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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.

Discrete choice modeling has been discussed by both academics and practitioners as a means of analytical support for B2C relationship marketing. This paper aims to discuss applying this analytical framework in B2B marketing, with an example of cross‐selling high‐tech services to a large business customer. This example is also used to show how an algorithm of genetic binary choice (GBC) modeling, developed by the author, performs in comparison with major techniques used nowadays, and to analyze the financial impact of these different approaches on profitability of B2B relationship marketing operations.

Predictive models based on the regression analysis, the classification tree and the GBC algorithm are built and analyzed in the context of their performance in optimizing cross‐selling campaigns. An example of business case analysis is used to estimate the financial implications of the different approaches.

B2B relationship marketing, although differing from B2C in many aspects, can also benefit from analytical support with discrete choice modeling. The financial impact of such support is significant, and can be further increased by improving the predictive accuracy of the models. In this context the GBC modeling algorithm proves to be an interesting alternative to the algorithms used nowadays.

The generalizability of the findings, concerning performance characteristics of the algorithms, is limited: which method is best depends, for example, on data distributions and the particular relationships being modeled.

The paper shows how B2B marketing managers can increase the profitability of relationship marketing using discrete choice modeling, and how implementing new algorithms like the GBC model presented here can allow for further improvement.

The paper bridges the gap between research on binary choice modeling and the practice of B2B relationship marketing. It presents a new possibility of analytical support for B2B marketing operations together with financial implications. It also includes a demonstration of an algorithm newly developed by the author.