We present the results of an evaluation of the performance of the Leap Motion Controller with the aid of a professional, high-precision, fast motion tracking system. A set of static and dynamic measurements was performed with different numbers of tracking objects and configurations. For the static measurements, a plastic arm model simulating a human arm was used. A set of 37 reference locations was selected to cover the controller’s sensory space. For the dynamic measurements, a special V-shaped tool, consisting of two tracking objects maintaining a constant distance between them, was created to simulate two human fingers. In the static scenario, the standard deviation was less than 0.5 mm. The linear correlation revealed a significant increase in the standard deviation when moving away from the controller. The results of the dynamic scenario revealed the inconsistent performance of the controller, with a significant drop in accuracy for samples taken more than 250 mm above the controller’s surface. The Leap Motion Controller undoubtedly represents a revolutionary input device for gesture-based human-computer interaction; however, due to its rather limited sensory space and inconsistent sampling frequency, in its current configuration it cannot currently be used as a professional tracking system.

Real-time biomechanical feedback (BMF) is a relatively new area of research. The potential of using advanced technology to improve motion skills in sport and accelerate physical rehabilitation has been demonstrated in a number of studies. This paper provides a literature review of BMF systems in sports and rehabilitation. Our motivation was to examine the history of the field to capture its evolution over time, particularly how technologies are used and implemented in BMF systems, and to identify the most recent studies showing novel solutions and remarkable implementations. We searched for papers in three research databases: Scopus, Web of Science, and PubMed. The initial search yielded 1167 unique papers. After a rigorous and challenging exclusion process, 144 papers were eventually included in this report. We focused on papers describing applications and systems that implement a complete real-time feedback loop, which must include the use of sensors, real-time processing, and concurrent feedback. A number of research questions were raised, and the papers were studied and evaluated accordingly. We identified different types of physical activities, sensors, modalities, actuators, communications, settings and end users. A subset of the included papers, showing the most perspectives, was reviewed in depth to highlight and present their innovative research approaches and techniques. Real-time BMF has great potential in many areas. In recent years, sensors have been the main focus of these studies, but new types of processing devices, methods, and algorithms, actuators, and communication technologies and protocols will be explored in more depth in the future. This paper presents a broad insight into the field of BMF.

Smartphone sensors are being increasingly used in mobile applications. The performance of sensors varies considerably among different smartphone models and the development of a cross-platform mobile application might be a very complex and demanding task. A publicly accessible resource containing real-life-situation smartphone sensor parameters could be of great help for cross-platform developers. To address this issue we have designed and implemented a pilot participatory sensing application for measuring, gathering, and analyzing smartphone sensor parameters. We start with smartphone accelerometer and gyroscope bias and noise parameters. The application database presently includes sensor parameters of more than 60 different smartphone models of different platforms. It is a modest, but important start, offering information on several statistical parameters of the measured smartphone sensors and insights into their performance. The next step, a large-scale cloud-based version of the application, is already planned. The large database of smartphone sensor parameters may prove particularly useful for cross-platform developers. It may also be interesting for individual participants who would be able to check-up and compare their smartphone sensors against a large number of similar or identical models.

We propose calibration methods for microelectromechanical system (MEMS) 3D accelerometers and gyroscopes that are efficient in terms of time and computational complexity. The calibration process for both sensors is simple, does not require additional expensive equipment, and can be performed in the field before or between motion measurements. The methods rely on a small number of defined calibration measurements that are used to obtain the values of 12 calibration parameters. This process enables the static compensation of sensor inaccuracies. The values detected by the 3D sensor are interpreted using a generalized 3D sensor model. The model assumes that the values detected by the sensor are equal to the projections of the measured value on the sensor sensitivity axes. Although this finding is trivial for 3D accelerometers, its validity for 3D gyroscopes is not immediately apparent; thus, this paper elaborates on this latter topic. For an example sensor device, calibration parameters were established using calibration measurements of approximately 1.5 min in duration for the 3D accelerometer and 2.5 min in duration for the 3D gyroscope. Correction of each detected 3D value using the established calibration parameters in further measurements requires only nine addition and nine multiplication operations.

Autonomous vehicles are expected to take complete control of the driving process, enabling the former drivers to act as passengers only. This could lead to increased sickness as they can be engaged in tasks other than driving. Adopting different sickness mitigation techniques gives us unique types of motion sickness in autonomous vehicles to be studied. In this paper, we report on a study where we explored the possibilities of assessing motion sickness with electrogastrography (EGG), a non-invasive method used to measure the myoelectric activity of the stomach, and its potential usage in autonomous vehicles (AVs). The study was conducted in a high-fidelity driving simulator with a virtual reality (VR) headset. There separate EGG measurements were performed: before, during and after the driving AV simulation video in VR. During the driving, the participants encountered two driving environments: a straight and less dynamic highway road and a highly dynamic and curvy countryside road. The EGG signal was recorded with a proprietary 3-channel recording device and Ag/AgCl cutaneous electrodes. In addition, participants were asked to signalize whenever they felt uncomfortable and nauseated by pressing a special button. After the drive they completed also the Simulator Sickness Questionnaire (SSQ) and reported on their overall subjective perception of sickness symptoms. The EGG results showed a significant increase of the dominant frequency (DF) and the percentage of the high power spectrum density (FSD) as well as a significant decrease of the power spectrum density Crest factor (CF) during the AV simulation. The vast majority of participants reported nausea during more dynamic conditions, accompanied by an increase in the amplitude and the RMS value of EGG. Reported nausea occurred simultaneously with the increase in EGG amplitude. Based on the results, we conclude that EGG could be used for assessment of motion sickness in autonomous vehicles. DF, CF and FSD can be used as overall sickness indicators, while the relative increase in amplitude of EGG signal and duration of that increase can be used as short-term sickness indicators where the driving environment may affect the driver.