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. 2020 Jul 29;20(15):4207.
doi: 10.3390/s20154207.

Development of a Smart Splint to Monitor Different Parameters during the Treatment Process

José María De Agustín Del Burgo  1 , Fernando Blaya Haro  2 , Roberto D'Amato  2 , Juan Antonio Juanes Méndez  1
Affiliations

Development of a Smart Splint to Monitor Different Parameters during the Treatment Process

José María De Agustín Del Burgo et al. Sensors (Basel). .

Abstract

For certain musculoskeletal complex rupture injuries, the only treatment available is the use of immobilization splints. This type of treatment usually causes discomfort and certain setbacks in patients. In addition, other complications are usually generated at the vascular, muscular, or articular level. Currently, there is a really possible alternative that would solve these problems and even allows a faster and better recovery. This is possible thanks to the application of engineering on additive manufacturing techniques and the use of biocompatible materials available in the market. This study proposes the use of these materials and techniques, including sensor integration inside the splints. The main parameters considered to be studied are pressure, humidity, and temperature. These aspects are combined and analyzed to determine any kind of unexpected evolution of the treatment. This way, it will be possible to monitor some signals that would be studied to detect problems that are associated to the very initial stage of the treatment. The goal of this study is to generate a smart splint by using biomaterials and engineering techniques based on the advanced manufacturing and sensor system, for clinical purposes. The results show that the prototype of the smart splint allows to get data when it is placed over the arm of a patient. Two temperatures are read during the treatment: in contact with the skin and between skin and splint. The humidity variations due to sweat inside the splint are also read by a humidity sensor. A pressure sensor detects slight changes of pressure inside the splint. In addition, an infrared sensor has been included as a presence detector.

Keywords: additive manufacturing; biomedical sensor; health monitoring; iot; personalized medicine; smart splint.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Scanning process of the arm to get the digital model (a) and points cloud (b).
Figure 2
Figure 2
3D model of the arm obtained after the scanning process: top side (a) and bottom side (b).
Figure 3
Figure 3
Post processed: mesh of the scanned arm (a) and solid digital model obtained from the clean mesh (b).
Figure 4
Figure 4
Initial solid obtained from the points cloud (a) and initial stage of the design, including the hole for the temperature sensor and closing buttons (b).
Figure 5
Figure 5
Different steps during the modeling of the splint: division of the splint (a), design of different sensor holes (b), and final top side splint (c).
Figure 6
Figure 6
Final result of the modeled splint over the patient arm.
Figure 7
Figure 7
TotalPrinter machine developed to work with biomaterials (a). G-Code process generation for 3D printing for the different parts of the splint (b) and (c).
Figure 8
Figure 8
The real model of the arm splint obtained from the 3D printer (a). Splint assembled with the different sensors (b) and wires for the connection to the Arduino board (c).
Figure 9
Figure 9
Electronic board wiring to get the data during the tests.
Figure 10
Figure 10
Example of the serial data collection using the Arduino board (a), real splint prototype with electrical wiring mounted over the arm (b) and detailed view of the closing buttons (c).
Figure 11
Figure 11
Temperatures graph.
Figure 12
Figure 12
H Collected data of the humidity sensor.
Figure 13
Figure 13
Collected data of the pressure sensor 3.2.4 Presence.
Figure 14
Figure 14
Collected data of the light reflection sensor over the skin.
See this image and copyright information in PMC

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