Development of a Smart Leg Splint by Using New Sensor Technologies and New Therapy Possibilities

Abstract

Nowadays, after suffering a fracture in an upper or lower limb, a plaster cast is placed on the affected limb. It is a very old and efficient technique for recovery from an injury that has not had significant changes since its origin. This project aims to develop a new low-cost smart 3D printed splint concept by using new sensing techniques. Two rapidly evolving Advanced Manufacturing (AM) technologies will be used: 3D scanning and 3D printing, thus combining engineering, medicine and materials evolution. The splint will include new small and lightweight sensors to detect any problem during the treatment process. Previous studies have already incorporated this kind of sensor for medical purposes. However, in this study it is implemented with a new concept: the possibility of applying treatments during the immobilization process and obtaining information from the sensors to modify the treatment. Due to this, rehabilitation treatments like infrared, ultrasounds or electroshock may be applied during the treatment, and the sensors (as it is showed in the study) will be able to detect changes during the rehabilitation process. Data of the pressure, temperature, humidity and colour of the skin will be collected in real time and sent to a mobile device so that they can be consulted remotely by a specialist. Moreover, it would be possible to include these data into the Internet of Things movement. This way, all the collected data might be compared and studied in order to find the best treatment for each kind of injury. It will be necessary to use a biocompatible material, submersible and suitable for contact with skin. These materials make it necessary to control the conditions in which the splint is produced, to assure that the properties are maintained. This development, makes it possible to design a new methodology that will help to provide faster and easier treatment.

Keywords: AM technique; IoT; customized medicine; health monitoring; rehabilitation therapy; smart splint.

Figure 1

TCS3472 sensor responsivity graphs (a,b) [55].

Figure 2

Chronogram of rehabilitation with new techniques applied [10].

Figure 3

(a) Original and treated points cloud in Geomagic Software; (b) Mesh surface creation parameters; (c) Mesh manipulation.

Figure 4

Mesh of the splint (a) and mesh over the original 3D model (b).

Figure 5

Original mesh (a) and reduced mesh (b).

Figure 6

Catia Software. Reduced mesh (a) and offset tool (b).

Figure 7

Splint imported from Inventor Autodesk Software (a) and dimensional check (b).

Figure 8

Division of the splint (a) and magnet housing design (b).

Figure 9

Therapeutic windows to be made in the splint.

Figure 10

View of the splint: front part (a), inside (b) outside (c) from the back part of the splint. (d) designed splint over the scanned model.

Figure 11

Total-Printer machine (a) and the two parts of the splint ready for printing (b,c).

Figure 12

Schematic design of the electronic board for the sensors (a), soldering process (b) and electronic assembly (c).

Figure 13

Real splint assembled with the sensors (a) and interior view (b).

Figure 14

Real model over the leg.

Figure 15

Temperature’s graph.

Figure 16

Humidity graph.

Figure 17

Pressure sensors on the “X” and “Y” axis.

Figure 18

Pressure graph.

Figure 19

Color RGB sensor lectures without hematoma (a) and with hematoma (b).

Figure 19

Color RGB sensor lectures without hematoma (a) and with hematoma (b).