As people pay more attention to physical health, smart textiles with motion detection functions are favored by consumers. Up to now, developing conductive composite yarns with high strain sensing performance that can be directly integrated into fabrics through textile technology is still a challenge. Here, we prepared a highly-stretchable carbon nanotube (CNT)/thermoplastic polyurethane (TPU) composite nanofiber yarn with a breaking elongation of up to 476% by dispersing CNTs uniformly into flexible TPU matrix and multi-needle liquid-bath electrospinning. The parameters about the preparation of the composite solution, electrospinning, and twisting were discussed and analyzed. On this basis, a highly-conductive dip-coating CNT/TPU composite nanofiber yarn (1.02 kΩ/cm) was developed by simply dip-coating CNT ink. The effects of coating methods on strain sensing performance and mechanism were explored according to the twisting structure of the yarn. This yarn-based strain sensor exhibited a high relative resistance change (440%) under 140% strain with satisfactory linearity and repeatability (1250 cycles). Finally, a smart sports bandage with sports auxiliary functions for badminton, basketball, running, and medical monitoring functions for heartbeat, respiration was developed by directly sewing the composite yarn into an elastic self-adhesive bandage. In the future, this yarn will be able to endow ordinary sportswears “intelligence” through simple sewing or embroidery. 

Flexible strain sensor based on CNT/TPU composite nanofiber yarn for smart sports bandage

Background: Artificial intelligence (AI) holds the promise of supporting nurses’ clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios.
Objective: This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care.
Methods: Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria.
Results: The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail.
Conclusions: The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care–specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.

Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review.

Electrochromic devices can act as passive displays. They change their color when a low voltage is applied. Flexible and bendable hybrid textile-film electrochromic devices with poly-3,4-ethylenedioxythiophene polystyrene sulfonate (PEDOT:PSS) were prepared on polyethylene polyethylene terephthalate (PEPES) membranes using a spray coating technique. The electrolyte consisted of a gelatin glycerol mixture as host matrix and calcium chloride. Titanium dioxide was used as an ion storage layer and a carbon containing dispersion was used for the counter electrode on a polyester rip-stop fabric. The sheet resistance of PEDOT:PSS on PEPES was 500 Ohm/sq. A 5 × 5 electrochromic matrix with individually addressable pixels was successfully designed and assembled. The switching time of the pixels was 2 s at a voltage of 2.0 V directly after assembling. The use of titanium dioxide as ion storage also increased the contrast of the dark-blue reduced electrochromic layer. Coloration was not self-sustaining. The PEDOT:PSS layer needed a constant low voltage of at least 0.5 V to sustain in the dark-blue reduced state. The switching time increased with time. After 12 months the switching time was ~4 s at a voltage of 2.8 V. The addition of glycerol into the electrolyte extended the lifetime of a non-encapsulated textile electrochromic cell, because moisture is retained in the electrolyte. Charge carriers can be transported into and out of the electrochromic layer.

https://www.mdpi.com/1424-8220/20/19/5691/pdf

Textile Based Electrochromic Cells Prepared with PEDOT: PSS and Gelled Electrolyte.

Embroidery is often the preferred technology when rigid circuit boards need to be connected to sensors and electrodes by data transmission lines and integrated into textiles. Moreover, conventional circuit boards, like Lilypad Arduino, commonly lack softness and flexibility. One approach to overcome this drawback can be flexible sequins as a substrate carrier for circuit boards. In this paper, such an approach of the development of flexible and functional sequins and circuit boards for wearable textile applications using subtractive and additive technology is demonstrated. Applying these techniques, one-sided sequins and circuit boards are produced using wax printing and etching copper-clad foils, as well as using dual 3D printing of conventional isolating and electrically conductive materials. The resulting flexible and functional sequins are equipped with surface mounted devices, applied to textiles by an automated embroidery process and contacted with a conductive embroidery thread.

https://www.mdpi.com/1996-1944/14/10/2633/pdf

Development of Flexible and Functional Sequins Using Subtractive Technology and 3D Printing for Embroidered Wearable Textile Applications.

Sleep posture, as a crucial index for sleep quality assessment and pressure ulcer prevention, has been widely studied for medical diagnoses and sleep disease treatment. In this paper, an unobtrusive smart mat system for sleep posture recognition is proposed, which is based on a dense flexible sensor array and printed electrodes and along with an algorithmic framework. With the dense flexible sensor array, the system offers a comfortable and high-resolution solution for long-term pressure sensing. Meanwhile, compared with other large-area and low-density mat systems, it reduces the area to minimize manufacturing cost and computational complexity, while also increases the density of the sensor to improve accuracy. To distinguish the sleep postures, the algorithmic framework that includes pre-processing, feature extraction, and posture classification is developed. Pilot studies in two scenarios including subject-dependent and subject- independent classification are performed with 7 persons for 4 different postures recognition. The experimental results show that the accuracy of the smart mat system can achieve over 78% using Support Vector Machines (SVMs) and k-Nearest Neighbor (kNN) for the subject-independent scenario. For the subject-dependent scenario, the accuracy can reach over 95%. It proves that the proposed method can recognize different sleep postures effectively.

Unobtrusive Smart Mat System for Sleep Posture Recognition

Demographic change in the next few years will lead to a pronounced disparity in generation distribution. Hence there is a need to develop intelligent systems to support and maintain the autonomy of the elderly at home. A high priority in this case assumes the preparation-free acquisition of vital signs and patient parameters in long-term monitoring systems to detect early changes or deterioration in health. It is thus possible to initiate treatment of a disease at an early stage. One way to carry out a long-term monitoring of vital signs at home is based on the functionalization of furniture, for example, through the use of suitable sensors in chairs [1] and beds [2, 3] to derive various patient parameters. In addition to monitoring basic parameters, e.g. the heart rate and respiratory activity, it is also possible to access information regarding motion or sleep patterns by means of pattern recognition systems. In addition to the challenge of building a suitable pattern recognition system there is a need for corresponding training data to create reference patterns. Typically, the necessary sensor data for the reference pattern training is generated in time-consuming sessions with real people. In this paper, a novel approach is presented, which provides a multi-stage model to create artificial training or test data. The model can be used as a supporting tool in the development of posture recognition systems and to create artificial data for training and testing.

A novel approach to creating artificial training and test data for an HMM based posture recognition system

Alternating-current (AC) electroluminescent (EL) devices on fabrics with high brightness are presented The EL-devices were fabricated via knife coating; inorganic luminous pigments are based on zinc sulfide Effects of parameters influencing the brightness were investigated These parameters are the AC-voltage, AC-frequency, AC-waveform, layer composition of the luminous capacitor and the fabric Introducing a flexible reflecting dielectric layer enhances the light yield on fine woven fabrics with green luminous pigment This can be achieved with small concentrations of reflective white pigments such as titanium dioxide, maintaining the flexibility and bendability of the textile substrate The produced luminous textiles are investigated as a possible replacement for light boxes used in the therapy of seasonal affective disorder (SAD) A high luminous emittance and a high portion of short and energy rich wavelengths are necessary for the treatment Contrarily to state-of-the-art light boxes a higher acceptance of light therapy is expected, because a luminous textile can be integrated easily and unremarkably into the living environment

Electroluminescent textile for therapeutic applications

Development of a HMM based posture recognition system to derive patient activity from a force sensor functionalized nursing bed

In an adverse acoustic environment the human brain can form a sensible sentence only from recognized fragments of a spoken text It completes parts that are missing and corrects defective parts This mechanism is called “close from the context” For a recognition system which basically has no context-related information, the same “skills” would be a very helpful extension In this work, we show the improvement through an additional recognition module that uses a retrospective process which is able to identify and correct errors in the initial recognition results The whole retrospective improvement is designed for a Hidden Markov Model (HMM) based recognition system and is realized for a functionalized nursing bed The recognition system is able to recognize the patient's body posture and actions in the bed and can be used eg for sleep pattern analysis or decubitus ulcer prevention The functionalized nursing bed is designed as a preparation free measuring system which is able to derive biomechanical signals from the patient For this purpose, it is very important to get a minimum error rate of the used recognition system to avoid false positive or false negative recognition results The system works as follows In a first step, the retrospective system analyzes the initial recognition results and classifies them into correct or incorrect results This classifying is based on probability based distance measure in combination with a context based method Subsequently the system extracts incorrect marked parts and tries to correct single errors with context based methods initially In a second step, double errors are corrected by a combination of context based and temporal mean values methods This work focusses on the development and combination of the two methods to correct double errors to improve the initial system

Improvements of a retrospective analysis method for a HMM based posture recognition system in a functionalized nursing bed

Abstract Motion capture systems or MoCap systems are used for game development and in the field of sports for the assessment and digitalization of human movement. Furthermore, MoCap systems are also used in the medical and therapeutic field for the analysis of human movement patterns. As examples gait analysis or examination of the musculoskeletal system and its function should be mentioned. Most application relate to a specific person and their movement or to the comparison of movements of different people. Within the scope of this paper an averaged motion sequence is supposed to be generated from MoCap data in order to be able to use it in the field of biomechanical modeling and simulation. For the averaging of individual movement sequences of different persons a Hidden Markov Model (HMM) based approach is presented.

/pdf/an-hmm-based-averaging-approach-for-creating-mean-motion-448shc5oet.pdf

An HMM-based averaging approach for creating mean motion data from a full-body Motion Capture system to support the development of a biomechanical model

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