OBJECTIVE: The present study introduces an approach for assessing athlete physical fitness in training environments: the Internet of Things (IoT) and CPS-based Physical Fitness Evaluation Method (IoT-CPS-PFEM).
METHODS: The IoT-CPS-PFEM employs a range of IoT-connected sensors and devices to observe and assess the physical fitness of athletes. The proposed methodology gathers information on diverse fitness parameters, including heart rate, body temperature, and oxygen saturation. It employs machine learning algorithms to scrutinize and furnish feedback on the athlete's physical fitness status.
RESULTS: The simulation findings illustrate the efficacy of the proposed IoT-CPS-PFEM in identifying the physical fitness levels of athletes, with an average precision of 93%. The method under consideration aims to tackle the existing obstacles of conventional physical fitness assessment techniques, including imprecisions, time lags, and manual data-gathering requirements. The approach of IoT-CPS-PFEM provides the benefits of real-time monitoring, precision, and automation, thereby enhancing an athlete's physical fitness and overall performance to a considerable extent.
CONCLUSION: The research findings suggest that the implementation of IoT-CPS-PFEM can significantly impact the physical fitness of athletes and enhance the performance of the Indian sports industry in global competitions.
OBJECTIVE: The chief aim of the study was to evaluate microbial retention on the salivary pellicle on treatment with oral rinses (CHX & EO)/PS (mimicking after meals use of mouth wash/PS).
METHODS: Noordini's Artifical Mouth model was used for developing the single species biofilm with early microbial colonizers of oral biofilm (A. viscosus, Strep. mitis and Strep. sanguinis respectively). The microbial retention on use of oral rinses comprising of CHX and EO as an active ingredients respectively was compared with Curcumin PS. For evaluating the microbial retention, the pellicle with microbial inoculation was developed on the glass beads in the mouth model. Subsequently the respective single specie biofilm was exposed to the mouth wash and PS after inoculation. It mimicked as use of mouth wash/PS after meals. The bacterial count in the dental biofilm was evaluated on serial dilution (CFU/ml). Sterile deionized water was used as a negative control. For qualitative analysis, Scanning electron microscope (SEM) was used to evaluate the microbial count.
RESULTS: From the data it was observed that for the treatment of single species experimental biofilm with commercially available mouth rinses (CHX & EO) and PS (curcumin), there was significant retention for S.mitis, S.sanguinis and A.viscosus. There was no significant difference observed between PS and CHX treated single species biofilm. Whereas a significant difference was observed between EO treated biofilms and CHX/PS treated biofilms (p⩽ 0.05).
CONCLUSION: It can be concluded from the results that curcumin PS and CHX should not be used after meals whereas EO containing mouth rinse can be used to maintain the oral mocroflora.
OBJECTIVE: This research aimed to verify the effects and progress of video-guided deep breathing (DB) integrated into CP through study on the changes of alpha waves and pain scale.
METHODS: Alpha waves were recorded using an electroencephalogram (EEG) and a visual analogue scale (VAS) to assess pain intensity before and after the intervention (6 weeks). Thirty CAP participants were recruited and randomly assigned to two groups: group A for video-guided DB integration into their CP and group B for CP. The effects of pre and post intervention were analyzed using a paired t-test with statistical significance set at p< 0.05.
RESULTS: Profound results from the research have shown that the participants who received both the DB+CP revealed a significant increase in alpha wave (p< 0.05) at occipital region.
CONCLUSION: The significant result reveals an increase in alpha waves in the occipital region after 6 weeks and indicates that the video-guided DB with a smartphone application is able to produce a change in CAP participants. This supports the DB integration to the CP for altering the pain perception.
OBJECTIVE: In this research, we investigated the correction between heart activation and the variations of walking paths.
METHOD: We employed Shannon entropy to analyze how the information content of walking paths affects the information content of HRV. Eight healthy students walked on three designed walking paths with different information contents while we recorded their ECG signals. We computed and analyzed the Shannon entropy of the R-R interval time series (as an indicator of HRV) versus the Shannon entropy of different walking paths and accordingly evaluated their relation.
RESULTS: According to the obtained results, walking on the path that contains more information leads to less information in the R-R time series.
CONCLUSION: The analysis method employed in this research can be extended to analyze the relation between other physiological signals (such as brain or muscle reactions) and the walking path.
OBJECTIVE: In this paper, we investigated the correlation between the brain and facial muscle activities by information-based analysis of electroencephalogram (EEG) signals and electromyogram (EMG) signals using Shannon entropy.
METHOD: The EEG and EMG signals of thirteen subjects were recorded during rest and auditory stimulations using relaxing, pop, and rock music. Accordingly, we calculated the Shannon entropy of these signals.
RESULTS: The results showed that rock music has a greater effect on the information of EEG and EMG signals than pop music, which itself has a greater effect than relaxing music. Furthermore, a strong correlation (r= 0.9980) was found between the variations of the information of EEG and EMG signals.
CONCLUSION: The activities of the facial muscle and brain are correlated in different conditions. This technique can be utilized to investigate the correlation between the activities of different organs versus brain activity in different situations.
OBJECTIVE: This paper analyzed the correlation between the brain and skin activities in rest and stimulations by information-based analysis of electroencephalogram (EEG) and galvanic skin resistance (GSR) signals.
METHODS: We recorded EEG and GSR signals of eleven subjects during rest and auditory stimulations using three pieces of music that were differentiated based on their complexity. Then, we calculated the Shannon entropy of these signals to quantify their information contents.
RESULTS: The results showed that music with greater complexity has a more significant effect on altering the information contents of EEG and GSR signals. We also found a strong correlation (r= 0.9682) among the variations of the information contents of EEG and GSR signals. Therefore, the activities of the skin and brain are correlated in different conditions.
CONCLUSION: This analysis technique can be utilized to evaluate the correlation among the activities of various organs versus brain activity in different conditions.
OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis.
METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18-22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked.
RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566).
CONCLUSION: We conclude that heart and brain activities are related.
OBJECTIVES: This study aims to develop a highly accurate, portable FMD and to demonstrate real-time monitoring of force applied by health professionals during JMT without altering its execution.
METHODS: The FMD was constructed using the FlexiForce sensor, potential divider, ATmega 328 microcontroller, custom-written software, and liquid crystal display. The calibration, accuracy, and cyclic repeatability of the FMD were tested from 0 to 90 N applied load with a gold standard universal testing machine. For practical demonstration, the FMD was tested for monitoring applied force by a physiotherapist while performing Maitland's grade I to IV over the 6th cervical vertebra among 30 healthy subjects.
RESULTS: The obtained Bland-Altman plot limits agreement for accuracy, and cyclic repeatability was -1.57 N to 1.22 N, and -1.26 N to 1.26 N, respectively with standard deviation and standard error of the mean values of 3.77% and 0.73% and 2.15% and 0.23%, respectively. The test-retest reliability of the FMD tested by the same researcher at an interval of one week showed an excellent intra-class correlation coefficient of r= 1.00. The obtained force readings for grade I to IV among 30 subjects ranged from 10.33 N to 45.24 N.
CONCLUSIONS: Appreciable performance of the developed FMD suggested that it may be useful to monitor force applied by clinicians during JMT among neck pain subjects and is a useful educational tool for academicians to teach mobilization skills.
OBJECTIVE: This paper presents a machine learning-based approach for the automatic classification of regular and irregular capnogram segments.
METHODS: Herein, we proposed four time- and two frequency-domain features experimented with the support vector machine classifier through ten-fold cross-validation. MATLAB simulation was conducted on 100 regular and 100 irregular 15 s capnogram segments. Analysis of variance was performed to investigate the significance of the proposed features. Pearson's correlation was utilized to select the relatively most substantial ones, namely variance and the area under normalized magnitude spectrum. Classification performance, using these features, was evaluated against two feature sets in which either time- or frequency-domain features only were employed.
RESULTS: Results showed a classification accuracy of 86.5%, which outperformed the other cases by an average of 5.5%. The achieved specificity, sensitivity, and precision were 84%, 89% and 86.51%, respectively. The average execution time for feature extraction and classification per segment is only 36 ms.
CONCLUSION: The proposed approach can be integrated with capnography devices for real-time capnogram-based respiratory assessment. However, further research is recommended to enhance the classification performance.
OBJECTIVE: In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain.
METHODS: Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content).
RESULTS: Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals.
CONCLUSION: This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.
OBJECTIVE: This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view.
METHODS: We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents.
RESULTS: According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.
OBJECTIVE: In this research, electroencephalography (EEG) as the feature of brain activity and voice signals were simultaneously analyzed.
METHOD: For this purpose, we changed the activity of the human brain by applying different odours and simultaneously recorded their voices and EEG signals while they read a text. For the analysis, we used the fractal theory that deals with the complexity of objects. The fractal dimension of EEG signal versus voice signal in different levels of brain activity were computed and analyzed.
RESULTS: The results indicate that the activity of human voice is related to brain activity, where the variations of the complexity of EEG signal are linked to the variations of the complexity of voice signal. In addition, the EEG and voice signal complexities are related to the molecular complexity of applied odours.
CONCLUSION: The employed method of analysis in this research can be widely applied to other physiological signals in order to relate the activities of different organs of human such as the heart to the activity of his brain.
OBJECTIVE: In order to address this issue, we analyzed how leg muscle activity is related to the variations of the path of movement.
METHOD: Since the electromyography (EMG) signal is a feature of muscle activity and the movement path has complex structures, we used entropy analysis in order to link their structures. The Shannon entropy of EMG signal and walking path are computed to relate their information content.
RESULTS: Based on the obtained results, walking on a path with greater information content causes greater information content in the EMG signal which is supported by statistical analysis results. This allowed us to analyze the relation between muscle activity and walking path.
CONCLUSION: The method of analysis employed in this research can be applied to investigate the relation between brain or heart reactions and walking path.
OBJECTIVE: In this research we benefit from fractal analysis to study the effect of complexity of path of movement on the complexity of human brain reaction.
METHODS: For this purpose we calculate the fractal dimension of the electroencephalography (EEG) signal when subjects walk on different paths with different fractal dimensions (complexity).
RESULTS: The results of the analysis show that the complexity of brain activity increases with the increment of complexity of path of movement.
CONCLUSION: The method of analysis employed in this research can also be employed to analyse the reaction of the human heart and respiration when subjects move on paths with different complexities.
METHODS: The proposed device measures and displays the FHR on a screen liquid crystal display (LCD). The device consists of hardware that comprises condenser microphone sensor, signal conditioning, microcontroller and LCD, and software that involves the algorithm used for processing the conditioned fetal heart signal prior to FHR display. The device's performance is validated based on analysis of variance (ANOVA) test.
RESULTS: FHR data was recorded from 22 pregnant women during the 17th to 37th week of gestation using the developed device and two standard devices; AngelSounds and Electronic Stethoscope. The results show that F-value (1.5) is less than F𝑐𝑟𝑖𝑡, (3.1) and p-value (p> 0.05). Accordingly, there is no significant difference between the mean readings of the developed and existing devices. Hence, the developed device can be used for monitoring FHR in clinical and home environments.