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.
While there is a wide literature that proposes frameworks to interact with commercial controllers like the WiiMote (eg: @cite_3 ) and general purpose sensor nodes (eg. @cite_2 ) there are only few works proposing frameworks composed by both hardware and software libraries for prototyping orientation and motion sensing. In @cite_15 the authors propose a framework composed by a wireless sensor board and gesture recognition algorithms. While their work on the algorithms is still interesting, their sensor hardware is now considerably outdated and the sensors used in FreeIMU are considerably more accurate, faster and easier to interface.
@cite_15 @cite_3 @cite_2
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In many applications today user interaction is moving away from mouse and pens and is becoming pervasive and much more physical and tangible. New emerging interaction technologies allow developing and experimenting with new interaction methods on the long way to providing intuitive human computer interaction. In this paper, we aim at recognizing gestures to interact with an application and present the design and evaluation of our sensor-based gesture recognition. As input device we employ the Wii-controller (Wiimote) which recently gained much attention world wide. We use the Wiimote's acceleration sensor independent of the gaming console for gesture recognition. The system allows the training of arbitrary gestures by users which can then be recalled for interacting with systems like photo browsing on a home TV. The developed library exploits Wii-sensor data and employs a hidden Markov model for training and recognizing user-chosen gestures. Our evaluation shows that we can already recognize gestures with a small number of training samples. In addition to the gesture recognition we also present our experiences with the Wii-controller and the implementation of the gesture recognition. The system forms the basis for our ongoing work on multimodal intuitive media browsing and are available to other researchers in the field. 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. The advent of sensor arrays providing tactile feedback with high spatial and temporal resolution asks for new control strategies to exploit this important and valuable sensory channel for grasping and manipulation tasks. In this paper, we introduce a control framework to realize a whole set of tactile servoing tasks, i.e. control tasks that intend to realize a specific tactile interaction pattern. This includes such simple tasks like tracking a touched object, maintaining both contact location and contact force, as well as more elaborate tasks like tracking an object’s pose or tactile object exploration. Exploiting methods known from image processing, we introduce robust feature extraction methods to estimate the 2D contact position, the contact force, and the orientation of an object edge being in contact to the sensor. The flexible control framework allows us to adapt the PID-type controller to a large range of different tasks by specification of a projection matrix toggling certain control components on and off. We demonstrate and evaluate the capabilities of the proposed control framework in a series of experiments employing a 16×16 tactile sensor array attached to a Kuka LWR as a large fingertip. We propose a sparse-coding framework for activity recognition in ubiquitous and mobile computing that alleviates two fundamental problems of current supervised learning approaches. (i) It automatically derives a compact, sparse and meaningful feature representation of sensor data that does not rely on prior expert knowledge and generalizes well across domain boundaries. (ii) It exploits unlabeled sample data for bootstrapping effective activity recognizers, i.e., substantially reduces the amount of ground truth annotation required for model estimation. Such unlabeled data is easy to obtain, e.g., through contemporary smartphones carried by users as they go about their everyday activities.Based on the self-taught learning paradigm we automatically derive an over-complete set of basis vectors from unlabeled data that captures inherent patterns present within activity data. Through projecting raw sensor data onto the feature space defined by such over-complete sets of basis vectors effective feature extraction is pursued. Given these learned feature representations, classification backends are then trained using small amounts of labeled training data.We study the new approach in detail using two datasets which differ in terms of the recognition tasks and sensor modalities. Primarily we focus on a transportation mode analysis task, a popular task in mobile-phone based sensing. The sparse-coding framework demonstrates better performance than the state-of-the-art in supervised learning approaches. More importantly, we show the practical potential of the new approach by successfully evaluating its generalization capabilities across both domain and sensor modalities by considering the popular Opportunity dataset. Our feature learning approach outperforms state-of-the-art approaches to analyzing activities of daily living.