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Objective: To investigate the effect of integrated approach of yoga therapy (IAYT) intervention in individual with knee Osteoarthritis.Design: Randomized controlled clincial trail.Participants: Sixty-six individual prediagnosed with knee osteoarthritis aged between 30 and 75 years were randomized into two groups, i.e., Yoga (n = 31) and Control (n = 35). Yoga group received IAYT intervention for 1 week at yoga center of S-VYASA whereas Control group maintained their normal lifestyle.Outcome measures: The Falls Efficacy Scale (FES), Handgrip Strength test (left hand LHGS and right hand RHGS), Timed Up and Go Test (TUG), Sit-to-Stand (STS), and right & left extension and flexion were measured on day 1 and day 7.Results: There were a significant reduction in TUG (p < 0.001), Right (p < 0.001), and Left Flexion (p < 0.001) whereas significant improvements in LHGS (p < 0.01), and right extension (p < 0.05) & left extension (p < 0.001) from baseline in Yoga group.Conclusion: IAYT practice showed an improvement in TUG, STS, HGS, and Goniometer test, which suggest improved muscular strength, flexibility, and functional mobility.CTRI Registration Number: http://ctri.nic.in/Clinicaltrials, identifier CTRI/2017/10/010141.

Lubricant condition monitoring (LCM) is a preferred condition monitoring (CM) technology for fault diagnosis and prognosis owing to its ability to derive a wide range of information from the system (machine/equipment) state and lubricant state. Given the importance of LCM for maintenance decision support, an accurate and reliable remaining useful life (RUL) prediction framework is necessary. The LCM health information in the form of degradation trends is therefore evaluated using numerous statistical, model-based, and artificial intelligence approaches by various researchers. A multitude of factors widely affects the degradation trends viz. operating conditions, environmental variations, oil replenishments, oil loss, chemical breakdown, etc. These factors increase the complexity of the time-series degradation trends making RUL prediction intractable using several of the standard statistical approaches. Therefore, limited research is available on lubricating oil RUL prediction with these influential factors accounted for. Focusing on the complexity of the degradation trend with oil replenishment effects (ORE), we propose the use of the Gaussian process regression (GPR) model for RUL prediction in this study. The model has an advantage over other data-driven approaches as it is a non-parametric Bayesian method. To exploit prior information and historical data collected, the approach is extended to multi-output GPR (MO-GPR) which effectively defines the correlations between historical degradation trends for similar lubrication systems with the current degradation pattern of a system being monitored in real-time. Three different oil replenishment strategies are considered under MO-GPR to demonstrate the applicability and flexibility of this method.

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