You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: We describe an inertial gesture recognition framework composed of three parts. The first is a compact, wireless six-axis inertial measurement unit to fully capture three-dimensional motion. The second, a gesture recognition algorithm, analyzes the data and categorizes it on an axis-by-axis basis as simple motions (straight line, twist, etc.) with magnitude and duration. The third allows an application designer to combine recognized gestures both concurrently and consecutively to create specific composite gestures can then be set to trigger output routines. This framework was created to enable application designers to use inertial sensors with a minimum of knowledge and effort. Sample implementations and future directions are discussed.

[2]: 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.

[3]: 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.
While there is a wide literature that proposes frameworks to interact with commercial controllers like the WiiMote (eg: [2] ) and general purpose sensor nodes (eg. [3] ) there are only few works proposing frameworks composed by both hardware and software libraries for prototyping orientation and motion sensing. In [1] 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.