Background and Aim: Work related musculoskeletal disorders (WMSDs) are common problems among the workers engaged in unorganised sector. Working as a house maid is a very old profession. The objective of the present study was to evaluate the prevalence of WMSDs in house maids of Kolkata. Methods: Ninety-four house maids (age range of 20-60 years) engaged in different household works for minimum 3 years of duration were recruited in this study by random sampling method from different parts of Kolkata, India. They were divided into four groups based on their age and years of working. Physical parameters, assessment of musculoskeletal disorders and analysis of posture were done by using standard methods and procedures. Results: The study revealed different grades of pain in different parts of the body, maximum being the prevalence of low back pain. The intensity and localisation of pain varied in different age groups showing significant relationship with working experiences and daily working hours. Body part wise pain varied during work, rest and sleep times. Conclusion: WMSDs and pain experienced by the house maids were due to their constrained and awkward working postures for longer duration which need to be corrected with immediate interventions.
This investigation will contribute in the evaluation of pump duty parameters required to design a system comprising of underground pop-up type sprinklers for irrigating turf grass lawn for a proposed site. The study specifically describes the design techniques of irrigation pipeline network of varied zones of landscape areas distant apart operating with minimum pumping units. The entire landscape area is designed with underground pop-up type sprinklers with proper selection spacing over the lawn. A method is proposed comprising of sprinklers, pipes diameters using pipe network simulating software where energy cost can be reduced. The study shows techniques on proper pump selection for landscaping. The labor cost, energy usage and water wastage has been tremendously reduced and optimized. Observations reveal varied pump parameters in that system for varied zones. This method reduces the various pumping units sectioning zones which can satisfy the maximum duty parameters of a single centrifugal pump of the landscape. This manuscript further elaborates on the practical aspects for the design of an efficient system.
Abstract. Existing methods to record interactions between the public and police officers are unable to capture the entirety of police-public interactions. In order to provide a comprehensive understanding of these interactions, the Los Angeles Police Department (LAPD) intends to utilize Body-Worn Video (BWV) collected from cameras fastened to their officers. BWV provides a novel means to collect fine-grained information about police-public interactions. The purpose of this project is to identify foot-chases from the videos using machine-learning algorithms. Our proposed algorithm uses the Bag-of-Intrinsic-Words algorithm followed by classification via support-vector machines. Our training dataset consists of 100 training videos (20 foot-chase & 80 non-foot-chase), and a test dataset of 60 LAPD videos (4 foot-chase & 56 non-foot-chase). We achieved results of 91.6% testing accuracy.1. Introduction. Studying the interaction between police and the public is often a difficult task because little information regarding police-public interaction is retained through activity logs and written reports [4]. In 2014, the Los Angeles Police Department (LAPD) implemented the use of chest-mounted Body-Worn Video (BWV) in small deployments, as seen in Figure 1.1, with the purpose of collecting more information regarding police-public interactions. BWV provides another line of evidence for outcomes of interactions.BWV generates massive volumes of data that can be difficult to analyze. Due to the size of the BWV dataset, it is infeasible for police officers to view all the videos in order to find specific interactions, e.g. foot-chases. Since many BWV videos are likely to be used as evidence, an automated labeling mechanism can save valuable time and resources while maintaining confidentiality of the data. Our work focuses on devising a learning algorithm that can automatically detect whether a particular video contains a foot-chase or not. From our exploration of the literature, such a project is the first of its kind to have been attempted. This paper is organized by the following sections. Section 2 discusses previous work done relevant to video and image processing. Section 3 describes the BWV data and preprocessing procedure. Section 4 discusses the mathematical background behind the feature-extraction methodology, and, our proposed Bag-of-Intrinsic-Words method. Section 5 presents our results and analysis on the BWV provided by the LAPD.
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.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.