Gait is a very complex movement, involving the central nervous system and a significant part of the skeletomuscular system. Any disease that is affecting one or more of the involved parts will reflect in the gait. Therefore, gait analysis has been studied extensively in the context of early disease diagnostics, post-operation rehabilitation monitoring, and sports injury prevention. Gait cycle phase partitioning is one of the most common gait characteristic analysis methods, which utilizes the cyclical nature of human gait. Pressure sensitive mats and insoles are considered the gold standard, but some inherent limitations of these methods urge researchers to seek for alternatives. One of the proposed alternatives is Smart Sock systems, which contain textile pressure sensors. The main limitation of Smart Sock systems is the limited number of sensors, thus complicating gait phase partitioning by these systems. The present paper describes gait phase partitioning using plantar pressure signal obtained by a Smart Sock system. Six-phase partitioning was achieved, including such gait phases as initial contact, loading response, mid stance, terminal stance, pre-swing and swing phase. Mean gait cycle time values obtained from the experimental data were in accordance with the ones found in the literature.
In the field of protective smart clothing, such topics as monitoring of wearer’s vital signs and spatial position are thoroughly studied in the literature. On the other hand, solutions for monitoring of hazardous events, such as high-energy impacts with foreign objects are studied to less extent. Such protective clothing is intended both for operators, working in environments with a risk of injury due to impact with moving parts or falling objects, e.g. operators of heavy agricultural machinery, first responders etc. Such a sensor would enable to detect the location of the impact, its force and would allow to develop a system that sends appropriate emergency signals. The proposed paper focuses on the development of such a sensor, giving a special attention to its structure and usable materials. The proposed matrix array sensor consists of three layers: upper and lower layers with electrically conductive traces and the intermediate piezoresistive layer. Due to the intended application, a textile piezoresistive layer is chosen and after studying the commercially available materials, EeonTex LTT-SLPA 60 kOhm conductive polymer-coated knitted fabric and Sefar Carbotex 03-120CF, 03-160CF and 03-205CF carbon/polyester woven fabrics were selected as the most suitable candidates for further testing. The testing was accomplished using Zwick/Roell Z2.5 compression/strain testing column and Agilent A34970A ohmmeter. The testing focused on changes in fabrics’ conductivity under cyclic stress, sensitivity, measurement hysteresis and repeatability. Different technologies for connecting the piezoresistive and the outside layers were tested as well: direct connection and connection using conductive adhesive textile tapes. Based on the results obtained during testing, recommendations are given for the usage of the studied materials, based on their performance under various levels of stress. During the experiments the best metrological results were demonstrated by piezoresistive fabrics with conductive polymer coating. Besides that, it was concluded that while hot-melt adhesive conductive tapes substantially degrade the sensors’ properties, the use of self-adhesive conductive tapes ensure constant and stable contact between the sandwich layers and thus improve the measuring stability.
Smart garment system is efficient for upper body movement monitoring during simple tasks. There is a lack of literature on smart textile garments being reliable for shoulder girdle motion assessment in advanced motor tasks such as high string performance. The aim of the article was to examine the reliability of the DAid Smart Shirt for Shoulder Girdle Motion Assessment during advanced motor tasks such as high string performance. Methods: 14 volunteer violinists aged 18.6 (SD 2.1) with a body mass index 20.05 (SD 2.3) were recruited. The violinists performed a legato bowing task. The DAid smart shirt worked as the assessment tool: a compression garment with textile strain sensors sewn onto it. Cronbach alpha coefficient, Interclass Correlation Coefficient were calculated to assess the within-session test-retest reliability. Results: An excellent and good result test-retest reliability was assessed in 57% of the violinists, for other 43%, the ICC and Cronbach alpha coefficient was less than 0.59. Conclusion: the DAid Smart shirt is reliable for shoulder girdle motion assessment during high string performance. The smart textile garment should be customized and suitable for the body in order to assess shoulder girdle motion during high level or advanced activities such as high string performance.
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