Introduction

The garment manufacturing industry relies heavily on precise time measurement systems to optimize production efficiency, control costs, and maintain competitiveness in the global market. Two fundamental concepts that form the backbone of industrial engineering in apparel manufacturing are Standard Allowed Minute (SAM) and Standard Minute Value (SMV). While these terms are often used interchangeably in practice, understanding their distinct characteristics and applications is crucial for manufacturing professionals, students, and industry stakeholders.

This comprehensive analysis examines the technical differences, calculation methodologies, practical applications, and strategic implications of SAM and SMV systems in modern garment manufacturing. Through detailed explanations, calculation examples, and industry insights, this educational guide provides essential knowledge for anyone working in or studying apparel production management.

Comprehensive comparison between SMV and SAM concepts in garment manufacturing, showing their definitions, formulas, components, and key applications

Fundamental Concepts and Definitions

Standard Minute Value (SMV): The Foundation of Time Measurement

Standard Minute Value (SMV) represents the time required for a qualified worker operating at standard performance level to complete a specific garment operation under normal working conditions. SMV is essentially the basic time or core time needed to perform a task, excluding additional allowances for real-world working conditions.

The concept originated from European industrial engineering practices and has become widely adopted in manufacturing systems influenced by European consultants. SMV serves as the fundamental building block for all other time-related calculations in garment manufacturing, providing a standardized baseline for measuring work content. SMV is calculated using the formula: 

SMV = Observed Time × Performance Rating. 

This calculation yields the theoretical minimum time required to complete an operation when performed by a skilled operator working at 100% efficiency under ideal conditions. The performance rating factor accounts for variations in operator speed and skill level, normalizing the observed time to a standard benchmark.

Standard Allowed Minute (SAM): Practical Time Standards

Standard Allowed Minute (SAM) extends beyond SMV by incorporating additional time allowances that account for realistic working conditions in garment manufacturing environments. SAM represents the total time required to complete a garment operation, including provisions for unavoidable delays, personal needs, and operational requirements that occur during actual production.

The fundamental relationship between SAM and SMV is expressed as: 

SAM = SMV + Allowances. 

These allowances typically include bundle handling time, machine maintenance requirements, and personal needs, making SAM a more practical and achievable standard for production planning and performance measurement.

SAM has its roots in American industrial engineering practices and is predominantly used in manufacturing systems influenced by American consultants. This regional preference has led to some confusion in global manufacturing operations, where both terms may be encountered depending on the consulting background and established practices.

Technical Differences and Characteristics

Scope and Time Coverage

The primary distinction between SMV and SAM lies in their scope of time measurement. SMV focuses exclusively on the core operation time, representing the pure working time required to complete a specific task. This narrow focus makes SMV ideal for theoretical calculations and baseline measurements, but it does not reflect the realities of actual production environments.

SAM provides a comprehensive time measurement that encompasses the complete task cycle, including all necessary supportive activities and unavoidable delays. This broader scope makes SAM more suitable for practical production planning, capacity calculation, and realistic performance targets.

Allowance Integration

SMV calculations deliberately exclude allowances, maintaining focus on the core operation time. This exclusion allows SMV to serve as a consistent baseline for comparative analysis across different operations, operators, and production environments.

SAM systematically incorporates multiple types of allowances that reflect real-world working conditions. Bundle allowances typically account for 10% of basic time, covering activities such as material handling, bundle opening, and work organization. Machine and personal allowances usually range from 15-20% of basic time, addressing equipment maintenance, personal needs, and fatigue recovery.

Regional and Industry Preferences

The choice between SMV and SAM terminology often reflects regional industrial engineering traditions and consulting influences. European-influenced systems typically emphasize SMV as the primary measurement unit, focusing on precise operation timing and method optimization. American-influenced systems commonly adopt SAM as the standard measurement, emphasizing practical implementation and realistic production planning.

Many modern garment manufacturers operate with both systems simultaneously, using SMV for technical analysis and method improvement while employing SAM for production planning and costing purposes. This dual approach allows organizations to benefit from both the analytical precision of SMV and the practical applicability of SAM.

Detailed Calculation Methodologies

SMV Calculation Process

The calculation of SMV follows a systematic approach that begins with time study methodology. Industrial engineers observe skilled operators performing specific operations over multiple cycles, typically 5-10 repetitions, to establish reliable timing data. The observed times are recorded using precision stopwatches or electronic timing devices to ensure accuracy.

Performance rating constitutes a critical component of SMV calculation, requiring industrial engineers to assess operator efficiency relative to standard performance levels. This subjective evaluation considers factors such as operator skill, work pace, method adherence, and overall effectiveness. Performance ratings typically range from 60% to 120%, with 100% representing standard performance expectations.

The basic time calculation applies the performance rating to the average observed time: 

Basic Time = Average Observed Time × Performance Rating. 

For example, if an operator completes a collar joining operation in an average of 0.522 minutes with a performance rating of 85%, the SMV would be: 0.522 × 0.85 = 0.444 minutes.

Step	Value	Formula/Calculation
Observed Time (Average)	0.522 minutes	Sum of 5 cycles ÷ 5
Performance Rating	85%	Observed operator efficiency
SMV (Basic Time)	0.444 minutes	0.522 × 0.85
Bundle Allowance (10%)	0.044 minutes	0.444 × 0.10
Machine & Personal Allowance (20%)	0.089 minutes	0.444 × 0.20
SAM (Total Time)	0.577 minutes	0.444 + 0.044 + 0.089

SAM Calculation Framework

SAM calculation builds upon the SMV foundation by incorporating systematic allowances that address practical production requirements. 

The comprehensive SAM formula is: 

SAM = SMV + Bundle Allowances + Machine and Personal Allowances.

Bundle allowances typically constitute 10% of the SMV and account for time spent handling material bundles, organizing work pieces, and managing workflow between operations. These allowances recognize that operators must spend time beyond core sewing activities to maintain production flow.

Machine and personal allowances generally range from 15-20% of SMV, addressing various factors that impact operator productivity. Machine allowances cover equipment maintenance activities such as needle changes, thread replacement, tension adjustments, and minor repairs. Personal allowances provide time for physiological needs, brief rest periods, and fatigue recovery.

Predetermined Time Standards (PTS) Method

An alternative approach to SMV calculation utilizes Predetermined Time Standards (PTS) such as General Sewing Data (GSD) or Methods Time Measurement (MTM). These systems break down operations into fundamental motion elements, each assigned predetermined time values in Time Measuring Units (TMUs).

GSD represents a specialized PTS system developed specifically for the garment industry, consisting of 36 standardized data elements across 7 categories. Each motion element has a predetermined TMU value, where 1 TMU = 0.0006 minutes. Industrial engineers analyze operations by identifying constituent motions and summing their TMU values to calculate total operation time.

This method offers significant advantages in terms of consistency, accuracy, and speed compared to traditional time study approaches. PTS methods eliminate the subjectivity of performance rating and allow for time calculation before production begins. However, they require specialized training and may not capture all operational nuances specific to individual production environments.

Practical Applications and Industry Usage

Production Planning and Capacity Management

SMV serves as the foundation for theoretical capacity calculations and method optimization initiatives. Production planners use SMV data to determine minimum time requirements and establish baseline productivity metrics. 

The formula for theoretical daily production capacity is: 

Capacity = (Available Minutes per Day) ÷ SMV per Garment.

SAM provides more realistic production planning parameters by incorporating practical working conditions. Manufacturing managers rely on SAM for actual capacity planning, considering real-world factors that affect productivity. 

The practical daily production formula becomes: 

Production = (Available Minutes × Line Efficiency) ÷ SAM per Garment.

Cost Estimation and Labor Costing

Cost per minute (CPM) calculations form the basis for labor cost estimation in garment manufacturing. 

The fundamental CPM formula is: 

CPM = Total Labor Cost ÷ Total Minutes Produced. 

SMV provides the base time component for theoretical labor costs, while SAM offers more accurate cost estimates for practical budgeting purposes.

For example, if a factory’s monthly labor cost is $50,000 and operators work 12,480 minutes per month at 80% efficiency, the CPM calculation would be: $50,000 ÷ (12,480 × 0.80) = $5.01 per minute. A garment with 15 minutes SAM would have a labor cost of: 15 × $5.01 = $75.15.

Performance Measurement and Efficiency Analysis

SMV enables operator skill assessment and method comparison by providing standardized time benchmarks. Industrial engineers use SMV data to identify performance variations and improvement opportunities across different operators and production methods.

SAM supports line efficiency measurement and production target setting by incorporating realistic working conditions. 

Line efficiency is calculated as: 

Efficiency = (Actual Production × SAM) ÷ (Available Minutes × Number of Operators). 

This comprehensive approach provides more meaningful performance indicators for manufacturing management.

Advanced Applications and Strategic Implications

Line Balancing and Workflow Optimization

Both SMV and SAM play crucial roles in line balancing activities that optimize production flow and minimize bottlenecks. SMV data helps identify operations with inherent time differences, enabling targeted method improvement initiatives. SAM values support practical line balancing by accounting for realistic working conditions and allowance requirements.Takt time calculation integrates with both measurement systems to establish production rhythm and cycle planning. Takt time represents the pace required to meet customer demand: 

Takt Time = Available Production Time ÷ Customer Demand. 

Operations with SMV or SAM values exceeding takt time requirements indicate potential bottlenecks requiring attention.

Quality Management and Standard Setting

SMV provides a foundation for method standardization and quality consistency initiatives. By establishing precise time standards for optimal methods, SMV supports training programs and performance benchmarking across multiple production lines.

SAM enables realistic target setting that considers practical constraints and human factors. This approach supports sustainable performance improvement and worker satisfaction by establishing achievable goals rather than theoretical maximums.

Technology Integration and Digital Manufacturing

Modern garment manufacturing increasingly integrates digital time study tools and computerized work measurement systems. GSDCost and similar software platforms utilize predetermined time standards to calculate SMV and SAM values with greater accuracy and consistency.

Real-time production monitoring systems leverage SAM data to track performance against established standards, enabling immediate corrective actions and continuous improvement initiatives. These digital solutions provide enhanced visibility into production efficiency and support data-driven decision making.

Industry Case Studies and Practical Examples

Implementation Challenges and Solutions

Manufacturing organizations often face challenges when implementing SMV and SAM systems, particularly regarding operator acceptance and measurement consistency. Successful implementations require comprehensive training programs that help operators understand the purpose and benefits of time standards.

Change management strategies should emphasize the supportive rather than punitive nature of time standards, focusing on performance improvement and fair compensation rather than workforce reduction. Organizations that successfully communicate these benefits typically achieve higher adoption rates and sustainable performance improvements.

Best Practices and Lessons Learned

Industry experience demonstrates the importance of regular standard updates to maintain accuracy and relevance. Production methods, equipment capabilities, and operator skills evolve over time, requiring periodic review and adjustment of established standards.

Cross-functional collaboration between industrial engineers, production managers, and operators enhances the quality and acceptance of time standards. Involving operators in the measurement process builds understanding and commitment while capturing valuable insights about practical working conditions.

Future Trends and Developments

Automation and Smart Manufacturing

The evolution toward Industry 4.0 and smart manufacturing is reshaping traditional approaches to time measurement and production planning. Automated sewing equipment and digital monitoring systems provide more precise timing data while reducing reliance on manual time studies.

Artificial intelligence and machine learning applications show promise for predicting optimal SMV and SAM values based on historical data, operator characteristics, and production conditions. These technologies may enhance the accuracy and responsiveness of time standards while reducing measurement costs.

Sustainability and Worker Welfare

Growing emphasis on sustainable manufacturing practices is influencing approaches to time standards and performance measurement. Organizations increasingly recognize the importance of setting realistic standards that support worker welfare while maintaining productivity requirements.

Fair wage initiatives and living wage commitments rely heavily on accurate SMV and SAM calculations to establish appropriate compensation structures. These social responsibility considerations add strategic importance to precise time measurement systems.

Conclusion

The distinction between Standard Allowed Minute (SAM) and Standard Minute Value (SMV) represents more than semantic difference in garment manufacturing terminology. These concepts embody fundamentally different approaches to time measurement, each serving distinct purposes in modern apparel production management. SMV provides the analytical precision necessary for method optimization and theoretical calculations, while SAM offers the practical realism required for effective production planning and performance management.

Understanding these differences enables manufacturing professionals to select appropriate measurement systems based on specific application requirements. Organizations benefit from implementing both concepts strategically, utilizing SMV for technical analysis and continuous improvement while employing SAM for operational planning and worker performance evaluation. This dual approach maximizes the advantages of each system while addressing the diverse needs of modern garment manufacturing operations.

The continuing evolution of manufacturing technology and social responsibility considerations will likely influence future applications of SAM and SMV systems. However, the fundamental principles of accurate time measurement and fair performance standards will remain central to successful garment manufacturing operations. Mastery of these concepts provides essential foundation knowledge for anyone seeking to excel in apparel production management and industrial engineering.

As the garment industry continues to face pressure for increased efficiency, cost reduction, and social compliance, the strategic application of SAM and SMV systems becomes increasingly critical. Organizations that invest in proper understanding and implementation of these time measurement concepts position themselves for sustainable competitive advantage in the global marketplace. The educational foundation provided by this comprehensive analysis supports informed decision-making and effective implementation of these essential manufacturing tools.

References

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2. https://www.onlineclothingstudy.com/2019/09/standard-minute-value-smv-definition.html
3. https://textilelearner.net/sam-and-smv-calculation-in-garment-industry/
4. https://www.coatsdigital.com/en/blog/defining-standard-time/
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10. https://www.textileindustry.net/ie-formula-for-garments/
11. https://garmentio.zohodesk.com/portal/en/kb/articles/calculating-standard-allowed-minutes-sam-through-time-study
12. https://gotextilefabrics.com/how-calculate-sam-garment-industry
13. https://www.onlineclothingstudy.com/2011/02/how-to-calculate-sam-of-garment.html
14. https://garmentsmerchandising.com/how-to-calculate-allowance-time-in-garment-industry/
15. https://www.linkedin.com/posts/textilelibrary_smv-sam-garments-activity-7301445464501948417-xGNF
16. http://textilemerchandising.com/calculate-allowance-clothing/
17. https://apparelresources.com/business-news/manufacturing/ie-apparel-manufacturing-v-determining-allowances/
18. https://www.scribd.com/document/888598422/SMV-and-SAM-Measurement-in-Garment-Industry
19. https://news.odmya.com/2024/01/12/maximizing-efficiency-mastering-sam-and-smv-in-apparel/
20. https://www.onlineclothingstudy.com/2015/01/pros-and-cons-of-setting-up-garment-sam.html
21. https://leanstitch.com/industrial-engineering-calculations-garment-production-factory/
22. https://www.scribd.com/doc/296228088/SMV-of-Different-Garments
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Are you a dynamic postgraduate looking to launch your career with a prestigious government organization? The Khadi and Village Industries Commission (KVIC), under the Ministry of Micro, Small & Medium Enterprises (MSME), Government of India, is inviting online applications for the post of Young Professionals (YPs) on a contractual basis for its offices in Kolkata, Bhubaneswar, Patna, and Ranchi.

About KVIC

KVIC is a statutory body dedicated to promoting and developing khadi and village industries across India. Now, as part of its plans for accelerating growth and transformation, KVIC is looking for young, talented, and committed professionals to join its team.

Vacancy Details

• Total Posts: 7
  • Khadi Division: 5 Posts
  • Village Industries (V.I.) Division: 2 Posts

Location of Posting

• Kolkata (KVIC Zonal & Satellite Offices)
• Bhubaneswar
• Patna
• Ranchi

Eligibility Criteria

Educational Qualifications:

• For Khadi YPs: Master’s degree in Engineering/Science in Textile Technology from a recognized university.
• For V.I. YPs: Master’s in Engineering/Science or MBA with B.Sc. in any discipline from a recognized university.

Age Limit:

• Below 27 years as on 31.08.2025.

Skills Required:

• Proficient in MS Word, Excel, PowerPoint
• Good communication and writing skills
• Sincere and committed to work

Remuneration & Benefits

• Consolidated Pay: ₹25,000 – ₹30,000 per month (depending on qualifications, experience, and interview performance)
• Conveyance Allowance: ₹2,500 – ₹3,000 per month

Note: No TA/DA for attending the interview.

Selection Process

• Shortlisting based on qualifications and experience
• Personal Interview to assess suitability for the post

Nature of Contract

• Duration: Initially 1 year (may be extended based on performance and requirement)
• Type: Purely contractual; no claim for permanent absorption

How to Apply

1. Apply Online Only through the official KVIC website [www.kvic.gov.in] (no postal/email/hard copies accepted).
2. Fill all details, upload required documents (photo, signature, certificates), and submit before the last date.
3. Last Date to Apply: 31 August 2025 (up to 11:00 PM)

Read the official notification carefully for detailed instructions on document size, photograph format, and signature guidelines.

Important Points to Remember

• Only full-time regular courses will be considered.
• Candidates must keep the same email and mobile number active throughout the recruitment process.
• Carry a printout of the online application and original photo ID for the interview.
• Any false information or canvassing will lead to disqualification.
• The engagement is purely temporary – KVIC reserves all rights regarding shortlisting and selection.

Why Join as a Young Professional at KVIC?

This is a golden opportunity to gain meaningful experience with a renowned government body working for rural economic development. If you are passionate, driven, and meet the eligibility criteria, apply now and set a strong foundation for your career!

For complete details and to apply:
Visit the “Vacancies” section at www.kvic.gov.in

Stay tuned to our blog for more such government job updates! Good luck to all the aspirants!

Disclaimer: Please refer to the official notification on the KVIC website for complete and authentic details. This article is for informational purposes only.

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Introduction to Smart Textiles

Smart textiles, or electronic textiles (e-textiles), are fabrics embedded with digital components such as sensors, actuators, and conductive fibers. These advanced textiles are capable of sensing and responding to environmental stimuli, providing functionality far beyond traditional garments. Smart textiles result from collaborative advances in textile engineering, electronics, computer science, and materials science.

Classification

• Passive Smart Textiles: Detects external stimuli without active response. Example: Outlast® Technology, which uses phase-change materials to absorb, store, and release heat, is integrated by brands such as Serta and Timberland.
• Active Smart Textiles: Respond dynamically to environmental input. Example: Levi’s Jacquard by Google allows users to control their smartphones via touch-sensitive jacket sleeves. ShiftWear sneakers, using embedded e-ink displays, can change design or color on demand.
• Ultra-smart Textiles: Not only sense and react, but also adapt and learn from the environment.

Current Trends in Smart Textile Innovation

1. Wearable Technology and Fashion Integration

Integration of smart sensors, actuators, and interactive technology is prominent in apparel:

• Tommy Hilfiger Xplore Collection integrates Bluetooth chips to track user activity and reward engagement.
• Samsung Galaxy Smart Ring and Oura Ring employ minimalist design to monitor sleep, heart rate, and movement.
• ShiftWear Sneakers display customizable, animated visuals on fabric through a connected app.
• Advances in textile-based electronics have prompted emerging generations of wearable products, ranging from attachable sensors (first generation) to fully integrated, responsive garments (third generation).

2. Healthcare and Biomedical Applications

Smart textiles are enabling continuous, real-time health monitoring and medical diagnostics:

• Hexoskin shirts track ECG, breathing rate, and motion for telemedicine and athletic purposes.
• Sensoria and Myant offer smart socks and shirts for cardiac monitoring via textile-based electrodes.
• KOB GmbH produces pressure-sensing bandages and medical wraps for wound management.
• Recent developments focus on biosensor integration within apparel, capable of measuring glucose, lactate, pathogens, and pollutants in real time.
• Smart compression garments automatically control pressure to treat lymphedema and similar conditions.

3. Sustainability in Smart Textiles

Eco-friendly smart textiles are increasingly adopted:

• Stella McCartney uses recycled and biodegradable fibers in temperature-regulating, moisture-wicking smart clothing.
• Other brands, like Adidas and Patagonia, are pioneering circular design and integrating smart functions with minimal environmental impact.
• Reviews emphasize sustainability as a guiding trend in smart textile innovation.

4. Military and Defense Applications

Smart textiles enhance safety and operational performance in defense:

• BAE Systems’ Broadsword Spine employs conductive fabrics to distribute power and data in tactical vests, removing the need for traditional wiring.
• U.S. Army projects and companies like EmbroTech are developing adaptive camouflage and biometric tracking uniforms.
• Exoskeletons and advanced military gear are leveraging embedded textile-based sensors for real-time health and fatigue monitoring.

5. Sports and Athletic Wear

Smart textiles are revolutionizing athletic performance and recovery:

• Athos uses integrated EMG sensors in wearable apparel to provide detailed muscle activation data for optimizing workouts.
• Under Armour Athlete Recovery Sleepwear reflects infrared energy for faster muscle recovery.
• Sensoria Smart Socks monitor running technique and deliver gait analysis using textile-integrated pressure sensors.
• EyeCan Swim offers sonar-based feedback headbands for visually impaired swimmers.
• Current research focuses on robust, flexible, and washable textile-based sensors for sports and fitness.

6. Other Emerging Commercial Applications

Startups and research labs are expanding the scope of smart textiles:

• MesoMat produces ultra-flexible conductive fibers for diverse textile applications.
• Viture One Smart Glasses utilize fabric tech in wearable headsets for AR media projection.
• MIT Media Lab’s 3DKnITS showcase novel fabrication of knit textiles that sense user posture and movement with high precision.

Cross-Sector Applications Table

Industry	Application Example	Commercial Product or Brand
Fashion	Wearables with color-changing or interactive capabilities	ShiftWear sneakers
Healthcare	Real-time biometric, ECG, or compression monitoring	Hexoskin, Sensoria, Myant
Military	Adaptive, sensor-equipped uniforms and gear	BAE Systems’ Broadsword Spine
Sports / Fitness	Performance analytics, recovery enhancement, safety for athletes	Athos, Under Armour, Sensoria
Consumer	Daily health tracking, mobile interaction, productivity	Samsung Galaxy Smart Ring

Challenges and Opportunities

Despite rapid growth, smart textiles face hurdles:

• High production costs hinder large-scale adoption, but advances in printable electronics and nanotechnology are reducing expenses.
• Limited consumer awareness slows uptake. Educational outreach is vital for widespread adoption.
• Regulatory and technical standards are required for safety, durability, and privacy.

Recent Research Highlights

• Comprehensive bibliometric mapping emphasizes the surge in global research and patent activity in smart textiles.
• Reviews focus on advanced integration technologies, sustainability, wearable biosensors, and applications across industries.
• Studies highlight breakthroughs in textile-based biosensors and electrochemical sensors for sweat and metabolite analysis.
• Innovations in shape-morphing materials, actuation, and energy harvesting are rapidly emerging.

Conclusion

Smart textiles are at the forefront of material and wearable technology innovation, with growing impact across fashion, healthcare, defense, and beyond. The integration of digital sensors, sustainable materials, and adaptive systems is transforming traditional textiles and creating new opportunities for personalized, data-driven performance and care. As research advances and costs fall, the next decade will likely see smart textiles become integral to daily life, driven by interdisciplinary progress and market adoption.

References

1. Wang, Y. (2022). Research on flexible capacitive sensors for smart textiles. Journal of Physics: Conference Series, 2181(1), 012038. https://doi.org/10.1088/1742-6596/2181/1/012038
2. Rudresh, H. M., & Das, R. (2022). Smart fabric textiles: Recent advances and challenges. Materials Today: Proceedings. Advance online publication. https://doi.org/10.1016/j.matpr.2022.02.250
3. Tian, Y., Xu, L., & Wang, Y. (2022). Smart textiles using fluid-driven artificial muscle fibers. Nature Communications, 13, 425. https://doi.org/10.1038/s41467-021-27847-0
4. Patel, M., Patel, R., & Upadhyay, S. H. (2023). Smart E-textiles: A review of their aspects and applications. Materials Today: Proceedings. Advance online publication. https://doi.org/10.1016/j.matpr.2023.07.199
5. Arefin, A., Saha, A. K., Kabir, S. M. M., Rahman, M. H., & Hasan, M. M. (2023). Smart textiles: A review and bibliometric mapping. Results in Engineering, 18, 101020. https://doi.org/10.1016/j.rineng.2023.101020
6. Hossain, M. R., Ahmed, M. R., & Alam, M. S. (2023). Smart textiles. Textile Progress, 55(2), 47–108. https://doi.org/10.1080/00405167.2023.2250651
7. Allish, S., Suthiksha, P., Pachiappan, K. M., Sathiyam, R. D., & Saniya, A. (2024). A review on recent trends in smart textiles. World Journal of Advanced Research and Reviews, 24(1), 1824–1828. https://doi.org/10.30574/wjarr.2024.24.1.3103
8. Hossain, M. T., Shahid, M. A., Limon, M. G. M., Hossain, I., & Mahmud, N. (2024). Techniques, applications, and challenges in textiles for a sustainable future. Journal of Open Innovation: Technology, Market, and Complexity, 10, 100230. https://doi.org/10.1016/j.joitmc.2024.100230
9. Azeem, M., Shahid, M., Masin, I., & Petru, M. (2025). Design and development of textile-based wearable sensors for real-time biomedical monitoring: A review. The Journal of The Textile Institute, 116(1), 80–95. https://doi.org/10.1080/00405000.2024.2318500

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Introduction

China has unveiled a groundbreaking advancement in agricultural technology: the world’s first fully automated cotton topping robot. Developed in Xinjiang, this robot promises to transform one of the most labor-intensive phases of cotton cultivation by replacing manual topping with a high-precision, laser-powered, intelligent system—delivering results at ten times the speed of human workers.

The Challenge of Cotton Topping

Cotton topping involves removing the plant’s top bud to redirect nutrients to lateral branches, boosting boll formation and yield. Traditionally, this process has been:

• Labor-intensive: Required large numbers of seasonal workers.
• Time-consuming: Manual topping is slow, inefficient, and prone to inconsistency.
• Physically demanding: Workers must bend and reach for hours in hot fields, leading to fatigue and potential errors.

The Technology Behind the Robot

The new robot was developed through a collaboration between Xinjiang University and EAVision Robotic Technologies. Key features include:

• Laser Topping: Uses a high-powered blue laser to vaporize the terminal bud without touching the plant, ensuring non-contact, non-damaging operation.
• Solid-State LiDAR & Machine Vision: Advanced sensors and AI-driven vision systems allow the robot to identify and target the correct bud, even as plants sway in the wind.
• Precision & Efficiency: Achieves 98.9% accuracy in bud detection, with less than 3% plant damage and over 82% topping success rate in field trials.
• Speed: Processes 0.4–0.53 hectares per hour, making it at least 10 times faster than manual labor.

Field Deployment and Impact

• First Large-Scale Trials: The robot has been deployed in demonstration bases across Xinjiang, a region responsible for 90% of China’s cotton production.
• Scalability: The technology is being promoted for large-scale use, with operation orders covering thousands of hectares.
• Around-the-Clock Operation: Robots can work day and night, unaffected by weather or fatigue, further increasing productivity.
• Economic Benefits: By automating topping, farmers save on labor costs and can expect higher yields due to more precise nutrient management.

Comparison: Manual vs. Robotic Topping

Feature	Manual Topping	Cotton Topping Robot
Speed	~0.27 hectares/day	0.4–0.53 hectares/hour
Consistency	Variable	Highly consistent
Labor Requirement	High	Minimal
Plant Damage	Higher risk	<3%
Accuracy	Inconsistent	98.9%
Environmental Impact	Can be damaging	Non-contact, eco-friendly

Broader Significance

This innovation marks a major step toward fully mechanized cotton farming in China. It addresses labor shortages, reduces production costs, and supports sustainable agriculture by minimizing plant damage and chemical use. The robot’s success also highlights China’s rapid progress in smart farming and its commitment to modernizing traditional industries.

Conclusion

China’s cotton topping robot is not just a technological marvel—it’s a game changer for the global cotton industry. By fully automating a once laborious task at unprecedented speed and accuracy, it sets a new standard for agricultural efficiency and sustainability.

References

• China’s new cotton topping robot fully automates laborious task at 10x speed | The Times Of Innovations
• China’s cotton topping robot promises fully automated production of Xinjiang crop | South China Morning Post
• The world’s first cotton laser topping robot successfully developed in China’s Xinjiang-China Xinjiang
• The world’s first cotton laser topping robot successfully developed in China’s Xinjiang-China Xinjiang
• The world’s first cotton laser topping robot successfully developed in China’s Xinjiang – Global Times
• China’s cotton topping robot promises fully automated production of Xinjiang crop | South China Morning Post
• Intelligent cotton topping robots busy working at cotton fields in NW China’s Xinjiang-TIANSHANNET-天山网

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As the global population ages, the need for innovative solutions to support elderly health and independence is becoming increasingly urgent. Smart garments—wearable textiles embedded with electronic sensors—are emerging as a transformative technology for elderly health monitoring and active living.

Why Smart Garments for the Elderly?

• Aging and Health Challenges: Elderly individuals are more likely to develop comorbidities such as cardiovascular disease, diabetes, and neurodegenerative disorders. Continuous health monitoring can help prevent complications and reduce hospitalizations.
• Preference for Independence: Most elderly people prefer to remain in their own homes, even if it means higher caregiver costs. Smart garments enable unobtrusive monitoring, supporting autonomy while ensuring safety.
• Rising Healthcare Costs: With the number of people over 79 expected to triple by 2060, healthcare systems face mounting pressure. Smart garments can help streamline care and reduce costs by enabling early intervention and remote monitoring.

How Do Smart Garments Work?

Smart garments incorporate biomedical sensors into comfortable, everyday clothing, allowing them to monitor vital signs such as:

• Pulse
• Body temperature
• Skin moisture (humidity)
• Breathing rhythm

The data collected is processed by embedded microcontrollers and transmitted wirelessly to aggregators like smartphones or directly to cloud platforms. This enables real-time monitoring by healthcare professionals or caregivers, and can trigger alerts in case of abnormal readings.

Key Components

• Sensors: Noninvasive, miniaturized devices for continuous monitoring of physiological parameters.
• Conductive Yarns: Special fibers (e.g., stainless steel, copper, or polymer-based) woven into textiles to transmit electrical signals without sacrificing comfort or flexibility.
• Embedded Electronics: Microcontrollers and communication modules integrated into the fabric or attached via modular connectors.
• Cloud Computing: Data storage, analysis, and decision support systems, ensuring data privacy and accessibility for authorized users.

Benefits of Smart Garments

• Comfort and Usability: Designed to be lightweight, flexible, and washable, smart garments minimize discomfort and maximize wearability.
• Continuous, Nonintrusive Monitoring: Unlike traditional hospital equipment, smart garments allow for mobility and normal daily activities.
• Early Detection and Alerts: Real-time data analysis enables early detection of health issues (e.g., heart irregularities, hypoglycemia), reducing emergency incidents1.
• Reduced Healthcare Costs: By enabling remote monitoring and timely interventions, smart garments help decrease hospital admissions and caregiver expenses.
• Data-Driven Decisions: Cloud-based analytics support healthcare providers in making informed decisions and personalizing care plans.

Challenges and Considerations

• Integration and Durability: Embedding electronics into textiles while maintaining comfort and washability is a technical challenge.
• Data Accuracy and Fault Tolerance: Textile properties can affect signal quality. Systems must be designed to handle data loss and ensure reliable readings.
• Privacy and Security: Handling sensitive health data requires robust security and privacy measures, especially when using cloud platforms.
• Power Consumption: Ensuring sufficient battery life and low power operation is essential for practical, long-term use.

Future Directions

• Advanced Materials: Research is ongoing into new fibers and coatings to improve conductivity, comfort, and durability.
• Enhanced Data Fusion: Combining data from multiple sensors and sources for more accurate health assessments and predictive analytics.
• Personalized Monitoring: Tailoring sensor configurations and alert thresholds to individual health profiles and risk factors.
• Integration with IoT and Smart Homes: Connecting smart garments with other ambient sensors and devices for a holistic approach to elderly care1.

Conclusion

Smart garments represent a promising frontier in elderly health monitoring and active living. By seamlessly blending technology with everyday clothing, they empower seniors to maintain independence, improve quality of life, and enable caregivers and healthcare providers to deliver proactive, data-driven care.

References

1. Aileni, R. M., Valderrama, A. C., & Strungaru, R. (2017). Wearable electronics for elderly health monitoring and active living. In Ambient Assisted Living and Enhanced Living Environments (pp. 247-272). Elsevier. https://doi.org/10.1016/B978-0-12-805195-5.00010-7
2. Baig, M. M., GholamHosseini, H., & Connolly, M. J. (2019). Wearable technologies for health promotion and disease prevention in older adults: Systematic scoping review and evidence map. International Journal of Medical Informatics, 123, 104-119. https://doi.org/10.1016/j.ijmedinf.2019.01.006
3. Pantelopoulos, A., & Bourbakis, N. G. (2010). A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(1), 1-12. https://doi.org/10.1109/TSMCC.2009.2032660
4. Heikenfeld, J., Jajack, A., Rogers, J., Gutruf, P., Tian, L., Pan, T., … & Kim, J. (2018). Wearable sensors: Modalities, challenges, and prospects. Lab on a Chip, 18(2), 217-248. https://doi.org/10.1039/C7LC00914C
5. Stoppa, M., & Chiolerio, A. (2014). Wearable electronics and smart textiles: A critical review. Sensors, 14(7), 11957-11992. https://doi.org/10.3390/s140711957
6. Dias, D., & Paulo Silva Cunha, J. (2018). Wearable health devices—Vital sign monitoring, systems and technologies. Sensors, 18(8), 2414. https://doi.org/10.3390/s18082414
7. Majumder, S., Mondal, T., & Deen, M. J. (2017). Wearable sensors for remote health monitoring. Sensors, 17(1), 130. https://doi.org/10.3390/s17010130
8. Stavropoulos, T. G., Papastergiou, A., Mpaltadoros, L., Nikolopoulos, S., & Kompatsiaris, I. (2020). IoT Wearable Sensors and Devices in Elderly Care: A Literature Review. Sensors, 20(10), 2826. https://doi.org/10.3390/s20102826
9. Pinheiro, G. P. M., Miranda, R. K., Praciano, B. J. G., Santos, G. A., Mendonça, F. L. L., Javidi, E., da Costa, J. P. J., & de Sousa, R. T., Jr (2022). Multi-Sensor Wearable Health Device Framework for Real-Time Monitoring of Elderly Patients Using a Mobile Application and High-Resolution Parameter Estimation. Frontiers in human neuroscience, 15, 750591. https://doi.org/10.3389/fnhum.2021.750591
10. Wenjin, H., Tajuddin, R. M., & Shariff, S. M. (2024). Construction of Smart Clothing Service System for the Health and Well-Being of the Aging Community in a Sustainable Society. Journal of Lifestyle and SDGs Review, 5(2), e02870. https://doi.org/10.47172/2965-730X.SDGsReview.v5.n02.pe02870