Abstract

Abstract
Virtual Reality (VR) is again in the spotlight. However, interactions and modeling operations are still major hurdles to its complete success. To make VR Interaction viable, many have proposed mid-air techniques because of their naturalness and resemblance to physical world operations. Still, natural mid-air metaphors for Constructive Solid Geometry (CSG) are still elusive. This is unfortunate, because CSG is a powerful enabler for more complex modeling tasks, allowing to create complex objects from simple ones via Boolean operations. Moreover, Head-Mounted Displays occlude the real self, and make it difficult for users to be aware of their relationship to the virtual environment. In this paper we propose two new techniques to achieve Boolean operations between two objects in VR. One is based on direct-manipulation via gestures while the other uses menus. We conducted a preliminary evaluation of these techniques. Due to tracking limitations, results allowed no significant conclusions to be drawn. To account for self-representation, we compared full-body avatar against an iconic cursor depiction of users' hands. In this matter, the simplified hands-only representation improved efficiency in CSG modelling tasks. © 2017 IEEE.

Abstract
Selecting objects outside user's arm-reach in Virtual Reality still poses significant challenges. Techniques proposed to overcome such limitations often follow arm-extension metaphors or favor the use of selection volumes combined with ray-casting. Nonetheless, these approaches work for room sized and sparse environments, and they do not scale to larger scenarios with many objects. We introduce PRECIOUS, a novel mid-air technique for selecting out-of-reach objects. It employs an iterative progressive refinement, using cone-casting to select multiple objects and moving users closer to them in each step, allowing accurate selections. A user evaluation showed that PRECIOUS compares favorably against existing approaches, being the most versatile. © 2017 IEEE.

Abstract

Abstract
Bad posture while working or playing videogames can affect our life quality and impose negative economic consequences over time. There's raising concern in companies regarding worker's wellness, many adopting preventive measures. Specialized training in posture is important to prevent occupational activities risks and to foster health promotion. In this paper, we present a study of different classifiers to detect good and bad body postures in workplaces. A set classifiers, namely artificial neural networks, support vector machine, decision trees, discriminant analysis, logistic regression, treebagger and naïve Bayes, were tested in three-dimensional acquisitions of 100 people for automatic determination of the type of body posture. The best classifier was the treebagger with a rating of True Positive and True Negative of 93.3% and 96.2%, respectively.

Abstract
According to the World Health Organization, 85% of the world population suffers from back pain, which accounts for over 50% of physical incapacity, permanent or temporary, among individuals in working-age. In most situations, this is caused by an incorrect posture, which causes changes in the spine structure. This paper proposes an instrumented vest for postural reeducation to address this issue. The vest has a set of inertial measurement unit (IMU) sensors strategically placed to provide an accurate characterization of the spine profile. The sensor readings are classified by a central processing unit. In case of an incorrect posture, users are alerted by an audio signal and through vibration. The wearable system works in stand-alone mode, but can also communicate with external systems through an API. Two applications were developed to communicate with the device through this API, one intended to run on a desktop computer and the other one for Android devices. These applications monitor spine profiles in real time and notify the user of incorrect postures, among other functionalities. The device prototype and the applications have been tested by 10 individuals in two different settings, first without any kind of feedback and then with feedback enabled. The tests demonstrate the usability, accuracy and robustness of the system, proving its high level of reliability in classifying postures and effectiveness for postural reeducation. In the future, the system is expected to be used as a platform for a serious game, to promote posture reeducation in a real world scenario.