Fabrication of Flexible Pressure Sensor Systems for Biomedical Applications

Abstract

In today’s sedentary lifestyle, a person spends a substantial amount of time in a sitting position. Having a poor sitting posture can put more stress on specific muscles and joints, forcing them to be overworked and causing them to fatigue, which results in back pains. “Health is Wealth” and when we are talking about a healthy body, posture is as important as eating in the right way and regular exercising. Hence, it is very important for us to sit in the correct posture. Incorrect sitting posture leads to widespread chronic back pain and other health-related issues, particularly in young adults. We developed a flexible pressure-sensor-array-based smart chair that analyses sitting posture using machine learning algorithms. This is an add-on feature that can be installed on any existing chair or can be integrated into chairs by manufacturers. The solution is completely flexible, requires very low power, and is low-cost. The smart chair works by mapping the body pressure at the seat and the backrest. Machine learning algorithms are used for the training and classification of different postures for posture recognition. To fabricate a flexible pressure sensor, we presented the synthesis of an organic polymer-polypyrrole used as a piezoresistive conductive material that was synthesized using in-situ chemical oxidative liquid polymerization. The sensor showed high sensitivity at low-pressure ranges and can measure pressure in the range of 160 Pa to 16 kPa. We then presented the design of a smaller sensor array mat (4 × 4) using a carbon-impregnated conductive polymer named velostat, and performed various tests to ensure that it could be effectively used for posture recognition applications. We presented the mechanical reliability of a velostat-based pressure sensor. We reported the bending response by examining its reliability when subjected to repeated mechanical stress for 150 bending cycles. We presented for the first time ever, the long-term reliability of velostat by testing it for 210 days. From the results, we observed that the particles of the velostat settle after a particular amount of time on repeated load applications. Once that happens, the change in the resistance of the velostat becomes practically invariant with time. We have observed that the decay ratio is closer to 1 after 210 days. This implies that we can expect reliable and repeatable results from the velostat sensor after the application of load 15 times with a load range from 1 to 12 kg. The relative error is also drastically reduced, and there is an overall error reduction by 53 percentage points in 210 days. A novel data acquisition circuit design with flexibility to read out both capacitive and resistive types of sensors has been presented. It requires less area and is more cost-effective compared to separate single-sensor read-out circuits. We finally present the design of a smart chair system. We tested different machine learning models for the classification of seven different postures and achieved the best accuracy of 95.89% using the Support Vector Machine (SVM) model and 95.61% using the Neural Network (NN). Though the training time for NN is longer compared to SVM, the prediction speed is almost double that of SVM.