Saliva is increasingly recognised as an attractive diagnostic fluid. The presence of various disease signalling salivary biomarkers that accurately reflect normal and disease states in humans and the sampling benefits compared to blood sampling are some of the reasons for this recognition. This explains the burgeoning research field in assay developments and technological advancements for the detection of various salivary biomarkers to improve clinical diagnosis, management, and treatment. This paper reviews the significance of salivary biomarkers for clinical diagnosis and therapeutic applications, with focus on the technologies and biosensing platforms that have been reported for screening these biomarkers.

The development of a human respiration carbon dioxide (CO₂) measurement device to evaluate cardiorespiratory status inside and outside a hospital setting has proven to be a challenging area of research over the few last decades. Hence, we report a real-time, user operable CO₂ measurement device using an infrared CO₂ sensor (Arduino Mega2560) and a thin film transistor (TFT, 3.5″), incorporated with low pass (cut-off frequency, 10 Hz) and moving average (span, 8) filters. The proposed device measures features such as partial end-tidal carbon dioxide (EtCO₂), respiratory rate (RR), inspired carbon dioxide (ICO₂), and a newly proposed feature—Hjorth activity—that annotates data with the date and time from a real-time clock, and is stored onto a secure digital (SD) card. Further, it was tested on 22 healthy subjects and the performance (reliability, validity and relationship) of each feature was established using (1) an intraclass correlation coefficient (ICC), (2) standard error measurement (SEM), (3) smallest detectable difference (SDD), (4) Bland–Altman plot, and (5) Pearson’s correlation (r). The SEM, SDD, and ICC values for inter- and intra-rater reliability were less than 5% and more than 0.8, respectively. Further, the Bland–Altman plot demonstrates that mean differences ± standard deviations for a set limit were 0.30 ± 0.77 mmHg, −0.34 ± 1.41 mmHg and 0.21 ± 0.64 breath per minute (bpm) for CO₂, EtCO₂ and RR. The findings revealed that the developed device is highly reliable, providing valid measurements for CO₂, EtCO₂, ICO₂ and RR, and can be used in clinical settings for cardiorespiratory assessment. This research also demonstrates that EtCO₂ and RR (r, −0.696) are negatively correlated while EtCO₂ and activity (r, 0.846) are positively correlated. Thus, simultaneous measurement of these features may possibly assist physicians in understanding the subject’s cardiopulmonary status. In future, the proposed device will be tested with asthmatic patients for use as an early screening tool outside a hospital setting.

In order to analyse surface electromyography (EMG) signals, it is necessary to extract the features based on a time or frequency domain. These approaches are based on the mathematical assumption of signal stationarity. Stationarity of EMG signals is thoroughly examined, especially in isotonic contractions. According to research, conflicting results have been identified depending on varying window sizes. Therefore, in this study, the authors endeavoured to determine the suitable window size to analyse EMG signals during isotonic contractions utilising stationary tests, reverse arrangement (RA), and modified reverse arrangement (MRA). There were slight differences in the average percentages of signal stationarity for RA and MRA tests in 100 ms, 500 ms, and 1000 ms window sizes. However, there was none for the 200 ms window size. On average, a window size of 200 ms provided stationary information with 88.57% of EMG signals compared to other window sizes. This study also recommended the MRA test to determine EMG signals stationarity for future studies, as the performances were better in comparison to RA tests. However, the following recommendation is only valid for window sizes greater than 200 ms. For a real-time application, the size of the analysis window together with the processing time should be less than 300 ms and a window size of 200 ms is applicable for isotonic contractions.

Gender recognition is trivial for a physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during exercise using a stepper. The relevant features were extracted and selected. The selected features were then fused to automatically predict gender recognition. However, the feature selection for gender classification became a challenge to ensure better accuracy. Thus, in this paper, a feature selection approach based on both the performance and the diversity between the two features from the rank-score characteristic (RSC) function in a combinatorial fusion approach (CFA) (Hsu et al.) was employed. Then, the features from the selected feature sets were fused using a CFA. The results were then compared with other fusion techniques such as naive bayes (NB), decision tree (J48), k-nearest neighbor (KNN) and support vector machine (SVM). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient and effective for feature selection. The fusion method was also able to improve the accuracy of the gender recognition rate. The CFA provides much better gender classification results which is 94.51% compared to Barani’s work (90.34%), Nazarloo’s work (92.50%), and other classifiers.