BackgroundAlthough Odisha is the largest contributor to the malaria burden in India, no systematic study has examined its malaria trends. Hence, the spatio-temporal trends in malaria in Odisha were assessed against the backdrop of the various anti-malaria strategies implemented in the state.MethodsUsing the district-wise malaria incidence and blood examination data (2003–2013) from the National Vector Borne Disease Control Program, blood examination-adjusted time-trends in malaria incidence were estimated and predicted for 2003–2013 and 2014–2016, respectively. An interrupted time series analysis using segmented regression was conducted to compare the disease trends between the pre (2003–2007) and post-intensification (2009–2013) periods. Key-informant interviews of state stakeholders were used to collect the information on the various anti-malaria strategies adopted in the state.ResultsThe state annual malaria incidence declined from 10.82/1000 to 5.28/1000 during 2003–2013 (adjusted annual decline: -0.54/1000, 95% CI: -0.78 to -0.30). However, the annual blood examination rate remained almost unchanged from 11.25% to 11.77%. The keyinformants revealed that intensification of anti-malaria activities in 2008 led to a more rapid decline in malaria incidence during 2009–2013 as compared to that in 2003–2007 [adjusted decline: -0.83 (-1.30 to -0.37) and -0.27 (-0.41 to -0.13), respectively]. There was a significant difference in the two temporal slopes, i.e., -0.054 (-0.10 to -0.002, p = 0.04) per 1000 population per month, between these two periods, indicating almost a 200% greater decline in the post-intensification period. Although, the seven southern high-burden districts registered the highest decline, they continued to remain in that zone, thereby, making the achievement of malaria elimination (incidence <1/1000) unlikely by 2017.ConclusionThe anti-malaria strategies in Odisha, especially their intensification since 2008, have helped improve its malaria situation in recent years. These successful measures need to be sustained and perhaps intensified further for eliminating malaria from Odisha.

Recently, surface electromyography (sEMG) emerged as a novel biometric authentication method. EMG system parameters, such as the feature sets and channel numbers, have been known to affect system performances. Therefore, it is crucial to investigate these effects on the performance of the sEMG-based biometric system to determine optimal system parameters. In this study, three robust feature sets, Time-domain (TD) feature, Frequency Division Technique (FDT), and Autoregressive (AR) feature, and their combinations were investigated, while the number of channels varying from one to eight. For these system parameters, the performance of sixteen static wrist and hand gestures was systematically investigated in two authentication modes: verification and identification. The results from 24 participants showed that the TD features significantly (p<0.05) and consistently outperformed FDT and AR features for all channel numbers. The results also showed that the performance of a four-channel setup was not significantly different from those with higher numbers of channels. The average equal error rate (EER) for a four-channel sEMG verification system was 4% for TD features, 5.3% for FDT features, and 10% for AR features. For an identification system, the average Rank-1 error (R1E) for a four-channel configuration was 3% for TD features, 12.4% for FDT features, and 36.3% for AR features. The electrode position on the flexor carpi ulnaris (FCU) muscle had a critical contribution to the authentication performance. Thus, the combination of the TD feature set and a four-channel sEMG system with one of the electrodes positioned on the FCU are recommended for optimal authentication performance.

Background: Multichannel surface electromyography (EMG) is a method to examine properties of motor unit (MU) activity using multiple electrodes arranged on a two-dimensional grid. This technique can be used to examine alterations in EMG activity distribution due to contraction intensity as well as due to physiological differences such as age or sex. Therefore, the purpose of this study was to compare strength and high-density surface EMG (HDsEMG) features during isometric and isokinetic knee extensions between older and younger men and women. Methods: Twenty younger (ages 19-25 years) and twenty older (ages 64-78) men and women performed submaximal and maximal isometric (at a joint angle of 90°) and isokinetic knee extensions, while HDsEMG was recorded from the vastus lateralis. Spatial distribution was estimated using the root mean square (RMS), and 2dimensional (2D) maps were developed to examine spatial features. Coefficient of variation (CV) and modified entropy were used to examine alterations in muscle heterogeneity and pattern. Peak torque and HDsEMG parameters were compared across age and gender. Results: Younger males and females produced significantly higher mean torque than the older group (p < 0.001) for all contractions. Both age-and sex-related significant differences (p < 0.05) were found for EMG spatial features suggesting neuromuscular differences. Modified entropy was significantly higher and CV was lower for young females compared to young males (p < 0.05) across both isometric and isokinetic contractions. Conclusions: We found that isometric and isokinetic knee extension strength, spatial distribution, and intensity differ as a function of age and sex during knee extensions. While there were no differences detected in entropy between age groups, there were sex-related differences in the younger age category. The lack of age-related differences in entropy was surprising given the known effects of aging on muscle fiber composition. However, it is often reported that muscle coactivation increases with age and this work was limited to the study of one muscle of the knee extensors (vastus lateralis) which should be addressed in future work. The findings suggest while both age and sex affect muscle activation, sex had a greater effect on heterogeneity. The results obtained will help to develop improved rehabilitation programs for aging men and women.

Accidental falls are a major health concern among older adults. Currently, fall prevention programs employ clinical assessment scores for identifying elderly fallers based on cutoff values. Biomechanical parameters provide crucial information differentiating pathological gait and posture and can be used to classify elderly fallers and non-fallers. Pattern recognition models based on biomechanical parameters may provide greater insight for such classification. The purpose of this study was to compare the classification accuracy of different pattern recognition models for identifying elderly fallers using biomechanical parameters measured during balance and gait tasks. Pattern recognition models were also developed using clinical assessment scores and compared to the models based on biomechanical parameters for accurately identifying elderly fallers. Participants included 58 non-fallers (age = 72.3 ± 5.7) and 41 fallers (age = 74.0 ±12.3) who performed balance and gait tasks on a walkway with embedded force plates and pressure mats. The parameters included 2D ground reaction force (GRF), center of pressure (COP), and the plantar pressure (PP). Using this data as input, different classification algorithms were used to build models. Maximum accuracy of 86.02% for classifying faller/non-faller categories was obtained using a classifier based on biomechanical parameters from combined gait and balance tasks. The GRF parameters ranked higher than COP and PP parameters based on F-score ranking suggesting predictor importance of GRF parameters. The classification performance was further improved by adding GRF parameters to the more commonly used COP parameters. However, the classifiers based on clinical assessment scores resulted in a maximum accuracy of 92.93% suggesting that elderly fallers can be accurately classified using pattern recognition models based on clinical assessment scores. INDEX TERMS Balance, biomechanical parameters, classification, clinical assessment tools, fall risk, gait, older adults.

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Recent advances in biometric research have established surface electromyogram (sEMG) as a potential spoof-free solution to address some key limitations in current biometric traits. The nature of sEMG signals provide a unique dual-mode security: sEMGs have individual-specific characteristics (biometrics), and users can customize and change gestures just like passcodes. Such security also facilitates the use of code sequences (multicode) to further enhance the security. In this study, three levels of fusion, score, rank, and decision were investigated for two biometric applications, verification and identification. This study involved 24 subjects performing 16 hand/finger gestures, and code sequences with varying codelengths were generated. The performance of the verification and identification system was analyzed for varying codelength (M: 1-6) and rank (K: 1-4) to determine the best fusion scheme and desirable parameter values for a multicode sEMG biometric system. The results showed that the decision-level fusion scheme using a weighted majority voting resulted in an average equal error rate of 0.6% for the verification system when M=4. For the identification system, the score-level fusion scheme with score normalization based on fitting a Weibull distribution resulted in a minimum false rejection rate of 0.01% and false acceptance rate of 4.7% using a combination of K=2 and M=4. The results also suggested that the parameters M and K could be adjusted based on the number of users in the database to facilitate optimal performance. In summary, a multicode sEMG biometric system was developed to provide improved dual-mode security based on the personalized codes and biometric traits of individuals, with the combination of enhanced security and flexibility.

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