This version is available at https://strathprints.strath.ac.uk/65187/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. INDEX TERMS Amplifiers, analog circuits, organic thin film transistors, sensors.

Six flexible force sensors, two on the backrest and four on the seat, were embedded in the upholstery of an off-the-shelf office chair to enable non-intrusive monitoring of sitting postures. Besides the sensors, the monitoring platform comprises an Arduino Nano microcontroller with Wi-Fi transmitter, embedded on the chair, a Wi-Fi receiver communicating with a remote server and a Graphical User Interface (GUI) showing real-time readings. Approximately 26,000 observations corresponding to 9 different postures were collected, labelled and classified using supervised machine learning. The results show that only a subset of the 6 sensors is needed for predicting these 9 sitting postures with high accuracy. This opens up the possibility for intelligent, real-time monitoring systems that can improve safety and wellbeing of today's office workers.

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All-textile capacitive sensor arrays made of a polyurethane foam, fabric and electricallyconducting yarn were fabricated for a 'smart chair'. Polyurethane foam slab that functioned as a dielectric medium was encased between two pieces of commercially available fabric. The electrically-conducting yarn was used to embroider the capacitor electrodes on both fabric pieces. The completed sensor arrays were investigated under normal compressive load with the targeted pressure range of 2 to 30 kPa for the chair seat and 2 to 8 kPa for the backrest. The sensor capacitance versus normal compressive load exhibited a load/unload hysteresis for all sensor arrays. The hysteresis was modelled with sigmoid function and much narrower hysteresis was observed when all sensors were loaded simultaneously, as opposed to their individual loading, allowing development of a phenomenological model for the former. Among the studied sensor arrays, the array with dimensions of 30 cm × 30 cm made of a 10-mm-thick polyurethane foam with density of ∼18.6 kg/m 3 was the most suitable for the following reasons: (a) unloaded sensor capacitance was ∼2.7 pF, (b) the sensor location did not affect its response, (c) ∼10 kg load applied across individual sensor raised its capacitance by ∼12 pF, and (d) 60 kg load applied uniformly across the whole sensor array increased the capacitance by ∼5 pF. During the compression of the individual sensors the top fabric affected the sensor's electro-mechanical response and elastic fabric would be favored for applications with non-uniform pressure distribution.INDEX TERMS Capacitive sensors, polymer foams, sensor arrays, smart devices, textiles.

While the OTFTs exhibit large on-state drain current and a.c. transconductance, smaller L leads to a slightly reduced mobility. In addition, the OTFTs with the largest W of 18.23 mm possess the lowest off-state drain current and subthreshold slope.