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Statistical process control of commercial force-sensing resistors

100%

Palacio Gómez C. A. , Paredes-Madrid L. , Garzon A. O.

tom Vol. 29, nr 3

469--481

The manufacturing and characterization of polymer nanocomposites is an active research trend nowadays. Nonetheless, statistical studies of polymer nanocomposites are not an easy task since they require several factors to consider, such as: large amount of samples manufactured from a standardized procedure and specialized equipment to address characterization tests in a repeatable fashion. In this manuscript, the experimental characterization of sensitivity, hysteresis error and drift error was carried out at multiple input voltages (𝑈𝑠) for the following commercial brands of FSRs (force sensing resistors): Interlink FSR402 and Peratech SP200-10 sensors. The quotient between the mean and the standard deviation was used to determine dispersion in the aforementioned metrics. It was found that a low mean value in an error metric is typically accompanied by a comparatively larger dispersion, and similarly, a large mean value for a given metric resulted in lower dispersion; this observation was held for both sensor brands under the entire range of input voltages. In regard to sensitivity, both sensors showed similar dispersion in sensitivity for the whole range of input voltages. Sensors’ characterization was carried out in a tailored test bench capable of handling up to 16 sensors simultaneously; this let us speed up the characterization process.

BoostSole: Design and Realization of a Smart Insole for Automatic Human Gait Classification

84%

Djamaa B. , Bessa M. M. , Diaf B. , Rouigueb A. , Yachir A.

tom Vol. 21

35--43

This paper presents BoostSole; a smart insole based system for automatic human gait recognition. It consists of a smart instrumented insole connected to the cloud via the patient's smartphone using low-power wireless communication. First, the design of BoostSole is introduced with discussions of sensors choice, placement, calibration, and data communication. Next, an adaptive multi-boost classification algorithm is deployed to accurately identify different gait patterns. The algorithm is fast and lightweight and can be implemented in ordinary smartphones with a small footprint in terms of computational requirements, energy consumption, and communication usage. Raw and on-device classified data can be securely uploaded to a distant cloud server for continuous monitoring and analysis. Indeed, they can be visualized and exploited by doctors to identify/correct walking habits and assess the risks of chronic pain associated with an abnormal walk. The system has been evaluated on a dataset containing three gait patterns, namely: shuffle walk; toe walking; and normal gait. Obtained results are promising with more than 97\\% classification accuracy accompanied by low response time and computational demands.