Development of a yarn capable of measuring localised temperature

In this research an electronic temperature sensor (ETS) yarn has been developed by embedding a commercially available thermistor chip into the fibres of a yarn. A polymer resin is used to encapsulate the thermistor creating a micro-pod which protects the thermistor from mechanical and chemical stresses during use, and also allows the ETS yarn to be washed. The thermistor micropod and interconnects were then encased within a warp knitted braid to from the ETS yarn. Temperature is the most widely measured physiological bio-marker in medicine. Temperature changes can indicate underlying pathologies such as wound infections or the formation of ulcers in diabetic patients. A temperature sensor capable of providing remote, continuous and localised (temperature at a given point) temperature measurements could provide clinicians with a powerful tool when handling such complications. Even though there are many flexible temperature sensors they lack true textile characteristics making them unsuitable in many situations. The existing textile-based temperature sensors are incapable of providing localised measurements and can suffer from hysteresis. At the start of the project a geometrical model of the ETS yarn was developed in-order to understand its design parameters. Then the crafting of the ETS yarn was achieved in three key stages. Hardware and software necessary to obtain temperature from the ETS yarn have been developed. Thereafter work has been conducted to characterise the behaviour of the thermistor and understand the design rules for the micro-pod. Theoretical models were created in COMSOL in-order to study the heat flow through the micro-pod and warp knitted braid, and the effect they have on the response and recovery times of the thermistor. The model has been validated using experiments. Results have shown that encapsulating the thermistor in a micro-pod and making it into a yarn has a minimal effect on the thermal time constant and that the resin of the micro-pod and fibres of the warp knitted braid have no significant impact on the accuracy of the temperature readings. The research into calibrating the ETS yarn has shown that the resistance-temperature conversion equation provided by the thermistor manufacturer provided the most accurate temperature measurement with 63 % of the readings being within ± 0.5 °C accuracy. Cyclic tests have been carried out on the ETS yarn to ensure that its performance is not effected by mechanical strain. Thereafter an evaluation of the response of the ETS yarn to operational conditions (ambient temperature, moisture content, wind speed) was studied. Finally, prototype temperature sensing garments have been produced using a network of ETS yarns. The necessary hardware and software to capture the temperature data from these prototypes has been developed. Finally, two prototypes have been created, a temperature sensing sock with five ETS yarn for detecting non-freezing cold injuries and a dressing with 16 ETS yarns to provide a temperature map of a wound. The temperature sensing sock was tested on volunteers. Both the wound dressing and the sock can provide remote, continuous and localised temperature measurements without compromising the textile characteristics of the fabric

Electronic temperature sensing yarn

This paper reports the development of an electronic temperature sensing (ETS) yarn by embedding commercially available thermistors within the fibres of a yarn. The thermistor is initially soldered onto a fine copper wire and then encapsulated to form a polymer micro-pod. The micro-pod protects the thermistor from mechanical and chemical stresses. Thereafter the fine copper wire with micro-pods is covered by a tubular warp knitted structure to craft the final ETS yarn. The miniature size of the micro-pod makes the electronics invisible to the wearer. Such a yarn can be used to knit or weave any textile structure. It is also capable of providing temperature readings at a given point (localised) and can be used to make a wearable thermograph. Where the ETS yarn is included in a garment, it can be safely washed

Developing novel temperature sensing garments for health monitoring applications

Embedding temperature sensors within textiles provides an easy method for measuring skin temperature. Skin temperature measurements are an important parameter for a variety of health monitoring applications, where changes in temperature can indicate changes in health. This work uses a temperature sensing yarn, which was fully characterized in previous work, to create a series of temperature sensing garments: armbands, a glove, and a sock. The purpose of this work was to develop the design rules for creating temperature sensing garments and to understand the limitations of these devices. Detailed design considerations for all three devices are provided. Experiments were conducted to examine the effects of contact pressure on skin contact temperature measurements using textile-based temperature sensors. The temperature sensing sock was used for a short user trial where the foot skin temperature of five healthy volunteers was monitored under different conditions to identify the limitations of recording textile-based foot skin temperature measurements. The fit of the sock significantly affected the measurements. In some cases, wearing a shoe or walking also heavily influenced the temperature measurements. These variations show that textile-based foot skin temperature measurements may be problematic for applications where small temperature differences need to be measured

A wearable textile thermograph

In medicine, temperature changes can indicate important underlying pathologies such as wound infection. While thermographs for the detection of wound infection exist, a textile substrate offers a preferable solution to the designs that exist in the literature, as a textile is very comfortable to wear. This work presents a fully textile, wearable, thermograph created using temperature-sensing yarns. As described in earlier work, temperature-sensing yarns are constructed by encapsulating an off-the-shelf thermistor into a polymer resin micro-pod and then embedding this within the fibres of a yarn. This process creates a temperature-sensing yarn that is conformal, drapeable, mechanically resilient, and washable. This work first explored a refined yarn design and characterised its accuracy to take absolute temperature measurements. The influence of contact errors with the refined yarns was explored seeing a 0.24 ± 0.03 measurement error when the yarn was held just 0.5 mm away from the surface being measured. Subsequently, yarns were used to create a thermograph. This work characterises the operation of the thermograph under a variety of simulated conditions to better understand the functionality of this type of textile temperature sensor. Ambient temperature, insulating material, humidity, moisture, bending, compression and stretch were all explored. This work is an expansion of an article published in The 4th International Conference on Sensor and Applications

Refinement of temperature sensing yarns

Body temperature is an important parameter to measure in a number of fields such as medicine and sport. In medicine temperature changes can indicate underlying pathologies such as wound infections, while in sport temperature can be associated to a change in performance. In both cases a wearable temperature monitoring solution is preferable. In earlier work a temperature sensing yarn has been developed and characterized. The yarns were constructed by embedding an off-the-shelf thermistor into a polymer resin micro-pod and then into the fibers of a yarn. This process created a temperature sensing yarn that was conformal, drapeable, mechanically resilient, and washable. This work builds on this early study with the purposes of identifying the steady state error bought about on the temperature measurements as a result of the polymer resin and yarn fibers. Here a wider range of temperatures than previously explored were investigated. Additionally two types of polymer resin with different thermal properties have been tested, with varying thicknesses, for the encapsulation of the thermistor. This provides useful additional information for optimizing the temperature sensing yarn design

Flexible temperature sensor integration into e-textiles using different industrial yarn fabrication processes

Textiles enhanced with thin-film flexible sensors are well-suited for unobtrusive monitoring of skin parameters due to the sensors’ high conformability. These sensors can be damaged if they are attached to the surface of the textile, also affecting the textiles’ aesthetics and feel. We investigate the effect of embedding flexible temperature sensors within textile yarns, which adds a layer of protection to the sensor. Industrial yarn manufacturing techniques including knit braiding, braiding, and double covering were utilised to identify an appropriate incorporation technique. The thermal time constants recorded by all three sensing yarns was <10 s. Simultaneously, effective sensitivity only decreased by a maximum of 14% compared to the uncovered sensor. This is due to the sensor being positioned within the yarn instead of being in direct contact with the measured surface. These sensor yarns were not affected by bending and produced repeatable measurements. The double covering method was observed to have the least impact on the sensors’ performance due to the yarn’s smaller dimensions. Finally, a sensing yarn was incorporated in an armband and used to measure changes in skin temperature. The demonstrated textile integration techniques for flexible sensors using industrial yarn manufacturing processes enable large-scale smart textile fabrication

Design and characterisation of a non-contact flexible sensor array for electric potential imaging applications

Capacitive non-contact imaging of electric fields and potentials with micro-metre resolution can provide relevant insights into material characterisation, structural analysis, electrostatic charge imaging and bio-sensing applications. However, scanning electric potential microscopes have been confined to rigid and single-probe devices, making them slow, prone to mechanical damage and complex to fabricate. In this work, we present the design and characterisation of a novel 5-element flexible array of electric potential probes with spatial resolution down to 20μm to speed up the scanning time. This was achieved by combining flexible thin-film probes for active guarding and shielding with state-of-the art discrete conditioning circuits. The potential of this approach is showcased by using the fabricated array to image latent fingerprints deposited on an insulating surface by contact electrification, obtain the surface topography of conductive samples and to visualise local dielectric variations

Flexible sensors—from materials to applications

Flexible sensors have the potential to be seamlessly applied to soft and irregularly shaped surfaces such as the human skin or textile fabrics. This benefits conformability dependant applications including smart tattoos, artificial skins and soft robotics. Consequently, materials and structures for innovative flexible sensors, as well as their integration into systems, continue to be in the spotlight of research. This review outlines the current state of flexible sensor technologies and the impact of material developments on this field. Special attention is given to strain, temperature, chemical, light and electropotential sensors, as well as their respective applications

Biocompatible gel-free coconut-oil and carbon black electrodes for ECG and respiration measurements

The current state of the art in telemedicine has increased the interest in long term monitoring of physiological and bioelectric signals. This motivated the development of materials and techniques for the fabrication of biocompatible, user and environmentally friendly alternatives to conventional resistive wet electrodes. Here we report a method for the fabrication of dry flexible and stretchable electrodes based on Coconut-Oil and Carbon Black for the monitoring of electrophysiological signals without conductive gels. The highly stretchable material shows a specific resistance ρ down to 33.2±12.3Ωm, high conformability, and a stretchability up to 1500%. The epidermal electrodes were used to record Electrocardiographic (ECG) signals and measure respiration in a 3-lead configuration and compared to commercial wet electrodes. Even after being elongated by 100% for 100 stretch/release cycles, a reliable recording of the QRS-complex is demonstrated without the need for any contact enhancer or substances that cause skin reaction, demonstrating the potential use of this material for long term ECG monitoring applications

Classifying gait alterations using an instrumented smart sock and deep learning

This paper presents a non-invasive method of classifying gait patterns associated with various movement disorders and/or neurological conditions, utilising unobtrusive, instrumented socks and a deep learning network. Seamless instrumented socks were fabricated using three accelerometer embedded yarns, positioned at the toe (hallux), above the heel and on the lateral malleolus. Human trials were conducted on 12 able-bodied participants, an instrumented sock was worn on each foot. Participants were asked to complete seven trials consisting of their typical gait and six different gait types that mimicked the typical movement patterns associated with various movement disorders and neurological conditions. Four Neural Networks and an SVM were tested to ascertain the most effective method of automatic data classification. The Bi-LSTM generated the most accurate results and illustrates that the use of three accelerometers per foot increased classification accuracy compared to a single accelerometer per foot by 11.4%. When only a single accelerometer was utilised for classification, the ankle accelerometer generated the most accurate results in comparison to the other two. The network was able to correctly classify five different gait types: stomp (100%), shuffle (66.8%), diplegic (66.6%), hemiplegic (66.6%) and “normal walking” (58.0%). The network was incapable of correctly differentiating foot slap (21.2%) and steppage gait (4.8%). This work demonstrates that instrumented textile socks incorporating three accelerometer yarns were capable of generating sufficient data to allow a neural network to distinguish between specific gait patterns. This may enable clinicians and therapists to remotely classify gait alterations and observe changes in gait during rehabilitation