ASSESSMENT OF PHYSIOLOGICAL STRAIN IN MALE FOOD CROP CULTIVATORS ENGAGED IN MANUAL THRESHING TASK IN A SOUTHERN DISTRICT OF WEST BENGAL

The impact of rise in ambient temperature is not confined to output; it has an impact on the work performance of human beings associated with occupational activities in informal sector, especially those carried out in the open field under the sky. The agricultural workers are constrained to work manually all through the day irrespective of disparity in working situation existing in the working environment. Hence, there is an urgent need to study the cardiac performance status in terms of indicators of physiological strain of the human resources. In this backdrop, the present study has been undertaken to assess the degree of physiological strain in male food crop cultivators’ (age range 24 - 36 years) engaged in manual threshing (separating the grains from the rice straw by manually - by hand i.e. beating method) during paddy cultivation time. Moreover the magnitude of physiological strain was significantly higher (P < 0.5) during “Boro” type of paddy cultivating time. The result of the study indicated that human resources are indeed subjected to strains, albeit to different degree, as adjudged by the indicators of physiological strain

Unleashing Innovation across the Value chain - A motto for Growth

Learning and innovation go hand in hand. The arrogance of success is to think that what was did yesterday will be sufficient for tomorrow. The journey from the invention of Fire to the electric bulb, from the air gliders made by Wright Brothers to the Boeing 707, from the typewriter to Laptop and Tablets, from the days of hand-written letters to e-mails, the whole transformation of the Human Era is entirely based on a single aspect: INNOVATION. The major aim of the work is to reveal how innovation could be a driving force for growth in the society

M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance

We present a robust system for state estimation that fuses measurements from multiple lidars and inertial sensors with GNSS data. To initiate the method, we use the prior GNSS pose information. We then perform incremental motion in real-time, which produces robust motion estimates in a global frame by fusing lidar and IMU signals with GNSS translation components using a factor graph framework. We also propose methods to account for signal loss with a novel synchronization and fusion mechanism. To validate our approach extensive tests were carried out on data collected using Scania test vehicles (5 sequences for a total of ~ 7 Km). From our evaluations, we show an average improvement of 61% in relative translation and 42% rotational error compared to a state-of-the-art estimator fusing a single lidar/inertial sensor pair.Comment: For associated video check https://youtu.be/-xSbfaroEP

IMU-based Online Multi-lidar Calibration

Modern autonomous systems typically use several sensors for perception. For best performance, accurate and reliable extrinsic calibration is necessary. In this research, we propose a reliable technique for the extrinsic calibration of several lidars on a vehicle without the need for odometry estimation or fiducial markers. First, our method generates an initial guess of the extrinsics by matching the raw signals of IMUs co-located with each lidar. This initial guess is then used in ICP and point cloud feature matching which refines and verifies this estimate. Furthermore, we can use observability criteria to choose a subset of the IMU measurements that have the highest mutual information -- rather than comparing all the readings. We have successfully validated our methodology using data gathered from Scania test vehicles.Comment: For associated video, see https://youtu.be/HJ0CBWTFOh

IMU-based online multi-lidar calibration

Modern autonomous systems typically use several sensors for perception. For best performance, accurate and reliable extrinsic calibration is necessary. In this research, we propose a reliable technique for the extrinsic calibration of several lidars on a vehicle without the need for odometry estimation or fiducial markers. First, our method generates an initial guess of the extrinsics by matching the raw signals of IMUs co-located with each lidar. This initial guess is then used in ICP and point cloud feature matching which refines and verifies this estimate. Furthermore, we can use observability criteria to choose a subset of the IMU measurements that have the highest mutual information - rather than comparing all the readings. We have successfully validated our methodology using data gathered from Scania test vehicles

Observability-aware online multi-lidar extrinsic calibration

Accurate and robust extrinsic calibration is necessary for deploying autonomous systems which need multiple sensors for perception. In this paper, we present a robust system for real-time extrinsic calibration of multiple lidars in vehicle base frame without the need for any fiducial markers or features. We base our approach on matching absolute GNSS and estimated lidar poses in real-time. Comparing rotation components allows us to improve the robustness of the solution than traditional least-square approach comparing translation components only. Additionally, instead of comparing all corresponding poses, we select poses comprising maximum mutual information based on our novel observability criteria. This allows us to identify a subset of the poses helpful for real-time calibration. We also provide stopping criteria for ensuring calibration completion. To validate our approach extensive tests were carried out on data collected using Scania test vehicles (7 sequences for a total of ~ 6.5 Km). The results presented in this paper show that our approach is able to accurately determine the extrinsic calibration for various combinations of sensor setups.Comment: For associated video file, see https://youtu.be/aMWvWozBdr

Effect of Practicing select Indian Classical Dance Forms on Body Composition Status Of Bengalee Females: An Anthropometric Study

40-48Emerging epidemic of obesity has led to focus on the exercise training and on regular practicing of moderate-to-vigorous intensity physical activity. Bharatnatyam and Kathak are two most popular traditional Indian classical Dance forms which have been practiced for a long period of time mainly for recreational purpose. They involve adoption of different body postures, movements and thereby may influence body composition. Present study has been undertaken in this context to assess the impact of regularly practicing these two dance forms on body composition of young adult Bengalee females. It has been found that individuals practicing both the two dancing forms have favorable body composition parameters adjudged anthropometrically, compared to their age and sex matched control group individuals of similar socioeconomic status. The favorable impact on body composition is more pronounced in individuals practicing Bharatnatyam form of dance

EFFECT OF BHARATNATTYAM DANCING ON BODY COMPOSITION AND PHYSICAL FITNESS STATUS OF ADULT BENGALEE FEMALES

9-15Globally more than 1 billion adults are overweight, and at least 300 million of them are clinically obese. Obesity in the developing countries, including India, undergoing major nutrition and lifestyle transition, too, is on the rise. It has also been found to be associated with increased risk of the metabolic syndrome, coronary heart disease (CHD etc. American College of Sports Medicine (ASCM), recommends aerobic exercise for of body weight optimization and maintaining cardio vascular fitness. In this backdrop a study has been undertaken to assess the impact of undergoing training in Bharatnatyam dancing- a popular form of Indian classical dancing an aerobic exercise, on body composition and physical fitness status in young adult Bengalee females. 87 such volunteers, receiving the training for at least five years, constituted the dancing group (DG). It has been observed that individuals receiving the training have significantly favorable body composition and fitness status, compared to the control group (CG) consisting of 39 individuals from similar socioeconomic background and but not receiving exercise training of any form. It could therefore be concluded that Bharatnatyam dancing is a cost effective beneficial way of exercising for maintaining a healthy body composition and better fitness status and thus consequently reducing the cardio vascular risk factors

ADDRESSING THE PUBLIC HEALTH CHALLENGE OF OBESITY THROUGH A NOVEL STRATEGY

Page : 78-86The prevalence of overweight and obesity-which have, during the past decade, joined underweight, malnutrition, and infectious diseases as major health problems threatening the developing world including India, has taken an alarming shape. Although obesity is a multi-factorial issue, evidences indicated the role of physical inactivity in its causation.As risk factors are being identified, various approaches have been tried out; but effective long term outcomes are yet to be obtained for prevention of the looming threat of obesity. Therefore, the challenge is to search for some novel and acceptable strategies to curb the rising trend of obesity. On the other hand, it is well known that India has a tradition of practicing dance, an appealing form of physical activity, for centuries. An attempt, in this context, has been made to assess and document the impact of being trained inBharatnatyam, an Indian classical dance form, on obesity status of Bengalee females of age range 25-30 years. A significant (P < 0.05) favorable impact of undergoing training and regular practicing of Bharatnatyam has been found. As a cost effective and holistic approach, Bharatnatyamcan be a choice to address obesity, the new age public health issue

Neural Greedy Pursuit for Feature Selection

We propose a greedy algorithm to select $N$ important features among $P$ input features for a non-linear prediction problem. The features are selected one by one sequentially, in an iterative loss minimization procedure. We use neural networks as predictors in the algorithm to compute the loss and hence, we refer to our method as neural greedy pursuit (NGP). NGP is efficient in selecting $N$ features when $N \ll P$, and it provides a notion of feature importance in a descending order following the sequential selection procedure. We experimentally show that NGP provides better performance than several feature selection methods such as DeepLIFT and Drop-one-out loss. In addition, we experimentally show a phase transition behavior in which perfect selection of all $N$ features without false positives is possible when the training data size exceeds a threshold