Numerous researchers have investigated the relationship between EMG and joint torque. Most of these studies use some conventional filtering (i.e. rectification followed by low pass filtering) to estimate the electromyogram (EMG) amplitude and then relate it to the joint torque. Currently some advanced pre-processing techniques (i.e. signal whitening) are also used to estimate the EMG amplitude and then relate it to joint torque. In this study we apply some pre-processing techniques like DC offset removal, noise filtering followed by rectification and then we calculate the moving average of the EMG signal. Thus we get a linear envelope (muscle activation) of the EMG signal and use that linear envelope to estimate the joint torque. To map the EMG to joint torque we propose a new mathematical model. This model has some unknown adj ustable parameters, and the values of these parameters are obtained using nonlinear regression. Five subjects took part in the experiments. They were asked to perform non-fatiguing and variable force maximal voluntary contractions (MVC) and submaximal voluntary contractions (SMVC), and the resulting elbow joint torque and EMG signals were recorded. This recorded data was entered to the model, to estimate best fit values for the unknown parameters. Once these values of the parameters were obtained they were put into the model and thus joint torque was estimated. Predictions made by our model are well correlated with experimental data in both MVC and SMVC, the correlation coefficient and mean square error obtained for experimental data during MVC are 0.998 and 0.056Nm respectively. The results of this new model were compared with other existing models and some new models and it was found that our model has greater correlation and least mean square error with experimental data. This model may be helpful in the control systems for recognition systems, robot manipulators, exoskeletons, EMG prosthesis and electric stimulators.
Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy. To address the issues of low accuracy and high energy consumption of DV-Hop-based localization in static WSNs, this paper proposes an enhanced DV-Hop algorithm for efficient and accurate localization with reduced energy consumption. The proposed method consists of three steps: first, the single-hop distance is corrected using the RSSI value for a specific radius; second, the average hop distance between unknown nodes and anchors is modified based on the difference between actual and estimated distances; and finally, the least-squares approach is used to estimate the location of each unknown node. The proposed algorithm, named Hop-correction and energy-efficient DV-Hop (HCEDV-Hop), is executed and evaluated in MATLAB to compare its performance with benchmark schemes. The results show that HCEDV-Hop improves localization accuracy by an average of 81.36%, 77.99%, 39.72%, and 9.96% compared to basic DV-Hop, WCL, improved DV-maxHop, and improved DV-Hop, respectively. In terms of message communication, the proposed algorithm reduces energy usage by 28% compared to DV-Hop and 17% compared to WCL.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.