Prolonged sitting inadequacies cause pressure ulcer to many individuals, especially to disadvantaged with reduced mobility. The measurement of distributed pressure and detection of irregular sitting postures is essential for preventing the risk of developing pressure ulcer. In this paper, a pressure sensing system capable of recognizing sitting postures by means of measuring interface pressure through printed pressure sensors is presented. A thin and flexible large area sensor is screen-printed using silver flake and carbon particle inks and comprises 16 sensing elements. For the evaluation of practical usability, the sensor characterization is carried out by conducting stability, repeatability, drift and bending tests. The performance of the sensor is checked under varying environmental conditions. Sitting posture detection accuracy above 80 % is achieved using a classification algorithm for four different sitting postures. Pressure distribution is monitored at a scanning rate of 10 Hz. A low power and small form factor of read-out electronics enables a compact packaging inside the seat cushion. The presented sensor design targets smart wheelchairs, but it is extendable to much larger areas, for example to be used in beds. The proposed sensing system would be of a great assistance for caregivers and health professionals.

Triboelectric nanogenerators (TENG:s) are used as efficient energy transducers in energy harvesting converting mechanical energy into electrical energy. Wind is an abundant source of mechanical energy but how should a good triboelectric wind harvester be designed? We have built and studied a TENG driven by air flow in a table-top sized wind tunnel. Our TENG constitutes of a plastic film of size 10 cm × 2 cm which is fluttering between two copper electrodes generating enough power to light up a battery of LED:s. We measured the voltage and frequency of fluttering at different wind speeds from zero up to 8 m/s for three electrode distances 6 mm, 10 mm and 14 mm. We found that the frequency increases linearly with the wind speed with a cutoff at some low speed. Power was generated already at 1.6 m/s. We seem to be able to explain the observed frequency dependence on wind speed by assuming excitation of the film into different harmonics in response to von Kármán vortices. We also find that the voltage increase linearly with frequency. We anticipate that TENG:s of this design could be useful both as generators and speed sensors because they work at low air speeds.

This paper presents a posture recognition system aimed at detecting sitting postures of a wheelchair user. The main goals of the proposed system are to identify and inform irregular and improper posture to prevent sitting-related health issues such as pressure ulcers, with the potential that it could also be used for individuals without mobility issues. In the proposed monitoring system, an array of 16 screen printed pressure sensor units was employed to obtain pressure data, which are sampled and processed in real-time using read-out electronics. The posture recognition was performed for four sitting positions: right-, left-, forward- and backward leaning based on k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF), decision tree (DT) and LightGBM machine learning algorithms. As a result, a posture classification accuracy of up to 99.03 percent can be achieved. Experimental studies illustrate that the system can provide real-time pressure distribution value in the form of a pressure map on a standard PC and also on a raspberry pi system equipped with a touchscreen monitor. The stored pressure distribution data can later be shared with healthcare professionals so that abnormalities in sitting patterns can be identified by employing a post-processing unit. The proposed system could be used for risk assessments related to pressure ulcers. It may be served as a benchmark by recording and identifying individuals’ sitting patterns and the possibility of being realized as a lightweight portable health monitoring device.

A horizontal printed Write-Once-Read-Many (WORM) resistive memory has been developed for use in wireless sensor tags targeting single-event detection in smart packaging applications. The WORM memory can be programmed using a 1.5-V printed battery. An alternative programming method is to use chemical sintering which allows the development of exposure-time triggered single event tags that can be accessed wirelessly. The new WORM memory has very low losses in the ON-state which allows direct integration into antenna structures. A sensor tag that utilizes the WORM memory functionality and the well established Electronic Article Surveillance (EAS) communication standard has been outlined. Both active and fully passive sensor tag solutions have been proposed. The role of printed electronics in smart packaging applications has been reviewed and discussed. Important enabling factors for the future development have been highlighted, such as the need for hierarchical design and test tools, better printed interconnect technologies as well as better components that allow communication with existing information and communication technology (ICT) standards. This is illustrated and underlined by the presented smart packaging concept demonstrators.