Behind the Sarees: The Physical Toll of Being a Saree Seller

In the bustling world of textile retail, particularly in saree-selling shops in Ahmedabad, workers face unique occupational challenges that often go unnoticed. A recent study sheds light on the musculoskeletal disorders (MSDs) affecting saree sellers due to their long hours, repetitive movements, and awkward postures. Let's explore the findings of this insightful study and the potential interventions that could improve the working conditions of these workers.

The study, conducted on 56 saree sellers in Ahmedabad, reveals startling data about the physical toll this occupation takes. Nearly all participants (99%) worked eight hours a day, with a significant portion (70%) working seven days a week. These long hours, combined with repetitive movements and awkward postures, contribute to a high prevalence of musculoskeletal disorders, particularly in the lower limbs.

More than half of the workers (54%) reported experiencing pain in the past 12 months, with the most common issues occurring in the knees (17%) and ankles (7%). The repetitive action of getting up and sitting down, often more than five times daily, exacerbates these conditions. Workers reported comfort in positions such as cross-legged sitting or kneeling on the floor, but these postures can further strain the body over time.

Despite the high prevalence of MSDs, awareness and utilization of treatment options remain low. While 62% of the affected workers underwent surgical treatment, only a tiny percentage (7%) received physiotherapy. This suggests a need for increased awareness of non-invasive treatments like physiotherapy, which could significantly alleviate discomfort and prevent further complications.

The study emphasizes the need for ergonomic interventions in saree-selling shops. Adjusting workspaces to reduce awkward postures and incorporating regular breaks to minimize repetitive movements could go a long way in preventing musculoskeletal disorders. Implementing proper seating arrangements, ensuring that workers do not have to sit or kneel for extended periods, and educating them on proper posture and movement techniques could greatly improve their quality of life.

Saree sellers, like many workers in physically demanding jobs, are vulnerable to long-term health issues caused by poor ergonomics and strenuous working conditions. The findings of this study highlight the urgent need for ergonomic solutions and greater awareness of physiotherapy in this industry. By prioritizing the health of saree sellers, we can help reduce the incidence of musculoskeletal disorders and improve the well-being of these essential workers.

The full study, published in the International Journal for Multidisciplinary Research, serves as a wake-up call for better workplace practices in the textile industry, particularly for saree sellers who endure long hours and repetitive movements daily. Let’s strive to make their workplaces healthier and more supportive.

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Revolutionizing Saree Shopping: How AI is Making Saree Texture Identification a Breeze

For centuries, sarees have been an integral part of Indian women's wardrobes[1,2,3,4]. These elegant garments come in a dazzling array of materials, each with its own unique texture and feel[5,6,7,8]. Traditionally, selecting the perfect saree involves touching and feeling the fabric, which is not always feasible with the rise of online shopping[5,9,10,11]. However, modern technology is stepping in to bridge this gap.

The Challenge of Choosing the Perfect Saree Online

One of the biggest challenges of buying sarees online is the inability to touch the fabric. With so many different materials available, from silk to cotton to chiffon, it's tough to know exactly what texture you're getting just by looking at a picture. This issue is compounded for those who might not have extensive experience with the various textures and types of saree materials.[12,13]

Enter Deep Learning: A Technological Solution

Thanks to rapid advancements in smartphone technology and artificial intelligence (AI), we can tackle this problem innovatively. Imagine being able to look at a photo of a saree and instantly know what texture it is made of. This is no longer science fiction but a reality made possible through deep learning.

How Does It Work?

A groundbreaking framework has been developed that uses deep learning to classify saree textures quickly and accurately. Here’s how it works:

1. Image Capture: You take a picture of the saree using your smartphone.

2. Mask RCNN: This advanced AI tool helps segment and identify the saree in the image by generating patches focusing on the fabric's texture.

3. VGG-16 Network: This is where the magic happens. The VGG-16 network, a type of deep learning model, analyzes these patches to accurately classify the saree texture.

Why Mask RCNN and VGG-16?

Mask RCNN is a state-of-the-art model for image segmentation. It ensures that the saree is accurately detected in the image, isolating the fabric from the background.[14] Once we have these precise patches, the VGG-16 network comes into play. This model is known for its robust performance in image recognition tasks. It processes the texture details in the patches and determines the saree material.[15,16,17,18]

Exceptional Accuracy

In a paper by D. S. Dakshina, Dr. P. Jayapriya, and R. Kala (source), the deep learning pipeline outperforms existing methods, achieving an impressive 97.41% accuracy in saree material classification. This means you can shop for sarees online with greater confidence, knowing that the fabric texture identified by the AI is almost always spot on.

The Future of Saree Shopping

With this technology, the traditional tactile experience of saree shopping can be brought to the digital world. You can make informed decisions about saree materials without physically touching them. This innovation is set to revolutionize how we shop for sarees, making it easier, faster, and more reliable.

The integration of deep learning into saree shopping is a game-changer. By harnessing the power of Mask RCNN and the VGG-16 network, a system is developed that accurately identifies saree materials from images. This enhances the online shopping experience and ensures that you get exactly what you expect. Say goodbye to the guesswork and hello to smarter saree shopping!

Bibliography:

1. Bhatnagar, Parul. Traditional Indian Textiles. Abhishek Publications, 2005.

2. Franck, Irene M., and David M. Brownstone. The Silk Road: A History. Facts on File, 1986.

3. Gordon, Beverly. Textiles: The Whole Story: Uses, Meanings, Significance. Thames & Hudson, 2011

4. Dehejia, Vidya. Indian Art. Phaidon Press, 1997.

5. Chishti, Rta Kapur, and Amba Sanyal. Saris: Tradition and Beyond. Roli Books, 2010.

6. Gillow, John, and Nicholas Barnard. Traditional Indian Textiles. Thames & Hudson, 2008.

7. Murphy, Veronica. The Indian Textile Sourcebook. A&C Black, 2011.

8. Naik, Shailaja D. Traditional Embroideries of India. APH Publishing, 1996.

9.    Barnes, Ruth, and Joanne B. Eicher. Dress and Gender: Making and Meaning in Cultural Contexts. Bloomsbury Publishing, 1997.

10. Pal, Pratapaditya. Indian Saris: Traditions - Perspectives - Design. Mapin Publishing, 2006.

11. Banerjee, Mukulika, and Daniel Miller. The Sari. Berg Publishers, 2008.

12. Sengupta, Joy. "E-commerce and Indian Fashion: How Digital Platforms Are Changing the Way We Shop." The Indian Journal of Business, vol. 22, no. 4, 2019, pp. 45-67.

13. Rathi, Meenal. "Challenges of Online Shopping for Traditional Wear in India." International Journal of Marketing & Technology, vol. 7, no. 5, 2018, pp. 115-128.

14. He, Kaiming, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick. "Mask R-CNN." Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2961-2969.

15. Simonyan, Karen, and Andrew Zisserman. "Very Deep Convolutional Networks for Large-Scale Image Recognition." arXiv preprint arXiv:1409.1556, 2014.

16. Russakovsky, Olga, et al. "ImageNet Large Scale Visual Recognition Challenge." International Journal of Computer Vision, vol. 115, no. 3, 2015, pp. 211-252.

17. Simonyan, Karen, and Andrew Zisserman. "Deep Convolutional Networks for Large-Scale Image Recognition." International Conference on Learning Representations (ICLR), 2015.

18. Yosinski, Jason, Jeff Clune, Yoshua Bengio, and Hod Lipson. "How Transferable Are Features in Deep Neural Networks?" Advances in Neural Information Processing Systems (NIPS), 2014.

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Handloom Weaving: Taking a Toll on the Joints !!!

In the ancient city of Varanasi, where tradition weaves its way through the fabric of daily life, handloom weaving stands as a testament to centuries-old craftsmanship. Yet, amid the intricate patterns and vibrant colors, a silent struggle unfolds—one that echoes through the aches and pains of the artisans themselves.

Picture this: hours spent hunched over, shoulders tense, back curved, as skilled hands move rhythmically across the loom. It's a scene of dedication and artistry, but also one fraught with risk. Poor posture, exacerbated by the demands of their craft, takes its toll on the bodies of handloom weavers, leading to a myriad of musculoskeletal problems.

A recent study done by Sunita Dixit, which is published titled “Anthropometric Measurement & Assessment of Occupational Ergonomic Risks of Handloom Weaving in Varanasi District” delves into this issue, shedding light on the physical challenges faced by these artisans. Through careful evaluation of anthropometric measurements and body mass index, researchers aimed to assess the physical fitness of handloom weavers. What they uncovered was illuminating—a high prevalence of musculoskeletal disorders, stemming from the prolonged hours of static work and awkward postures inherent in traditional handloom designs.

As reported by her “In traditional old looms, normally there is no workstation adjustability and adjustment of weaving height is difficult that causes the awkward postures of the upper body. Inappropriately designed hand tools and the kind of the task are the chief causes of awkward postures of wrists and fingers. “

                        

As can be seen from the results a full 86% of the weavers surveyed have to work with the  postures which are in the top risk category. 

The findings underscore a pressing need for intervention. By understanding the ergonomic demands of handloom weaving and the strain it places on the body, we can pave the way for meaningful change. From redesigning traditional looms to accommodate healthier working postures to implementing targeted interventions aimed at mitigating musculoskeletal risks, there are actionable steps we can take to support the well-being of handloom weavers.

One crucial tool in this endeavor is the Rapid Entire Body Assessment (REBA), which offers a systematic approach to evaluating working postures and identifying areas for improvement. Through observations of handloom weavers in action, researchers assigned scores to various body parts, pinpointing areas of concern and highlighting opportunities for intervention.

At the heart of this research lies a simple yet profound question: Are handloom weavers suffering because of unnatural postures? The answer, it seems, is a resounding yes. But with awareness comes opportunity—the opportunity to advocate for change in the ergonomic design of the machines and other adjustment , to champion the well-being of artisans whose craft is not only a livelihood but a cultural heritage.

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Is Tussar Silk Inferior to Mulberry Silk ?

In a paper entitled  "Study of property and structural variants of mulberry and Tussar silk filaments" by professor Mohan Gulrajani, one can get several hints which may lead to the answer to the question.

"A glance at the typical tensile behaviour reveals that the stress-strain curve of these two varieties is distinctly different, in that tasar shows a clear yield point and very high elongation compared to the mulberry filament."

Conclusion 1:  Tussar silk can undergo significant stretching before permanently deforming.

The tusar silk stress-strain curve exhibits a clear yield point. A yield point is a point on the stress-strain curve where the material transitions from elastic deformation (where it returns to its original shape after the force is removed) to plastic deformation (where it retains some deformation even after the force is removed). This suggests that Tussar silk can undergo significant stretching before permanently deforming. 

Conclusion 2:  Tussar can stretch a lot before reaching its breaking point compared to mulberry silk.

The stress-strain curve of tussar silk also shows very high elongation compared to mulberry silk. Elongation refers to how much a material stretches before breaking. The fact that tussar silk exhibits high elongation means it can stretch a lot before reaching its breaking point compared to mulberry silk.

In contrast, mulberry silk does not show as pronounced a yield point and has lower elongation compared to tussar silk. This implies that mulberry silk is less flexible and may have a more limited ability to stretch before breaking compared to tasar silk.

Why there is a difference in their properties

One answer can  be density.  The density of mulberry is higher ( 1.35 g/cc) as compared to tussar ( 1.30 g/cc). This suggests a relatively poor degree of orientation and less order in Tussar, which gives to lower modulus and elongation behavior of tussar.

These values have their commercial and functional implications.