluate walking performance and prefrontal cortical (PFC) activity in persons with and without LLA during different walking conditions.Methods: Thirty-nine persons with LLA and thirty-three able-bodied controls participated. Walking performance was evaluated using the Figure-of 8-walk-test during three conditions: 1) UW (Usual walking with self-selected walking speed); 2) WCT (walking and carrying a tray with two cups filled with water); and 3) WUT (walking on uneven terrain). PFC activity was assessed using functional near-infrared spectroscopy (fNIRS). Linear mixed models were used to detect changes between groups and between walking conditions within each group.Results: Between-group comparisons showed increased PFC activity in persons with LLA during UW and WUT, and a significant decrease in walking performance during WCT and WUT compared to controls. Within-group comparisons showed increased PFC activity during WUT compared with UW and WCT and an overall difference in walking performance between the conditions (WU > WUT > WCT) in both groups. However, the effect of walking condition on PFC activity and walking performance was not modified by group (P > .1).Conclusion: The results suggest that persons with LLA have increased attentional demands during walking but choose the same cognitive-mobility strategy during challenging walking conditions as able-bodied persons. However, the attentional demands seem to depend on the complexity of the task.
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Amputação Cirúrgica , Caminhada , Marcha , Humanos , Extremidade Inferior , Córtex Pré-Frontal , Velocidade de CaminhadaRESUMO
In this article, we consider an emergent problem in the sensor fusion area in which unreliable sensors need to be identified in the absence of the ground truth. We devise a novel solution to the problem using the theory of replicator dynamics that require mild conditions compared to the available state-of-the-art approaches. The solution has a low computational complexity that is linear in terms of the number of involved sensors. We provide some sound theoretical results that catalog the convergence of our approach to a solution where we can clearly unveil the sensor type. Furthermore, we present some experimental results that demonstrate the convergence of our approach in concordance with our theoretical findings.
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Functional near-infrared spectroscopy (fNIRS) is a comparatively new noninvasive, portable, and easy-to-use brain imaging modality. However, complicated dexterous tasks such as individual finger-tapping, particularly using one hand, have been not investigated using fNIRS technology. Twenty-four healthy volunteers participated in the individual finger-tapping experiment. Data were acquired from the motor cortex using sixteen sources and sixteen detectors. In this preliminary study, we applied standard fNIRS data processing pipeline, i.e., optical densities conversation, signal processing, feature extraction, and classification algorithm implementation. Physiological and non-physiological noise is removed using 4th order band-pass Butter-worth and 3rd order Savitzky-Golay filters. Eight spatial statistical features were selected: signal-mean, peak, minimum, Skewness, Kurtosis, variance, median, and peak-to-peak form data of oxygenated haemoglobin changes. Sophisticated machine learning algorithms were applied, such as support vector machine (SVM), random forests (RF), decision trees (DT), AdaBoost, quadratic discriminant analysis (QDA), Artificial neural networks (ANN), k-nearest neighbors (kNN), and extreme gradient boosting (XGBoost). The average classification accuracies achieved were 0.75±0.04, 0.75±0.05, and 0.77±0.06 using k-nearest neighbors (kNN), Random forest (RF) and XGBoost, respectively. KNN, RF and XGBoost classifiers performed exceptionally well on such a high-class problem. The results need to be further investigated. In the future, a more in-depth analysis of the signal in both temporal and spatial domains will be conducted to investigate the underlying facts. The accuracies achieved are promising results and could open up a new research direction leading to enrichment of control commands generation for fNIRS-based brain-computer interface applications.
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Interfaces Cérebro-Computador , Espectroscopia de Luz Próxima ao Infravermelho , Análise Discriminante , Humanos , Movimento , Máquina de Vetores de SuporteRESUMO
BACKGROUND: Previous studies show that people with lower limb amputation (LLA) have a sedentary lifestyle, reduced walking capacity, and low cardiorespiratory fitness (VO2peak). There is, however, no knowledge on the relationship between cardiorespiratory fitness and objectively measured level of physical activity in daily life. OBJECTIVES: To investigate the relationship between upper-body VO2peak, physical activity levels, and walking capacity in persons with LLA. STUDY DESIGN: Correlational and descriptive study. METHODS: Fourteen participants with LLA performed an assessment of VO2peak on an arm-crank ergometer and walking capacity (preferred walking speed and 2-minute walking test). Level of physical activity was measured over 7 days with a step activity monitor (number of steps; sedentary time; and proportion of low-intensity, moderate-intensity, high-intensity, and peak-intensity activity level). RESULTS: VO2peak correlated significantly with number of steps per day (r = 0.696, p = 0.006), sedentary time (r = -0.618, p = 0.019), high-intensity activity level (r = 0.769, p = 0.001), and peak-intensity activity level (r = 0.674, p = 0.008). After correcting for age, correlations were still large and significant. Large correlations were also found between VO2peak, preferred walking speed (r = 0.586, p = 0.027), and 2-minute walking test (r = 0.649, p = 0.012). CONCLUSIONS: We provide the first evidence of the strong relationships between upper-body VO2peak, sedentary behavior, high-intensity activity level, and walking capacity in persons with LLA. Further research is needed to investigate the potential effect of upper-body cardiorespiratory fitness on the level of activity in daily life, or vice versa.