Higher order feature extraction and selection for robust human gesture recognition using CSI of COTS Wi-Fi devices

Device-free human gesture recognition (HGR) using commercial o the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections ofWi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five di erent users was 96.23% in the laboratory environment.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio

Sign language gesture recognition with bispectrum features using SVM

Wi-Fi based sensing system captures the signal reflections due to human gestures as Channel State Information (CSI) values in subcarrier level for accurately predicting the fine-grained gestures. The proposed work explores the Higher Order Statistical (HOS) method by deriving bispectram features (BF) from raw signal by adopting a Conditional Informative Feature Extraction (CIFE) technique from information theory to form a subset of informative and best features. Support Vector Machine (SVM) classifier is adopted in the present work for classifying the gesture and to measure the prediction accuracy. The present work is validated on a secondary dataset, SignFi, having data collected from two different environments with varying number of users and sign gestures. SVM reports an overall accuracy of 83.8%, 94.1%, 74.9% and 75.6% in different environments/scenarios.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio

Experimental study of developing turbulent flow and heat transfer in ribbed convergent/divergent rectangular ducts

The article represents an experimental investigation of friction and heat transfer characteristics of divergent / convergent rectangular ducts with an inclination angle of 1˚ in the y-axis. Measurements were taken for a convergent / divergent rectangular duct of aspect ratio AR at inlet1.25 and outlet in convergent channel 1.35; but in case of divergent duct it can be reversed. The four uniform rib heights, e = 3, 6, 9 and 12 mm the ratio between rib height to hydraulic mean diameter (e/Dm) are 34.8, 69.7, 104.6 and 138.7 a constant rib pitch distance, P = 60 mm has been used. The flow rate in terms of average Reynolds number based on the hydraulic mean diameter (Dm) is 86 mm of the channel was in a range of 20,000 to 50,000. The two ceramic heating strip of 10 mm thickness is used as a heating element have attached on top and bottom surfaces for the test sections. The heat transfer performance of the divergent / convergent ducts for 3, 6, 9 and 12 mm ribs was conducted under identical mass flow rate based on the Reynolds number. In our experiments has totally 8 different ducts were used. In addition, the acceleration / deceleration caused by the cross section area, the divergent duct generally shows enhanced heat transfer behavior for four different rib sizes, while the convergent duct has an appreciable reduction in heat transfer performance. From result point view divergent duct with 3 mm height ribbed square duct gets maximum heat transfer coefficient with minimum friction loss over the other convergent / divergent ducts