Current additive manufacturing allows for the implementation of electrically interrogated 3-D printed sensors. In this contribution various technologies, sensing principles and applications are discussed. We will give both an overview of some of the sensors presented in literature as well as some of our own recent work on 3-D printed sensors. The 3-D printing methods discussed include fused deposition modelling (FDM), using multi-material printing and poly-jetting. Materials discussed are mainly thermoplastics and include thermoplastic polyurethane (TPU), both un-doped as well as doped with carbon black, polylactic acid (PLA) and conductive inks. The sensors discussed are based on biopotential sensing, capacitive sensing and resistive sensing with applications in surface electromyography (sEMG) and mechanical and tactile sensing. As these sensors are based on plastics they are in general flexible and therefore open new possibilities for sensing in soft structures, e.g. as used in soft robotics. At the same time they show many of the characteristics of plastics like hysteresis, drift and non-linearity. We will argue that 3-D printing of embedded sensors opens up exciting new possibilities but also that these sensors require us to rethink how to exploit non-ideal sensors.

In this review paper, we focus on the 3D printing technologies that consist of the extruding of fluid material in lines to form structures for electro- and biomechanical applications. Our paper reviews various 3D print technologies, materials, sensing technologies and applications of extrusion-based 3D printing. We also discuss how to overcome some of the challenges with 3D printed sensors, such as the anisotropy of the conductors as well as the drift and nonlinearity of the materials.

Precise and objective assessments of upper limb movement quality after strokes in functional task conditions are an important prerequisite to improve understanding of the pathophysiology of movement deficits and to prove the effectiveness of interventions. Herein, a wearable inertial sensing system was used to capture movements from the fingers to the trunk in 10 chronic stroke subjects when performing reach-to-grasp activities with the affected and non-affected upper limb. It was investigated whether the factors, tested arm, object weight, and target height, affect the expressions of range of motion in trunk compensation and flexion-extension of the elbow, wrist, and finger during object displacement. The relationship between these metrics and clinically measured impairment was explored. Nine subjects were included in the analysis, as one had to be excluded due to defective data. The tested arm and target height showed strong effects on all metrics, while an increased object weight showed effects on trunk compensation. High inter- and intrasubject variability was found in all metrics without clear relationships to clinical measures. Relating all metrics to each other resulted in significant negative correlations between trunk compensation and elbow flexion-extension in the affected arm. The findings support the clinical usability of sensor-based motion analysis.

3D printing of soft EMG sensing structures enables the creation of personalized sensing structures that can be potentially integrated in prosthetic, assistive and other devices. We developed and characterized flexible carbon-black doped TPU-based sEMG sensing structures. The structures are directly 3D-printed without the need for an additional post-processing step using a low-cost, consumer grade multi-material FDM printer. A comparison between the gold standard Ag/AgCl gel electrodes and the 3D-printed EMG electrodes with a comparable contact area shows that there is no significant difference in the EMG signals’ amplitude. The sensors are capable of distinguishing a variable level of muscle activity of the biceps brachii. Furthermore, as a proof of principle, sEMG data of a 3D-printed 8-electrode band are analyzed using a patten recognition algorithm to recognize hand gestures. This work shows that 3D-printed sEMG electrodes have great potential in practical applications.

Sensing of the interaction forces at fingertips is of great value in assessment and rehabilitation therapy. Current force sensors are not compliant to the fingertip tissue and result in loss of touch sensation of the user. This work shows the development and characterization of a flexible fully-3D-printed piezoresistive shear and normal force sensor that uses the mechanical deformation of the finger tissue. Two prototypes of the sensing structure are evaluated using a finite element model and a measurement setup that applies normal and shear forces up to 10 N on a fingertip phantom placed inside the sensing structure, which is fixed to prevent slippage. Furthermore, the relation between strain (rate) and resistance of the conductive TPU, used for the strain gauges, is characterized. The applied normal and shear force components of the 3D-printed sensing structure can be partly separated. FEM analysis showed that the output of the sensor is largely related to the sensor geometry and location of the strain gauges. Furthermore, the conductive TPU that was used has a negative gauge factor for the strain range used in this study and might cause non-linear behaviors in the sensor output.

This paper introduces characterization techniques to investigate electrical properties of 3D-printed conductors. It presents the combination of a physical model to describe frequency dependent electrical properties of 3D-printed conductors; the use of infrared thermography in combination with Joule heating to characterize electrical anisotropy in 3D-printed sheets; and the use of the voltage contrast scanning electron microscopy method (VCSEM) to determine potential distributions in 3D-printed sheets. By means of lock-in thermography, infrared (IR) measurements are improved and amplitude modulation enables lock-in thermography at excitation frequencies above the thermal cut-off frequency. Measurements on sensor samples show the potential of the methods for characterizing sheet-like, conductive structures. The characterization methods allow improvement of 3D-printed sensor designs and exploit electrical properties of 3D-printed conductors.