Design and Development of Smart-Jacket for Posture Detection

Randhawa, Princy; Shanthagiri, Vijay; Mour, Rishabh; Kumar, Ajay

doi:10.1109/icscee.2018.8538384

Cited by 11 publications

(3 citation statements)

References 3 publications

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“…The paper also presents the placement of sensors on the subject using fabric pressure and stretch sensors and 9 DOF accelerometer. The jacket in this study was constructed from scratch and worn by the subject to achieve human posture data collection using fabric sensors that were woven on the jacket (Randhawa et al, 2018). This is the first of the kind study where fabric sensors were used to classify normal and violent response.…”

Section: Discussionmentioning

confidence: 99%

“…The pressure sensor was made using Veloster material which acts as a semi-permeable layer between two pieces of conductive fabric to create pressure on it (Yang et al, 2015). After experimentation, it was observed that the response to pressure was not steady using Veloster (Randhawa et al, 2018). To counter this, an additional fabric pressure sensor was built by sandwiching the pressure-sensitive fabric between the conductive fabrics, which shows a reliable response to pressure using trial and error (Randhawa et al, 2017).…”

Section: Hardware Descriptionmentioning

confidence: 99%

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Human activity detection using machine learning methods from wearable sensors

Randhawa

Shanthagiri

Kumar

et al. 2020

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Purpose The paper aims to develop a novel method for the classification of different physical activities of a human being, using fabric sensors. This method focuses mainly on classifying the physical activity between normal action and violent attack on a victim and verifies its validity. Design/methodology/approach The system is realized as a protective jacket that can be worn by the subject. Stretch sensors, pressure sensors and a 9 degree of freedom accelerometer are strategically woven on the jacket. The jacket has an internal bus system made of conductive fabric that connects the sensors to the Flora chip, which acts as the data acquisition unit for the data generated. Different activities such as still, standing up, walking, twist-jump-turn, dancing and violent action are performed. The jacket in this study is worn by a healthy subject. The main phases which describe the activity recognition method undertaken in this study are the placement of sensors, pre-processing of data and deploying machine learning models for classification. Findings The effectiveness of the method was validated in a controlled environment. Certain challenges are also faced in building the experimental setup for the collection of data from the hardware. The most tedious challenge is to collect the data without noise and error, created by voltage fluctuations when stretched. The results show that the support vector machine classifier can classify different activities and is able to differentiate normal action and violent attacks with an accuracy of 98.8%, which is superior to other methods and algorithms. Practical implications This study leads to an understanding of human physical movement under violent activity. The results show that data compared with normal physical motion, which includes even a form of dance is quite different from the data collected during violent physical motion. This jacket construction with woven sensors can capture every dimension of the physical motion adding features to the data on which the machine learning model will be built. Originality/value Unlike other studies, where sensors are placed on isolated parts of the body, in this study, the fabric sensors are woven into the fabric itself to collect the data and to achieve maximum accuracy instead of using isolated wearable sensors. This method, together with a fabric pressure and stretch sensors, can provide key data and accurate feedback information when the victim is being attacked or is in a normal state of action.

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Section: Discussionmentioning

confidence: 99%

Section: Hardware Descriptionmentioning

confidence: 99%

Human activity detection using machine learning methods from wearable sensors

Randhawa

Shanthagiri

Kumar

et al. 2020

Self Cite

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“…The first requirement was a jacket in which various fabric sensors woven at strategic locations from where physical movements could be recorded. Data are analysed acquired from body-worn sensors such as pressure sensors stretch sensor and accelerometer, as they were recorded in the dataset and it gives the data in the form of voltage fluctuations while stretching and bending (Randhawa et al, 2018). To achieve the same, an in house smart jacket was built as shown in the Figure 1 and Table 1 Shows the location of these units.…”

Section: Introductionmentioning

confidence: 99%

Identification of Violent Response with Stretch Sensor Data from a Smart-Jacket using Naïve Bayes Algorithm

2019

IJITEE

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In this paper, a smart-jacket using stretch sensors, pressure sensors was built for purpose of generating body-movements data and in order to record different kinds of signals and the distribution of the same on the jacket. Every degree of motion, when exercised, generates voltage changes in the stretch sensors as it is its property to do so. This data is collected in a flora chip set, which is Arduino based. The collected data is processed, pruned and filtered for outliers. This paper concerns with a supervised learning algorithm called Naive Bayes, which is applied over independent datasets, meaning one set of observation has no direct relations to each other. The placement of sensor are on the shoulders and elbows and the responses from each are independent of each other. Using Naive Bayes, the date has been classified for the violent response and the normal action.

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Wearables, E-textiles, and Soft Robotics for Personalized Medicine

Martínez

2023

Springer Handbooks

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Design and Development of Smart-Jacket for Posture Detection

Cited by 11 publications

References 3 publications

Human activity detection using machine learning methods from wearable sensors

Human activity detection using machine learning methods from wearable sensors

Identification of Violent Response with Stretch Sensor Data from a Smart-Jacket using Naïve Bayes Algorithm

Wearables, E-textiles, and Soft Robotics for Personalized Medicine

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