The effect of washing on the electrical performance of knitted textile strain sensors for quantifying joint motion
Source abstract
Abstract
Successful market penetration of textile-based strain sensors requires long-term reliability which in turn relies on the washability of the sensor. First, this paper presents an evaluation of the effect of 5 washing cycles on the electrical performance of a knitted conductive transducer, over 1500 cycles of repetitive elongation. The promising behaviour of the textile sensor in this study showed that it might be possible to make a smart garment, capable of quantifying elbow flexion-extension motion, by integrating it into an elbow sleeve. Second, a prototype sleeve, incorporating a knitted sensor (the so-called smart sleeve), was tested in a simulated training/clinical setting by performing 50 flexion-extension cycles after 1, 5, 15, 25, 50 and 75 washes. In both studies, the electrical resistance of the sensor increased with the number of washes in a predictable manner and exhibited a repeatable, reliable and prompt response to elongation. In particular, the electrical pattern representing flexion-extension motion measured using the sleeve was clear and distinguishable up to the 75 th wash. Moreover, resistance measurements within the same trial were repeatable at maximum flexion (≤2% variation) and at maximum extension (≤3% variation) and predictable with increasing washes (R 2 = 0.992 at maximum flexion and R 2 = 0.989 at maximum extension). The good washability of the smart sleeve, evidenced by its ability to detect, distinguish and measure parameters of flexion-extension motion up to 75 washes, makes it a suitable and sustainable choice for applications, such as strength conditioning or rehabilitation, where repetition count and speed are useful.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Muscle activation and electromyography studies · Tactile and Sensory Interactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Engineering, University of Nottingham, Nottingham, UK
Footfalls & Heartbeats (UK) Limited, Nottingham, UK
Funding
Innovate UK
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