MyWear: A Novel Smart Garment for Automatic Continuous Vital Monitoring

Sethuraman, Sibi Chakkaravarthy; Kompally, Pranav; Mohanty, Saraju P.; Choppali, Uma

doi:10.1109/tce.2021.3085888

Cited by 35 publications

(11 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The traditional domestic garment industry is labour-intensive and has always suffered from low production levels, serious homogenisation of products, and low brand awareness. In recent years, with the emergence and rapid development of big data [ 21 ], cloud computing [ 22 ], intelligent manufacturing [ 23 ], and e-commerce [ 24 ], it has become possible to shift from high-end tailoring to large-scale personalised tailoring. This possibility will accelerate the transformation of the garment industry towards the Internet.…”

Section: Introductionmentioning

confidence: 99%

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

Yan

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

With the rapid economic development and rising consumption levels in recent years, people are becoming more and more demanding in terms of style and fashion of clothes. As a result, customer demand for personalised clothing is increasing and the need to respond quickly to consumer demands is also becoming a competitive issue for clothing companies. The automation and intelligence of the garment design and production process is an important part of the implementation of intelligent manufacturing in the garment industry and a necessary way to transform and upgrade the garment industry. Successful clothing styles always have a distinct style identity. The style of the garment can not only conveys the designer's vision but also express the emotional needs of the consumer. In contrast, traditional garment design involves only designers and a single style. With so many styles available, the user has only been able to combine them repeatedly and has not been able to create an innovative design. In addition, apparel design and product development is still a highly empirical task. To be specific, most apparel companies can only respond to a rapidly changing market by increasing the number of designers. However, this blind expansion of staff inevitably leads to increased production costs. As a result, how to effectively develop garment products without relying on the empirical knowledge of garment designers is one of the important issues in achieving intelligent manufacturing in garment enterprises. With the rapid development of computer and network technologies, artificial intelligence, machine learning, and expert systems are widely used in various industries. Nevertheless, the application of these advanced technologies in the field of garment design is still not deep enough. This is mainly due to the uncertainty and imprecision of garment design knowledge. Also, with the rapid development of the fashion industry and the arrival of the trend of personalisation, people's demand for clothing has gradually shifted from mass appeal in terms of comfort and aesthetics to personalisation in terms of self-polishing and temperament. The personalisation of clothing encompasses a wide range of preferences in terms of style and fit. The bottom-up design process and the relatively independent setup of functional modules in traditional clothing technology have prevented the different design levels from being interlinked. This does not reflect the composition of the garment elements in the process of forming features and makes it difficult to grasp the overall design state of the garment. Therefore, in order to address these above issues, this paper proposes a garment design model based on the Bayesian classifier and decision tree algorithm to investigate how computer technologies can be applied to model garment design knowledge. This model can enable inexperienced designers to develop garment products quickly and efficiently to meet the customisation needs of customers, thus enhancing the market competitiveness of garment enterprises.

show abstract

Section: Introductionmentioning

confidence: 99%

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

Yan

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…This is an automatic IoT Edge level system, which provides information on the emotional state of a person along with the classification of eating behaviors to Normal-Eating or Stress-Eating. Recently, wearable devices including accelerometer, gyroscope, activity tracker, and electrocardiogram (ECG) [19]. become very popular for monitoring and managing the personal health status such as daily activities, detection of falling event, cardiac anomaly and arrhythmia [20,21].…”

Section: Consumer Electronics For Smart Healthcarementioning

confidence: 99%

“…Coronavirus disease (COVID- 19) is a respiratory tract infectious disease that has spread across the world [1,2]. Coronaviruses belong to such a family of viruses whose infection can cause complications that vary from a cold to shortness arXiv:2106.05861v1 [eess.IV] 9 Jun 2021…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CoviLearn: A Machine Learning Integrated Smart X-Ray Device in Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19

2022

Self Cite

View full text Add to dashboard Cite

The pandemic of novel Coronavirus Disease 2019 (COVID-19) is widespread all over the world causing serious health problems as well as serious impact on the global economy. Reliable and fast testing of the COVID-19 has been a challenge for researchers and healthcare practitioners. In this work, we present a novel machine learning (ML) integrated X-ray device in Healthcare Cyber-Physical System (H-CPS) or smart healthcare framework (called “CoviLearn”) to allow healthcare practitioners to perform automatic initial screening of COVID-19 patients. We propose convolutional neural network (CNN) models of X-ray images integrated into an X-ray device for automatic COVID-19 detection. The proposed CoviLearn device will be useful in detecting if a person is COVID-19 positive or negative by considering the chest X-ray image of individuals. CoviLearn will be useful tool doctors to detect potential COVID-19 infections instantaneously without taking more intrusive healthcare data samples, such as saliva and blood. COVID-19 attacks the endothelium tissues that support respiratory tract, and X-rays images can be used to analyze the health of a patient’s lungs. As all healthcare centers have X-ray machines, it could be possible to use proposed CoviLearn X-rays to test for COVID-19 without the especial test kits. Our proposed automated analysis system CoviLearn which has 98.98% accuracy will be able to save valuable time of medical professionals as the X-ray machines come with a drawback as it needed a radiology expert.

show abstract

“…Paiva et al (2018) described the design process for developing a cycling garment, with embedded electrodes for heart rate measurement to widen the possibilities of health and performance monitoring in a practical and unobtrusive way. Sethuraman et al (2021) presented the design and development of an intelligent garment called MyWear that continually monitors and collects physiological data, which can be used to analyze muscle movement, pressure levels, heart rate deviations and send all the data to the cloud. It can also predict the risk of heart failure with the in-built alert system and notify the associated medical officials if necessary.…”

Section: Robotic Embroidery System For Electronic Textilesmentioning

confidence: 99%

The application of robotics and artificial intelligence in embroidery: challenges and benefits

Chen

Su²,

He³

et al. 2022

View full text Add to dashboard Cite

Purpose Embroidery as a textile embellishment technique plays an important role in people's daily life. Esthetic embroidery artworks possess cultural values. With the development of robotics and artificial intelligence (AI), these technologies have been studied and applied in the embroidery process. This study aims to survey how these technologies facilitate embroidery from different aspects. Design/methodology/approach This paper surveys how the technologies of robotics and AI are applied in the embroidery field. The applications are mainly reviewed from three aspects: computerized robotic embroidery systems has been widely used for the mass production of embroidered textiles, the advanced technological systems and techniques have greatly facilitated the development of smart textiles and the artificial intelligence plays an important role in the inheritance, innovation and protection of traditional handicraft artwork of embroidery. Findings The programmable robotic embroidery machines have greatly improved the production efficiency of embroidered textiles and promoted the development of electronic textiles. The AI, mainly the deep learning technology, brings significant benefits to esthetic embroidery creation. Technology-based embroidery has become a hot research topic in the field of textiles. Originality/value This paper summarizes the application of robotics and AI technologies in the field of embroidery, which provides readers a comprehensive and systematic understanding about the research progress of modern technology-oriented embroidery. This helps readers gain inspiration from the technology perspectives.

show abstract

MyWear: A Novel Smart Garment for Automatic Continuous Vital Monitoring

Cited by 35 publications

References 38 publications

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

Garment Design Models Combining Bayesian Classifier and Decision Tree Algorithm

CoviLearn: A Machine Learning Integrated Smart X-Ray Device in Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19

The application of robotics and artificial intelligence in embroidery: challenges and benefits

Contact Info

Product

Resources

About