Flexible sensors that can be integrated into clothing to measure everyday functional performance is an emerging concept. It aims to improve the patient's quality of life by obtaining rich, real-life data sets. One clinical area of interest is the use of these sensors to accurately measure knee motion in, e.g., osteoarthritic patients. Currently, various methods are used to formally calculate joint motion outside of the laboratory and they include electrogoniometers and inertial measurement units. The use of these technologies, however, tends to be restricted, since they are often bulky and obtrusive. This directly influences their clinical utility, as patients and clinicians can be reluctant to adopt them. The goal of this paper is to present the development process of a patient centered, clinically driven design for an attachable clothing sensor (ACS) system that can be used to assess knee motion. A pilot study using 10 volunteers was conducted to determine the relationship between the ACS system and a gold standard apparatus. The comparison yielded an average root mean square error of ∼1°, a mean absolute error of ∼3°, and coefficient of determination above (R 2 ) 0.99 between the two systems. These initial results show potential of the ACS in terms of unobtrusive long-term monitoring.

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This paper proposes a novel wearable system and assesses its reliability in monitoring sagittal knee movement, and discriminating between activities of daily living. The system consists of a flexible conductive polymer unit, embedded into a pair of leggings at the level of the knee, interfaced with a customized sensing node for wireless data acquisition. Design constraints included the need for the system to be unobtrusive, low cost, low power, and simple to use. The wearable system was evaluated through a series of trials conducted on healthy participants, tested on two different occasions, while walking, running, and going up and down a set of stairs. The waveforms of the sensor output resemble typical knee kinematics curves. An intraclass correlation coefficient greater than 0.8 was obtained for the output signal of the sensor from which, knee movement is derived for each of the different activities. Time and frequency domain features of the signal were used to discriminate between activities. Results show good discriminative capacity of selected parameters to an accuracy of 93% when employing a random forest analytical approach. These results suggest that the system can be used accurately to monitor both knee movement and activity performed in unconstrained environments, and thus suggesting its potential use to support knee rehabilitation.Index Terms-Activity discrimination, flexible sensor, knee rehabilitation, median frequency, power spectral density, random forest, range of motion.

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