Capacitive fiber-meshed transducers for touch and proximity-sensing applications

Capacitive sensing is been used in E-Textiles for touch sensing and proximity sensing applications. The common approach is been to construct electrode on top of a non conducting fabric structure. Woven & knitted fabric structures are been used for the construction. Metallic wire and conductive material coated fibres are primarily been used. Due to the performance degradation and poor comfort of these constructions we had constructed electrodes with inherently conductive polymers and multifilament metallic fibres by integrating into fibre meshed structures such that the electrodes are a part of the base structure. We had used capacitive and resistive techniques for the measurements. Out of many mechanical methods of fibre integrating processors we had used flat bed knitting technology. In this paper we had discussed the construction, sensing and applications of capacitive fibre-meshed transducers and their applications

User Recognition Based on Human Body Impulse Response: A Feasibility Study

Human recognition technologies for security systems require high reliability and easy accessibility in the advent of the internet of things (IoT). While several biometric approaches have been studied for user recognition, there are demands for more convenient techniques suitable for the IoT devices. Recently, electrical frequency responses of the human body have been unveiled as one of promising biometric signals, but the pilot studies are inconclusive about the characteristics of human body as a transmission medium for electric signals. This paper provides a multi-domain analysis of human body impulse responses (HBIR) measured at the receiver when customized impulse signals are passed through the human body. We analyzed the impulse responses in the time, frequency, and wavelet domains and extracted representative feature vectors using a proposed accumulated difference metric in each domain. The classification performance was tested using the k-nearest neighbors (KNN) algorithm and the support vector machine (SVM) algorithm on 10-day data acquired from five subjects. The average classification accuracies of the simple classifier KNN for the time, frequency, and wavelet features reached 92.99%, 77.01%, and 94.55%, respectively. In addition, the kernel-based SVM slightly improved the accuracies of three features by 0.58%, 2.34%, and 0.42%, respectively. The result shows potential of the proposed approach for user recognition based on HBIR

Clip-on Gadgets: Expanding Multi-touch Interaction Area with Unpowered Tactile Controls

ABSTRACT Virtual keyboards and controls, commonly used on mobile multi-touch devices, occlude content of interest and do not provide tactile feedback. Clip-on Gadgets solve these issues by extending the interaction area of multi-touch devices with physical controllers. Clip-on Gadgets use only conductive materials to map user input on the controllers to touch points on the edges of screens; therefore, it is batteryfree, lightweight, and low-cost. In addition, it can be used in combination with multi-touch gestures. We present several hardware designs and a software toolkit, which enable users to simply attach Clip-on Gadgets to an edge of a device and start interacting with it

A Tagless Indoor Localization System Based on Capacitive Sensing Technology

Accurate indoor person localization is essential for several services, such as assisted living. We introduce a tagless indoor person localization system based on capacitive sensing and localization algorithms that can determine the location with less than 0.2 m average error in a 3 m × 3 m room and has recall and precision better than 70%. We also discuss the effects of various noise types on the measurements and ways to reduce them using filters suitable for on-sensor implementation to lower communication energy consumption. We also compare the performance of several standard localization algorithms in terms of localization error, recall, precision, and accuracy of detection of the movement trajectory

Low Frequency Electric Field Imaging

abstract: Electric field imaging allows for a low cost, compact, non-invasive, non-ionizing alternative to other methods of imaging. It has many promising industrial applications including security, safely imaging power lines at construction sites, finding sources of electromagnetic interference, geo-prospecting, and medical imaging. The work presented in this dissertation concerns low frequency electric field imaging: the physics, hardware, and various methods of achieving it. Electric fields have historically been notoriously difficult to work with due to how intrinsically noisy the data is in electric field sensors. As a first contribution, an in-depth study demonstrates just how prevalent electric field noise is. In field tests, various cables were placed underneath power lines. Despite being shielded, the 60 Hz power line signal readily penetrated several types of cables. The challenges of high noise levels were largely addressed by connecting the output of an electric field sensor to a lock-in amplifier. Using the more accurate means of collecting electric field data, D-dot sensors were arrayed in a compact grid to resolve electric field images as a second contribution. This imager has successfully captured electric field images of live concealed wires and electromagnetic interference. An active method was developed as a third contribution. In this method, distortions created by objects when placed in a known electric field are read. This expands the domain of what can be imaged because the object does not need to be a time-varying electric field source. Images of dielectrics (e.g. bodies of water) and DC wires were captured using this new method. The final contribution uses a collection of one-dimensional electric field images, i.e. projections, to reconstruct a two-dimensional image. This was achieved using algorithms based in computed tomography such as filtered backprojection. An algebraic approach was also used to enforce sparsity regularization with the L1 norm, further improving the quality of some images.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Balancing Security and Utility in Medical Devices?

ABSTRACT Implantable Medical Devices (IMDs) are being embedded increasingly often in patients' bodies to monitor and help treat medical conditions. To facilitate monitoring and control, IMDs are often equipped with wireless interfaces. While convenient, wireless connectivity raises the risk of malicious access to an IMD that can potentially infringe patients' privacy and even endanger their lives. Thus, while ease of access to IMDs can be vital for timely medical intervention, too much ease is dangerous. Obvious approaches, such as passwords and certificates, are unworkable at large scale given the lack of central authorities and frequent emergencies in medical settings. Additionally, IMDs are heavily constrained in their power consumption and computational capabilities. Designing access-control mechanisms for IMDs that can meet the many constraints of real-world deployment is an important research challenge. In this paper, we review proposed approaches to the accesscontrol problem for IMDs, including the problem of secure pairing (and key distribution) between an IMD and another device, such as a programmer. (We also treat related technologies, such as bodyarea networks.) We describe some limitations of well-conceived proposals and reveal security weaknesses in two proposed cryptographic pairing schemes. Our intention is to stimulate yet more inventive and rigorous research in the intriguing and challenging areas of IMD security and medical-device security in general

User-defined multimodal interaction to enhance children's number learning

Children today are already exposed to the new technology and have experienced excellent number learning applications at an early age. Despite that, most of the children's application softwares either fail to establish the interaction design or are not child-friendly. Involving children in the design phase of any children application is therefore essential as adults or developers do not know the children’s needs and requirements. In other words, designing children's computer applications adapted to the capabilities of children is an important part of today's software development methodology. The goal of this research is to propose a new interaction technique and usability that evaluates children learning performance of numbers. The new interaction technique is designed by participatory design in which children are involved in the design process. A VisionMath interface was implemented with the user-defined multimodal interaction dialogues which was proposed to evaluate the children’s learning ability and subjective satisfaction. An evaluation with 20 participants was conducted using usability testing methods. The result shows that there is a significant difference in the number learning performance between tactile interaction and multimodal interaction. This study reveals the proposed user-defined multimodal interaction dialogue was successful in providing a new interaction technique for children’s number learning by offering alternative input modality and potentially providing a rich field of research in the future