A New Threshold Estimation Method of SEMG Wavelet De-noising for Prolonged Fatigue Identification

In sports training, fatigue management is very important to avoid muscle injury and chronic fatigue syndrome. It occurs due to untreated normal fatigue (NF) which leads to a higher level of fatigue. This paper refers to the higher level of fatigue condition as prolonged fatigue (PF). Fatigue can be identified based on musculoskeletal, physiological, psychological, immunological and endocrinal system condition. Presently, musculoskeletal or muscle condition can be accessed quantitatively based on an invasive technique known as biopsies. It is accurate to evaluate muscle condition, but not suitable for frequent measurements. The non-invasive method used through self-evaluation tools such as questionnaire is not a quantitative measurement and sometimes is difficult to quantify. There are other non-invasive methods to evaluate muscle condition known as surface electromyography (SEMG). Before this, SEMG were only widely used to classify between non-fatigue and NF conditions. However, NF prediction might not be accurate and suitable to be used on athletes since they are required to undergo high-intensive training every day and this involves PF condition. Recently, SEMG signals characteristics found out to be able to evaluate ionic concentration changes in the muscle due to intensive training. The signals characteristics are different compared to NF identification. Therefore, high quality SEMG plays an important role in PF detection to avoid misinterpretation. In this study, twenty healthy participants were recruited and performed five consecutive days of intensive training to induce PF symptoms. The training was based on Bruce Protocol treadmill test and SEMG data were collected from the participants’ rectus femoris muscle. This paper pre-processed SEMG signals using Stationary Wavelet Transform (SWT) ‘db’ 45 with different threshold estimation techniques of de-noising such as RigRSURE, HeurSURE, minimax, universal threshold and a new estimation of threshold method. The new method able to overcome the limitation of conventional methods which estimate threshold based on statistical principles. Most importantly, the new method can preserve significant SEMG information, remove corner frequency and mitigate baseline noises. The performance of conventional and propose methods can then be evaluated based on PF classification performance. The de-noised signals extracted based on time, frequency and time-frequency features. Naïve Bayes classification results using time and frequency features indicate that the new estimation of threshold method with time and frequency features have the highest accuracy (98%), compared to RigRSURE (85%), HuerSURE (68%), Universal Threshold (74%) and minimax (76%) in PF identification

Field-dependent viscoelastic properties of graphite-based magnetorheological grease

This paper highlights the effect of graphite on the dynamic viscoelastic properties of magnetorheological grease (MRG). Two types of MRG namely MRG and graphite-MRG, GMRG with 0 wt.% and 10 wt. % of graphite respectively was synthesized by using a mechanical stirrer. The rheological properties of both sample at various magnetic field strength from 0 to 0.603 T was analyzed via rheometer under oscillatory mode with strain ranging from 0.001 to 1% with fixed frequency at 1 Hz for strain sweep and frequency ranging from 0.1 to 80 Hz at a constant strain of 0.01 % for frequency sweep. Based on the result obtained, the value of storage and loss modulus are dependent on the graphite content. A high value of storage modulus was achieved in the GMRG sample at all applied magnetic field strengths within all frequency ranges. These phenomena related to the contribution of graphite to forming the chain structure with CIPs and offered a more stable and stronger structure as compared with MRG. Moreover, the reduction in the value of loss modulus in GMRG was noticed compared to MRG at on-state conditions reflected by the stable structure obtained by GMRG. Lastly, both samples displayed a strong solid-like (elastic) behavior due to the high value of storage modulus, G’ acquired compared to loss modulus, G’’ at all frequency ranges. Therefore, the utilization of graphite in MRG can be used in wide applications such as brake and seismic dampers

Biomarkers of systemic inflammation and depression and fatigue in moderate clinically stable COPD

<p>Abstract</p> <p>Introduction</p> <p>COPD is an inflammatory disease with major co-morbidities. It has recently been suggested that depression may be the result of systemic inflammation. We aimed to explore the association between systemic inflammation and symptoms of depression and fatigue in patients with mainly moderate and clinically stable COPD using a range of inflammatory biomarkers, 2 depression and 2 fatigue scales.</p> <p>Method</p> <p>We assessed 120 patients with moderate COPD (FEV₁% 52, men 62%, age 66). Depression was assessed using the BASDEC and CES-D scales. Fatigue was assessed using the Manchester COPD-fatigue scale (MCFS) and the Borg scale before and after 6MWT. We measured systemic TNF-α, CRP, TNF-α-R1, TNF-α-R2 and IL-6.</p> <p>Results</p> <p>A multivariate linear model of all biomarkers showed that TNF-α only had a positive correlation with BASDEC depression score (p = 0.007). TNF-α remained positively correlated with depression (p = 0.024) after further adjusting for TNF-α-R1, TNF-α-R2, 6MWD, FEV₁%, and pack-years. Even after adding the MCFS score, body mass and body composition to the model TNF-α was still associated with the BASDEC score (p = 0.044). Furthermore, patients with higher TNF-α level (> 3 pg/ml, n = 7) had higher mean CES-D depression score than the rest of the sample (p = 0.03). Borg fatigue score at baseline were weakly correlated with TNF-α and CRP, and with TNF-α only after 6MWT. Patients with higher TNF-α had more fatigue after 6MWD (p = 0.054).</p> <p>Conclusion</p> <p>This study indicates a possible association between TNF-α and two frequent and major co-morbidities in COPD; i.e., depression and fatigue.</p

Pulmonary Gas Transplant Time

The experimental evidence presented by Wagner et al. (1) does not imply that there is an interaction between the bulk flow and diffusion. On the contrary, the C-shaped curve in Fig. 1 suggests that the mechanism of transport is by convection alone [a more detailed discussion of convective dispersion is given in (2)]. The fact that the shortest transit time is about half that obtained from a calculation based upon the average velocity would also result if the velocity profile in the ducts are nearly parabolic; when the Reynolds number of the flow is less than about 2100, the velocity profile is nearly parabolic and the maximum velocity within the duct is nearly twice the average velocity. Furthermore, because Taylor diffusion (3) involves only radial diffusion, the minimum transit time does not change; that is, the minimum transit time remains equal to the distance between the larynx and the alveolus, divided by the velocity of the fastest streamline

Relation between Indoor and Outdoor Exposure to Fine Particles Near a Busy Arterial Road

Various studies on indoor and outdoor particulate matter in the urban environment in the vicinity of busy arterial roads in the centre of the subtropical city of Brisbane have indicated that the revised United States Environmental Protection agency National Ambient air Quality Standards for Particulate matter PM2.5 could be exceeded not only outdoors but also indoors. The aim of this work was to investigate outdoor exposure to submicrometer particles and their relationship with indoor exposure in a hypothetical office building located in the vicinity of a busy arterial road. The outdoor exposure values and trends were measured in terms of particle number in the submicrometer size range and were then recalculated to represent mass concentration trends. The results of this study indicate that exposure to PM0.7 particles in ambient air close to a busy road often exceeds the levels of the annual and 24-hour US EPA NAAQS PM2.5 standards. It is likely that exposure to PM2.5 is even higher, and may significantly exceed these standards