Orientation and Motion Sensing are widely implemented on various consumer products, such as mobile phones, tablets and cameras as they enable immediate interaction with virtual information. The prototyping phase of any orientation and motion sensing capable device is however a quite difficult process as it may involve complex hardware designing, math algorithms and programming. In this paper, we present FreeIMU, an Open Hardware Framework for prototyping orientation and motion sensing capable devices. The framework consists in a small circuit board containing various sensors and a software library, built on top of the Arduino platform. Both the hardware and library are released under open licences and supported by an active community allowing to be implemented into research and commercial projects.
@cite_4 is a small sensor board containing an accelerometer, a microcontroller and wireless communication which has been designed for ease-of-use. While this device may be interesting for inexperienced developers, it may be also constraining compared to Arduino, as also noted by the authors. Our framework also aims to ease sensor based interaction prototyping, however we concentrated on not loosing the flexibility and extensibility of the Arduino platform while providing wider sensor input.
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We have designed and constructed a modular platform for use in compact wireless sensing. This platform is based around a series of circuit boards (or panes), each of which instantiates a specific sensing modality - e.g. inertial sensing, tactile sensing or ambient sensing. As opposed to similar architectures, this system treats the sensor panes as discrete design objects that have data collection as their primary purpose. The main goals achieved by this design are ease of prototyping (and revision) and encapsulation of design knowledge. We describe a number of different projects instantiated with this system. Key among them are a wearable gait measurement system which exploited the modularity of the system, a novel musical controller which benefited from the design encapsulation and a study of dynamically low-power sensor nodes based on the structure of the panes (i.e. multiple sensors for a single modality). Several other applications are discussed. Lessons learned in the areas of software design, sensor placement and mechanical stability are commented on. In this paper we briefly motivate, present, and give an initial evaluation of DUL Radio, a small wireless toolkit for sketching sensor-based interaction. In the motivation, we state the purpose of this specific platform, which aims to balance ease-of-use (learning, setup, initialization), size, speed, flexibility and cost, aimed at wearable and ultra-mobile prototyping where fast reaction is needed (e.g. in controlling sound), and we mention general issues facing this category of embodied interaction design tools. We briefly present the platform, both regarding hardware and software. In the evaluation, we present our experiences with the platform both in design projects and in teaching. We conclude that DUL Radio was the preferred platform for sketching sensor-based interaction compared to other solutions, and that it does seem to be a relatively easy-to-use tool, but that there are many ways to improve it. Target users include designers, students, artists etc. with minimal programming and hardware skills, but this paper addresses the issues with designing the tools, which includes some technical considerations. The rapid deployment of sensing technology in smartphones and the explosion of their usage in people's daily lives provide users with the ability to collectively sense the world. This leads to a growing trend of mobile healthcare systems utilizing sensing data collected from smartphones with without additional external sensors to analyze and understand people's physical and mental states. However, such healthcare systems are vulnerable to user spoofing attacks, in which an adversary distributes his registered device to other users such that data collected from these users can be claimed as his own to obtain more healthcare benefits and undermine the successful operation of mobile healthcare systems. Existing mitigation approaches either only rely on a secret PIN number (which can not deal with colluded attacks) or require an explicit user action for verification. In this paper, we propose a user verification scheme leveraging unique gait patterns derived from acceleration readings in mobile healthcare systems to detect possible user spoofing attacks. Our framework exploits the readily available accelerometers embedded within smartphones for user verification. Specifically, our user spoofing attack mitigation scheme (which consists of three components, namely Step Cycle Identification, Step Cycle Interpolation, and Similarity Score Computation) is used to extract gait patterns from run-time accelerometer measurements to perform robust user verification under various walking speeds. Our experiments using 322 smartphone-based traces over a period of 6 months confirm that our scheme is highly effective for detecting user spoofing attacks. This strongly indicates the feasibility of using smartphone based low grade accelerometer to conduct gait recognition and facilitate effective user verification without active user cooperation. Mobile phones are increasingly equipped with a range of highly responsive sensors. From cameras and GPS receivers to three-axis accelerometers, applications running on these devices are able to experience rich interactions with their environment. Unfortunately, some applications may be able to use such sensors to monitor their surroundings in unintended ways. In this paper, we demonstrate that an application with access to accelerometer readings on a modern mobile phone can use such information to recover text entered on a nearby keyboard. Note that unlike previous emanation recovery papers, the accelerometers on such devices sample at near the Nyquist rate, making previous techniques unworkable. Our application instead detects and decodes keystrokes by measuring the relative physical position and distance between each vibration. We then match abstracted words against candidate dictionaries and record word recovery rates as high as 80 . In so doing, we demonstrate the potential to recover significant information from the vicinity of a mobile device without gaining access to resources generally considered to be the most likely sources of leakage (e.g., microphone, camera).