Monitoring kinematic parameters (e.g., joint angle) is a critical activity in several fast-growing fields such as robotics, rehabilitation, wearable healthcare, etc. The rapidly advancing in organic electronics and electrical sensing techniques have contributed to the development of flexible kinematic sensors which possess unique advantageous properties: outstanding flexibility, low cost and compatibility with large-area processing techniques. The main challenge faced by researchers is the development of non-invasive, soft and comfortable and conformable sensors. The project aims to the improvement and the integration of the foam based sensors realized by the proponent of this project [1] into an innovative data glove able to measure operator¿s hand movement: fingers abduction and adduction; radial and ulnar abduction; fingers flexion and extension; dorsal and palmar flexion. The sensors will be realized starting from a commercial Polyurethane (PU) foam coated with a patented piezoresistive water-based paint [2]. The as obtained sensor will be integrated in a custom-made glove.
[1] A. Rinaldi, et al. Sensors, 2016 IEEE, Orlando 30 Oct-2 Nov. 2016, pp.1-3
[2] Patent WO2016207804 A1
Wearable strain sensors with excellent stretchability and sensitivity have emerged as a very promising field which could be used for human motion detection and biomechanical systems, etc. The sensor response is strongly affected by the material¿s young modulus [1-2] and as demonstrated in [3-5] foam-based sensors can be used to monitor kinematic and physiological parameters. A foam based sensor has several advantages, an intrinsically low modulus, a stable piezoresistive response [4, 6] and they can be easily embedded in elastomers [7] In this project, the experience gained in [4] will be used as a starting point to produce more sensitive and comfortable sensors. These tasks will be achieved using a more compliant and stretchable polymer (Ecoflex) which has demonstrated great performances as wearable sensors substrate [] and it is more compatible than PDMS with nanostructures. [10,11]. The thickness of the sensors will be greatly reduced using a laser cutter instead of a scalpel. The reduced thickness will contribute to increase the comfort and the sensitivity [12,13] of the data glove. In addition to the new polymer even the design of the sensor will be improved. This task will be accomplished removing the polyurethane foam and the PVA binder through pyrolysis. This solution has already demonstrated to be extremely suitable to produce high sensitive wearable sensors [] and to the best of my knowledge, the smart glove project will be the first to implement this solution for producing stretchable sensors.
In literature, several data gloves are presented, most of them use commercial gloves with the sensors attached somehow on them [14]. This solution has obvious drawbacks in terms of user experience and comfort. In this project, a custom glove inspired by the work of [15] will be produced. In the device¿s design the limitations pointed out in [15] will be overcame. More in detail, the thickness of the glove (2mm) hindered the movement and reduced the comfort; the authors adopted a conductive liquid as active material and classical wire to connect the sensor to the data acquisition system, noticing active material leaks near embedded wires. Smart Glove will easy overcome the aforementioned problems. Firstly, the sensors thickness, that rules the glove one, will be reduced to 0.5 mm thanks to a cutter laser. Secondly, Smart Glove will not use liquid as active material thus eliminating any leakage.
[1] S. Jung, et al. Adv. Mater. 2014, 26, pp. 4825¿4830.
[2] Y. Li, et al. Adv. Funct. Mater. 2016, 26, pp. 2900¿2908
[4] A. Rinaldi, et al. Sensors, 2016 IEEE, Orlando 30 Oct-2 Nov. 2016, pp.1-3
[5] Y.A. Samad, et al. ACS Appl. Mater. Interfaces 2015, 7, 9195 ¿9202
[6] W. Li, et al. Adv. Mater. Technol. 2017, 1700070.
[7] S. Zhao et al, ACS Appl. Mater. Interfaces 2017, 9, 12147¿12164
[8] X. Zhao et al. Adv. Electron. Mater., 1: 1500142
[9] L. W. yap et al. Sci. Bull. (2016) 61: 1624
[10] Lu N. et al. Adv. Funct. Mater. 22 4044¿50,
[11] Y-L Park et al. J. Micromech. Microeng. 20 125029
[12] S. Ryu et al. ACS Nano, 2015, 9 (6), pp 5929¿5936
[13] M. Amjadi et al. Nanotechnology. 2015 Sep 18;26(37):375501
[14] R. Gentner et al. Journal of Neuroscience Methods 178 (2009) 138¿147
[15] F.L. Hammond III et al. 2014 IEEE/RSJ IROS 2014, September 14-18, 2014, Chicago, IL, USA