Applications of Smart Textiles in Post-Stroke Rehabilitation

Korzeniewska, Ewa; Krawczyk, A.; Mróz, Józef; Wyszyńska, Elżbieta; Zawiślak, Rafał

doi:10.3390/s20082370

Cited by 12 publications

(8 citation statements)

References 45 publications

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“…A recent work by Korzeniewska et al also developed a similar strategy to produce Ag‐deposited nonwoven PU‐fabric sensor by employing a PVD technique using a Classic 250 vacuum chamber based on the Pfeiffer Vacuum system; however, no comprehensive investigation was conducted on the resultant textile sensor. [ 101 ]…”

Section: Form Factors and Fabrications Of Textile Sensorsmentioning

confidence: 99%

Electronic Textile Sensors for Decoding Vital Body Signals: State‐of‐the‐Art Review on Characterizations and Recommendations

Shuvo

Shah

Dağdeviren

2022

Advanced Intelligent Systems

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The digitization of textiles (textronics) has created new opportunities for integration with conformable sensors to enable unobtrusive, noninvasive, and continuous decoding of vital body signals. This article provides an in‐depth review of the materials and fabrication methodologies used for textronic sensors per their form‐factor in the textile manufacturing process chain—fiber, yarn, fabric, and apparel. Next, it analyzes the performance characterization techniques currently used for these sensors and highlights the needs for standardized test methods in the following aspects: biocompatibility, thermal and tactile comfort, aging, and operation of the biomedical sensing modality at standard human stretch. It also identifies the significance of pretreatment and conditioning reporting of the textile form‐factors based on their impact on mechanical and electric performance of the textronic sensor. The study concludes by recommending a universal testing roadmap for textronic sensors which is expected to veritably complement the work of different standardization committees, including CEN TC‐248/WG‐31, IEC TC‐124, ASTM D13.50, and AATCC RA111.

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Section: Form Factors and Fabrications Of Textile Sensorsmentioning

confidence: 99%

Electronic Textile Sensors for Decoding Vital Body Signals: State‐of‐the‐Art Review on Characterizations and Recommendations

Shuvo

Shah

Dağdeviren

2022

Advanced Intelligent Systems

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“…For the final verification of the generalization capacity of the newly developed LSTM network, additional validation was carried out using real data. In our laboratory, a prototype of a special vest has been developed, which allows you to perform tomographic measurements and reconstructions, as well as ECG measurements [51,52]. Figure 14 shows the arrangement of electrodes seen from the inside of the vest.…”

Section: Model Validation In Real Conditionsmentioning

confidence: 99%

The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification

Kłosowski

Rymarczyk

Wójcik³

et al. 2020

Electronics

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This paper refers to the method of using the deep neural long-short-term memory (LSTM) network for the problem of electrocardiogram (ECG) signal classification. ECG signals contain a lot of subtle information analyzed by doctors to determine the type of heart dysfunction. Due to the large number of signal features that are difficult to identify, raw ECG data is usually not suitable for use in machine learning. The article presents how to transform individual ECG time series into spectral images for which two characteristics are determined, which are instantaneous frequency and spectral entropy. Feature extraction consists of converting the ECG signal into a series of spectral images using short-term Fourier transformation. Then the images were converted using Fourier transform again to two signals, which includes instantaneous frequency and spectral entropy. The data set transformed in this way was used to train the LSTM network. During the experiments, the LSTM networks were trained for both raw and spectrally transformed data. Then, the LSTM networks trained in this way were compared with each other. The obtained results prove that the transformation of input signals into images can be an effective method of improving the quality of classifiers based on deep learning.

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“…Due to their electrical properties, woven and knitted fabrics belonging to flat textile objects can be used as textile sensors [ 1 , 2 , 3 , 4 ]. Most textile-based sensors rely on a change in electrical resistance.…”

Section: Introductionmentioning

confidence: 99%

A Mixing Model for Describing Electrical Conductivity of a Woven Structure

Tokarska

2022

Materials

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The main aim of the research was to describe electro-conductive woven structures by specifying the phases’ exponents using the generalised Archie’s law. Special woven structures were designed to transfer Archie’s model to the textile object. The woven structure was treated as a complex multiphase mixture. The structure was composed of two conducting phases (strips and strip contacts) and one non-conducting phase (pore space). It was found that the designed structures were characterised by the phases’ exponents that exceeded the value of 2, which denoted low connectivity in the conductive phases. A qualitative and quantitative description of the woven structure was feasible, i.e., the connectedness and the connectivity, respectively. The connectedness of both of the phases was dependent on the material from which the structure was designed. The fraction of each of the phases involved in the current conductivity was important. The connectivity connected with structure density, in varying degrees, affected the electro-conductive properties of the woven structure. It was important how the phases were arranged in the whole composite. It was found that the strips’ contact phases played an important role in the structure of the composite.

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Applications of Smart Textiles in Post-Stroke Rehabilitation

Cited by 12 publications

References 45 publications

Electronic Textile Sensors for Decoding Vital Body Signals: State‐of‐the‐Art Review on Characterizations and Recommendations

Electronic Textile Sensors for Decoding Vital Body Signals: State‐of‐the‐Art Review on Characterizations and Recommendations

The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification

A Mixing Model for Describing Electrical Conductivity of a Woven Structure

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