Pressure sensitive insoles are an emerging and promising technology that has always been interesting for gait and planar pressure related applications. This technology can be especially valuable for monitoring, movement, and rehabilitation purposes where the pressure sensing insoles could be utilized to assess for abnormalities in order to treat or prevent complications. This thesis project explores the use of graphene coated resistive textiles based smart insoles with the purpose of constructing a functional, easy to fabricate prototype that is viable for plantar pressure and gait cycle applications. This project follows a double diamond, co-productive approach with multiple stakeholders involved during the discovery, definition, development, and delivery of the project to co-create knowledge of value for society. The results of the thesis project present three functional prototypes with 3, 4 and 6 pressure sensors with the 4-sensor prototype indicating to be the most feasible out of the three. The highlight of the prototypes features is that it is capable of detecting and measuring pressure, operates with durable and thin properties and low accuracy. Through proper calibration with an ADC tool, the prototype was able to detect and measure movement during testing. Furthermore, several areas with a room for improvement have been identified with potential for further automating the production process as well as unlocking barriers for certain applications with a cost effective approach. In conclusion, this thesis project contributes to the advancement of smart insoles by presenting a functional, easy to fabricate method for the production of smart insoles for low accuracy gait cycle and plantar pressure applications.

When performing a biomechanical analysis of human movement, knowledge about the ground reaction force (GRF) is necessary to compute forces and moments within joints. This is important when analysing a movement and its effect on the human body. To obtain knowledge about the GRF, the gold standard is to use force plates which directly measure all three components of the GRF (mediolateral, anteroposterior and normal). However, force plates are heavy, clunky and expensive, setting constraints on possible experimental setups, which make it desirable to exclude them and instead use a predictive method to obtain the full GRF. Several predictive methods exist. The node model is a GRF predictive method included in a musculoskeletal modeling software. The tool use motion capture and virtual actuators to predict all three GRF components. However, this model has not yet been validated during weighted leg press and weighted squat. Furthermore, the normal component of the GRF can be measured continuously during the activity with pressure sensitive insoles (PSIs), which might provide better accuracy of the GRF prediction. The objectives of this thesis were to investigate whether force plates can be exluded during weighted leg press and weighted squat and to investigate whether PSIs can improve the GRF prediction. To investigate this, the node model and a developed shear model was validated. The shear model computes the two shear GRF components based on data from PSIs, an external load acting upon the body and data from a motion capture system. Both the node model and the shear model were analysed with two test subjects performing two successive repetitions of both weighted squat and weighted leg press in a flywheel exercise device. During the leg press exercise, the node model had a mean coeffcient of correlation (Pearson's) ranging from 0.70 to 0.98 for all three directions with a mean root mean square error ranging between 8 % to 20 % of the test person's body weight. The developed shear model had a coeffcient of correlation (Pearson's) between 0.64 to 0.99 and a mean root mean square error between 3 % and 21 % of the test person's body weight. This indicates that it is possible to exclude force plates and instead predict the GRF during weighted leg press. During squat, neither the node model nor the shear model provided accurate results regarding the mediolateral and anteroposterior components of the GRF, suggesting that force plates can not yet be excluded to obtain the full GRF during weighted squat. The results of the normal component during leg press was somewhat improved with the shear model compared to the node model, indicating that using PSIs can improve the results to some extent.