Infant's spontaneous movements mirror integrity of brain networks, and thus also predict the future development of higher cognitive functions. Early recognition of infants with compromised motor development holds promise for guiding early therapies to improve lifelong neurocognitive outcomes. It has been challenging, however, to assess motor performance in ways that are objective and quantitative. Novel wearable technology has shown promise for offering efficient, scalable and automated methods in movement assessment. Here, we describe the development of an infant wearable, a multi-sensor smart jumpsuit that allows mobile data collection during independent movements. A deep learning algorithm, based on convolutional neural networks (CNNs), was then trained using multiple human annotations that incorporate the substantial inherent ambiguity in movement classifications. We also quantify the substantial ambiguity of a human observer, allowing its transfer to improving the automated classifier. Comparison of different sensor configurations and classifier designs shows that four-limb recording and end-toend CNN classifier architecture allows the best movement classification. Our results show that quantitative tracking of independent movement activities is possible with a human equivalent accuracy, i.e. it meets the human inter-rater agreement levels in infant posture and movement classification.

Cotton waste dyed with different vat and reactive dyes is systematically upcycled to colored cellulose fibers via dry-jet wet spinning.

Background Early neurodevelopmental care needs better, effective and objective solutions for assessing infants’ motor abilities. Novel wearable technology opens possibilities for characterizing spontaneous movement behavior. This work seeks to construct and validate a generalizable, scalable, and effective method to measure infants’ spontaneous motor abilities across all motor milestones from lying supine to fluent walking. Methods A multi-sensor infant wearable was constructed, and 59 infants (age 5–19 months) were recorded during their spontaneous play. A novel gross motor description scheme was used for human visual classification of postures and movements at a second-level time resolution. A deep learning -based classifier was then trained to mimic human annotations, and aggregated recording-level outputs were used to provide posture- and movement-specific developmental trajectories, which enabled more holistic assessments of motor maturity. Results Recordings were technically successful in all infants, and the algorithmic analysis showed human-equivalent-level accuracy in quantifying the observed postures and movements. The aggregated recordings were used to train an algorithm for predicting a novel neurodevelopmental measure, Baba Infant Motor Score (BIMS). This index estimates maturity of infants’ motor abilities, and it correlates very strongly (Pearson’s r = 0.89, p < 1e-20) to the chronological age of the infant. Conclusions The results show that out-of-hospital assessment of infants’ motor ability is possible using a multi-sensor wearable. The algorithmic analysis provides metrics of motility that are transparent, objective, intuitively interpretable, and they link strongly to infants’ age. Such a solution could be automated and scaled to a global extent, holding promise for functional benchmarking in individualized patient care or early intervention trials.

Purpose -While aiming to create methods for fibre recycling, the question of colours in waste textiles is also in focus; whether the colour should be kept or should be removed while recycling textile fibre. More knowledge is needed for colour management in a circular economy approach.Design/methodology/approach -The research included the use of different dye types in a cotton dyeing process, the process for decolourizing and the results. Two reactive dyes, two direct dyes and one vat dye were used in the study. Four chemical treatment sequences were used to evaluate colour removal from the dyed cotton fabrics, namely, HCE-A, HCE-P-A, HCE-Z-P-A and HCE-Y-A.Findings -The objective was to evaluate how different chemical refining sequences remove colour from direct, reactive and vat dyed cotton fabrics, and how they influence the specific cellulose properties. Dyeing methods and the used refining sequences influence the degree of colour removal. The highest achieved final brightness of refined cotton materials were between 71 and 91 per cent ISO brightness, depending on the dyeing method used.Research limitations/implications -Only cotton fibre and three different colour types were tested. © Marjo Määttänen, Sari Asikainen, Taina Kamppuri, Elina Ilen, Kirsi Niinimäki, Marjaana Tanttu and Ali Harlin. Published by Emerald Group Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at Practical implications -With cotton waste, it appears to be easier to remove the colour than to retain it, especially if the textile contains polyester residues, which are desired to be removed in the textile refining stage.Originality/value -Colour management in the CE context is an important new track to study in the context of the increasing amount of textile waste used as a raw material.

Research and development of new products in the smart textile field is growing rapidly because of versatile application areas. There is an extensive focus on the integration of electronics into textiles. However, if different fields are merged, as here, the sustainability and recycling issues might descend into even more complex systems. The paper reviews current research and development conducted on the end-of-life solutions for electronic textiles (e-textiles). Chosen papers had to be peer-reviewed, written in English, and address the end-of-life issue for electronic-based smart textiles. The search resulted in 18 publications, which indicates a low amount of research but also the serious lack of legislation and actual solutions emerged in this multidisciplinary field. Three main themes were found: smart textile services, eco-design strategy and educating guidelines. Authors suggest taking urgent actions by preventive steps in combining current electronics and textile waste management systems into one standard for e-textiles. TEXTILE INTEGRATED ELECTRONICS IN ECO-DESIGN CONTEXTVan Langenhove and Hertleer (2004) state "smart textiles are fabrics or apparel products that contain technologies, which sense and react to the conditions of the environment they are exposed to, thus allowing the wearer to experience increased functionality". The conditions or stimuli can be electrical, mechanical, thermal, chemical, or a combination of these. The main research in the smart textile field is indefinitely focused on improving the integration level, from moving from garment level to fibre level (Schneegass & Amft, 2017). For example, Katashev et al. (2019) replaced conventional EIT (electrical impedance tomography) electrodes with knitted textiles electrodes where conductive parts are on fibre level. Electronic textiles (e-textiles) are a subcategory of smart textiles that are based on electronics and conductive textiles, e.g. silver-coated fabrics or yarns, conductive inks and/or conductive polymers (Stoppa & Chiolerio, 2014). The E-textiles system includes the traditional electronic components, for example, printed circuit boards (PCB) and non-textile sensors that include ceramics in addition to metals and plastics.In e-textile products, the level of integration has a remarkable influence on the materials' recyclability and the end-of-life solutions of the product. As a result, e-textiles require specified end-of-life treatment methods and standardized waste processing. It is vital to tackle the topic early to avoid mistakes made in textile waste management. End of product lifetime or EOL (End-of-Life) of the product is the point when it is not usable anymore or just not needed by the user anymore. Thus, it should be reused, recycled, remanufactured or