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Kalra, Parveen
- Gait Parameters in School Going Children Using a Marker-Less Approach
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PDF Views:73
Authors
Affiliations
1 Center of Excellence in Industrial and Product Design, PEC University of Technology, Chandigarh 160 012, IN
2 National Institute of Industrial Engineering, Powai, Mumbai 400 087, IN
1 Center of Excellence in Industrial and Product Design, PEC University of Technology, Chandigarh 160 012, IN
2 National Institute of Industrial Engineering, Powai, Mumbai 400 087, IN
Source
Current Science, Vol 111, No 10 (2016), Pagination: 1668-1675Abstract
The burden of course work in Indian schools has exposed the school children to various postural/gait disorders due to heavy backpack. Therefore, it is paramount to develop a low cost, non-intrusive and reliable method for calculation of gait parameters. This study assessed the spatiotemporal parameters such as height of earlobe (HoE), stride length (SL) and stride width (SW) using the markerless sensor Kinect v2 and conventional techniques pursued in Indian clinics. Sixty school children (aged 11 to 15 years) were monitored through both the techniques while performing walking trials. To assess the agreement between the techniques Bland-Altman 95% bias, percentage error (PE), Pearson's correlation coefficients (r1) and concordance correlation coefficients (r2) were determined. Each parameter obtained from both techniques possessed strong correlation (r1 and 2 > 0.90). Gait analysis using the Kinect V2 sensor is an acceptable, unobtrusive and economical method. The effect of relative backpack weight (RBW), i.e. (bag weight to body weight percentage) and strategies of backpack packing recommended by the American Occupational Therapy Association on the selected parameters was studied. The effect of RBW on the variation in parameters was evaluated using the regression curve whereas the effect of proper packing was evaluated by paired sample T test. RBW has positive correlation with SW (r1 = 0.631), negative correlation with HoE (r1 = -0.387) but shows no correlation with SL. Recommended packing strategy of schoolbag by AOTA shows results to reduce the unwanted variation in gait parameters.Keywords
Packing, Heavy Backpack, Spatiotemporal Parameters, Kinect V2, School Children.Full Text
References
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- Early Seizure Detection Techniques: A Review
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PDF Views:0
Authors
Affiliations
1 Department of Centre of Excellence (Industrial & Product Design), PEC University of Technology, Sector-12, Chandigarh - 160012, IN
2 Electronics and Communication Engineering Department, PEC University of Technology, Sector-12, Chandigarh – 160012, IN
1 Department of Centre of Excellence (Industrial & Product Design), PEC University of Technology, Sector-12, Chandigarh - 160012, IN
2 Electronics and Communication Engineering Department, PEC University of Technology, Sector-12, Chandigarh – 160012, IN
Source
Indian Journal of Science and Technology, Vol 11, No 10 (2018), Pagination:Abstract
Objectives: To study the different seizure detection techniques and find an efficient technique which results in optimum output parameters in terms of sensitivity and specificity. Methods/Statistical Analysis: Different seizure detection techniques were studied to find the efficient technique for early seizure detection. Past researches extracted different features from raw Electroencephalogram (EEG) signals. Features like time domain and frequency domain were extracted. Recently, wavelet parameters have been introduced which is the combination of time and frequency domain. For the classification, different classifiers like RNN, Artificial Neural Network, Modified Neural Network and Support Vector Machine were used. Findings: This review paper studies different types of seizure detection techniques. Development of early seizure detection techniques gains a major attention to provide an early alert to the epileptic patient. Different time domain, frequency domain and time-frequency domain features were extracted from the raw EEG signal. For the classification, different classifiers like RNN, Artificial Neural Network (ANN), Modified Neural Network and Support Vector Machine (SVM) were used. It was observed that the technique employing DT-CWT as a feature set and Support Vector Machine as a classifier resulted in maximum classification accuracy of 100% and 0 false alarm rate. So, from the findings, it is concluded that the combination of wavelet decomposition and a number of features extraction will improve the accuracy for early seizure detection in future. Application/Improvements: In future, the early seizure detection techniques can be used to provide an alert to the epileptic patient so that an effective diagnosis can be done before the occurrence of seizure onset.Keywords
Daubechies Wavelet, Detection Techniques, Electroencephalogram, Osorio-Frei Algorithm and SeizureFull Text
- Evaluation of School Backpack Prototype-Based on Gait Parameters, Energy Expenditure and Posture of Students
Abstract Views :321 |
PDF Views:83
Authors
Affiliations
1 Center of Excellence in Industrial & Product Design, PEC University of Technology, Chandigarh - 160012, IN
2 National Institute of Industrial Engineering, Powai, Mumbai - 400087, IN
1 Center of Excellence in Industrial & Product Design, PEC University of Technology, Chandigarh - 160012, IN
2 National Institute of Industrial Engineering, Powai, Mumbai - 400087, IN
Source
Current Science, Vol 115, No 5 (2018), Pagination: 930-936Abstract
Carrying of backpacks by school going children leads to postural and gait adaptations, leading to falls, imbalances, low back pain and musculoskeletal disorders. This study examined gait parameters, posture and energy expenditure differences between the modified backpack (MBP) and the existing backpacks (EBPs) in school students. Using portable gait system, 26 healthy participants performed the trials with three loading conditions of 10%, 20% and 30% of their body weight (BW) with the two different categories of backpacks. There was significant difference in cadence, double support time, ground impact, energy expenditure and anterior lean angle between no-load condition and the backpacks. Relative to the no-load condition, energy expenditure (EE) increased by 4.26 cal/min, and anterior lean angle (ALA) increased by 6.90° for the EBPs at 30% load condition whereas EE increased by 2.83 calories/min and ALA by 4.43° in case of MBP at 30% condition. The results indicate that parameters recorded while carrying MBP resembled the those recorded during no-load condition. The erect posture, natural gait patterns and reduction in energy consumption supported by the MBP may thus reduce the causes of back pain and fatigue among school children.Keywords
Backpack Design, Trunk Angle, Natural Spine, School Children, Back Pain.Full Text
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