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Devices for step estimation are body-worn devices used to compute steps taken and/or distance covered by the user. Even though textiles or clothing are foremost to come to mind in terms of articles meant to be worn, their prominence among devices and systems meant for cadence is overshadowed by electronic products such as accelerometers, wristbands and smart phones. Athletes and sports enthusiasts using knee sleeves should be able to track their performances and monitor workout progress without the need to carry other devices with no direct sport utility, such as wristbands and wearable accelerometers. The purpose of this study thus is to contribute to the broad area of wearable devices for cadence application by developing a cheap but effective and efficient stride measurement system based on a knee sleeve.

A textile strain sensor is designed by weft knitting silver-plated nylon yarn together with nylon DTY and covered elastic yarn using a 1 × 1 rib structure. The area occupied by the silver-plated yarn within the structure served as the strain sensor. It worked such that, upon being subjected to stress, the electrical resistance of the sensor increases and in turn, is restored when the stress is removed. The strip with the sensor is knitted separately and subsequently sewn to the knee sleeve. The knee sleeve is then connected to a custom-made signal acquisition and processing system. A volunteer was employed for a wearer trial.

Experimental results establish that the number of strides taken by the wearer can easily be correlated to the knee flexion and extension cycles of the wearer. The number of peaks computed by the signal acquisition and processing system is therefore counted to represent stride per minute. Therefore, the sensor is able to effectively count the number of strides taken by the user per minute. The coefficient of variation of over-ground test results yielded 0.03%, and stair climbing also obtained 0.14%, an indication of very high sensor repeatability.

The study was conducted using limited number of volunteers for the wearer trials.

By embedding textile piezoresistive sensors in some specific garments and or accessories, physical activity such as gait and its related data can be effectively measured.

To the best of our knowledge, this is the first application of piezoresistive sensing in the knee sleeve for stride estimation. Also, this study establishes that it is possible to attach (sew) already-knit textile strain sensors to apparel to effectuate smart functionality.

The purpose of this research is to ascertain the feasibility of fabricating polymer optical fibers (POFs) based textile structures by knitting with Polymethylmethacrylate (PMMA) based optical fibers for textile sensor application. It has long been established that by using the principles of physics, POFs have the capability to function as sensors, detecting strain, temperature and other variables. However, POF applications such as strain and pressure sensing using knitting techniques has since not been very successful due to a number of reasons. Commercially available PMMA-based optical fibers tend to be fragile and susceptible to breakages when subjected to stress during the knitting processes. Also light transmitted within these fibers is prone to leakage due to the curvature that results when optical fibers are interlaced or interlooped within fabric structures.

Using Stoll’s multi-gauge CMS 350 HP knitting machine, five fabric structures namely, 1 × 4 float knit structure, tunnel inlay knit structure, 3:1 fleece fabric and 2:1 fleece fabric structure respectively were used to knit sensor samples. The samples were subsequently tested for length of illumination and sensitivity relative to applied pressure.

The results of this preliminary study establish that embedding plastic optical fibers into a knitted structure during the fabric formation process for soft strain sensor application possible. The best illumination performance was recorded for tunnel inlay structure which had an average of 94 cm course length of POF being illuminated. Sensor sensitivity experiments also establish that the relative spectral intensity of the fiber is sensitive to both light and pressure. Problems encountered and recommendations for further research have also been discussed and proffered.

Due to resource limitations, an innovative technique (use of precision weight set) was used to apply pressure to the sensors. Consequently, information regarding the extent of corresponding sensor deformation has not been used in this initial analysis.

Because the fundamental step toward finding a solution to any engineering problem is the acquisition of reliable data, and considering the fact that most of the popular technologies used for soft textile sensors are still bedeviled with the problem of signal instability and noise, the success of this application thus has the tendency to promote the wide spread adoption of POF sensors for smart apparel applications.

As far as research on soft strain sensors is concerned, to the best of the authors’ knowledge, this is the first study to have attempted to knit deformable sensors using commercially available POFs.

At the moment, in terms of both research and commercial products, smart shoe technology and applications seem not to attract the same magnitude of attention compared to smart garments and other smart wearables such as wrist watches and wrist bands. The purpose of this study is to fill this knowledge gap by discussing issues regarding smart shoe sensing technologies, smart shoe sensor placements, factors that affect sensor placements and finally the areas of smart shoe applications.

Through a review of relevant literature, this study first and foremost attempts to explain what constitutes a smart shoe and subsequently discusses the current trends in smart shoe applications. Discussed in this study are relevant sensing technologies, sensor placement and areas of smart shoe applications.

This study outlined 13 important areas of smart shoe applications. It also uncovered that majority of smart shoe functionality are physical activity tracking, health rehabilitation and ambulation assistance for the blind. Also highlighted in this review are some of the bottlenecks of smart shoe development.

To the best of the authors’ knowledge, this is the first comprehensive review paper focused on smart shoe applications, and therefore serves as an apt reference for researchers within the field of smart footwear.

The use of conductive yarns or wires to design and construct fabric-based strain sensors is a research area that is gaining much attention in recent years. This is based on a profound theory that conductive yarns will have a variation in resistance if subjected to tension. What is not clear is to which types of conductive yarns are most suited to delivering the right sensitivity. The purpose of this paper is to look at strain sensors knitted with conductive composite and coated yarns which include core spun, blended, coated and commingled yarns. The conductive components are stainless steel and silver coating respectively with polyester as the nonconductive part. Using Stoll CMS 530 flat knitting machine, five samples each were knitted with the mentioned yarn categories using 1×1 rib structure. Sensitivity tests were carried out on the samples. Piezoresistive response of the samples reveals that yarns with heterogeneous external structures showed both an increase and a decrease in resistance, whereas those with homogenous structures responded linearly to stress. Stainless steel based yarns also had higher piezoresistive range compared to the silver-coated ones. However, comparing all the knitted samples, silver-coated yarn (SCY) proved to be more suitable for strain sensor as its response to tension was unidirectional with an appreciable range of change in resistance.

Conductive composite yarns, namely, core spun yarn (CSY1), core spun yarn (CSY2), silver-coated blended yarn (SCBY), staple fiber blended yarn (SFBY) and commingled yarn (CMY) were sourced based on specifications and used to knit strain sensor samples. Electro-mechanical properties were investigated by stretching on a fabric tensile machine to ascertain their suitability for a textile strain sensor.

In order to generate usable signal for a strain sensor for a conductive yarn, it must have persistent and consistent conductive links, both externally and internally. In the case of composite yarns such as SFBY, SCBY and CMY where there were no consistent alignment and inter-yarn contact, resistance change fluctuated. Among all six different types of yarns used, SCY presented the most suitable result as its response to tension was unidirectional with an appreciable range of change in resistance.

This is an original research carried out by the authors who studied the electro-mechanical properties of some composite conductive yarns that have not been studied before in textile strain sensor research. Detailed research methods, results and interpretation of the results have thus been presented.

The purpose of this paper is to introduce a novel respiration pattern-based biometric prediction system (BPS) by using artificial neural network (ANN).

Respiration patterns were obtained using a knitted piezoresistive smart chest band. The ANN model was implemented by using four hidden layers to help achieve the best complexity to produce an adequate fit for the data. Not only did this study give a detailed distribution of an ANN model construction including the scheme of parameters and network layers, ablation of the architecture and the derivation of back-propagation during the iterations but also engaged a step-based decay to systematically drop the learning rate after specific epochs during training to minimize the loss and increase the model’s accuracy as well as to limit the risk of overfitting.

Findings establish the feasibility of using respiratory patterns for biometric identification. Experimental results show that, with a learning rate drop factor = 0.5, the network is able to continue to learn past epoch 40 until stagnation occurs which yielded a classification accuracy of 98 per cent. Out of 51,338 test set, the model achieved 51,557 correctly classified instances and 169 misclassified instances.

The findings provide an impetus for possible studies into the application of chest breathing sensors for human machine interfaces in the area of entertainment.

This is the first time respiratory patterns have been applied in biometric prediction system design.