3633 research outputs found
Sort by
Optimal position and orientation of an ossicular accelerometer for human auditory prostheses
Get PDFIn this study, a method for determining the optimal location and orientation of an implantable piezoelectric accelerometer on the short process of the incus is presented. The accelerometer is intended to be used as a replacement for an external microphone to enable totally implantable auditory prostheses. The optimal orientation of the sensor and the best attachment point are determined based on two criteria—maximum pressure sensitivity sum and minimum loudness level sum. The best location is determined to be near the incudomalleolar joint. We find that the angular orientation of the sensor is critical and provide guidelines on that orientation. The method described in this paper can be used to further optimize the design and performance of the accelerometer
Development of a stochastic finite element model for use in the diagnosis of middle-ear pathologies
Get PDFThe calibrated model accurately reproduces the mean and variance of middle-ear measurements like impedance, reflectance, stapes and umbo transfer function. Ligament and joint material parameters have a significant effect on the variance of these measurements, while variations in center of mass positions, for example, have less effect. The neural network trained on the simulated data shows promise for diagnostics, achieving 86-100% sensitivity and 85-93% specificity for detecting otosclerosis and disarticulation, which is similar to the performance of classifiers trained on measured immittance data
Assessing body position during sleep using FSR sensors and machine learning algorithms
No full textThis study investigates the application of Force Sensing Resistor (FSR) sensors and machine learning algorithms for non-invasive body position monitoring during sleep. Although reliable, traditional methods like Polysomnography (PSG) are invasive and unsuited for extended home-based monitoring. Our approach utilizes FSR sensors placed beneath the mattress to detect body positions effectively. We employed machine learning techniques, specifically Random Forest (RF), K-Nearest Neighbors (KNN), and XGBoost algorithms, to analyze the sensor data. The models were trained and tested using data from a controlled study with 15 subjects assuming various sleep positions. The performance of these models was evaluated based on accuracy and confusion matrices. The results indicate XGBoost as the most effective model for this application, followed by RF and KNN, offering promising avenues for home-based sleep monitoring systems
Intuitive multi-modal human-robot interaction via posture and voice
No full textCollaborative robots promise to greatly improve the quality-of-life for the aging population and also easing elder care. However existing systems often rely on hand gestures, which can be restrictive and less accessible for users with cognitive disability. This paper introduces a multi-modal command input, which combines voice and deictic postures, to create a natural humanrobot interaction. In addition, we combine our system with a chatbot to make the interaction responsive. The demonstrated deictic postures, voice and the perceived table-top scene are processed in real-time to extract the human’s intention. The system is evaluated for increasingly complex tasks using a real Universal Robots UR3e 6-DoF robot arm. The preliminary results demonstrate a high success rate in task completion and a notable improvement compared to gesture-based systems. Controlling robots through multi-modal commands, as opposed to gesture control, can save up to 48.1% of the time taken to issue commands to the robot. Our system adeptly integrates the advantages of voice commands and deictic postures to facilitate intuitive human-robot interaction. Compared to conventional gesture control methods, our approach requires minimal training, eliminating the need to memorize complex gestures, and results in shorter interaction times
Non-invasive system for measuring parameters relevant to sleep quality and detecting sleep diseases: the data model
No full textHealthy and good sleep is a prerequisite for a rested mind and body. Both form the basis for physical and mental health. Healthy sleep is hindered by sleep disorders, the medically diagnosed frequency of which increases sharply from the age of 40. This chapter describes the formal specification of an on-course practical implementation for a non-invasive system based on biomedical signal processing to support the diagnosis and treatment of sleep-related diseases. The system aims to continuously monitor vital data during sleep in a patient’s home environment over long periods by using non-invasive technologies. At the center of the development is the MORPHEUS Box (MoBo), which consists of five main conceptualizations: the MoBo core, the MoBo-HW, the MoBo algorithm, the MoBo API, and the MoBo app. These synergistic elements aim to support the diagnosis and treatment of sleep-related diseases. Although there are related developments in individual aspects concerning the system, no comparative approach is known that gives a similar scope of functionality, deployment flexibility, extensibility, or the possibility to use multiple user groups. With the specification provided in this chapter, the MORPHEUS project sets a good platform, data model, and transmission strategies to bring an innovative proposal to measure sleep quality and detect sleep diseases from non-invasive sensors
Smart maintenance system for inner city public bus services
Get PDFAs part of the emerging Industry 4.0 movement, maintenance is increasingly being digitalized. This trend sees the realization of smart maintenance strategies and systems that allow organizations to take better control of their maintenance. The reality is that the maintenance approaches and systems that are currently in use at inner city public bus services are based on previous technological and industrialization methods. Smart maintenance and Industry 4.0 technologies can be used to optimize maintenance at inner city public bus services to support informed decision-making. This paper presents the development of a smart maintenance system for these organizations to minimize the downtime caused by unexpected breakdowns and ensure inner city buses operate reliably. Designing the smart maintenance system is done by considering the findings from an extensive literature review and the feedback from structured interviews with bus service representatives. The system validation is performed as a case study with an industry partner. For conducting the case study, a concept demonstrator is designed according to the defined system and addresses the main problem causing downtime of the buses at the industry partner. Tests are conducted with the demonstrator to verify the smart maintenance system's functionalities
An array of double-Cornu spiral antenna
No full textBased on a framework recently published, the double-Cornu spiral antenna is extended to an array to enhance the gain. The designed array of 2×2-elements is of low profile and small sizes, has however a large effective bandwidth, and shows overall good radiation characteristics: enhanced gain, large axial ratio bandwidth, and high degree of polarization purity. Except for a few deviations, which are due to manufacturing tolerances, artificial noise and measurement uncertainties on the one hand and diffracted waves at external edges on the other, simulated results and experimental data fit well together. In addition, EMC along with signal integrity issues related to the reduction of noise and unwanted radiation have been addressed. The proposed antenna is suitable for 5G applications and radar systems. With 14.02 dB realized gain, 6.2 GHz effective bandwidth and an uplink data rate of 3.44 Mbit/s, the array is promising for many mobility applications
Automatic gear tooth alignment in vision based preventive maintenance
Get PDFThorough maintenance of industrial equipment is crucial for the finances of companies. Whereas the purchase of new tools can be an expensive business, reconditioning special gear often costs just a fraction. In this paper, preliminary steps for an accurate visual based preventive maintenance of hobbing wheels are investigated. To perform robust and reliable decisions about the wheel's condition, tool department specialists require precise taken captures of it. For this reason, a visual control cell is built, which depends on correctly aligned hobbing wheels in its image acquisition construction. The tool needs to be placed on a turn-table and rotated, so that a single tooth is centered in the field-of-view of the camera mounted on a robot arm. For this alignment task, three different main approaches with various preprocessing steps are investigated, a brute-force algorithm, an orb-feature approach and an image regression model. The results show that even a brute-force algorithm can be outperformed by a moderate deep neural network
The case of the soccer world cup 2022 in Qatar and sustainability: comparison of FIFA's plan (target) and public perception (actual)
Get PDFNo other mega sporting event has ever been as controversial as the 2022 soccer World Cup in Qatar. This paper does not look at how the World Cup was awarded to Qatar and the role played by corruption and the power of money. Instead, this case study will take a closer look at the sustainability of the event. Was it possible to keep the promises made by Fédération Internationale de Football Association (FIFA) and the state of Qatar in the run-up to the event? To what extent do FIFA's self-image and the public's perception of it match? In the analysis (target/actual comparison), the dialectical three-step method (with thesis, antithesis, and synthesis) is used
Determinants of customer recovery in retail banking : lessons from a German banking case study
Get PDFDue to the increased willingness of retail banking customers to switch and churn their banking relationships, a question arises: Is it possible to win back lost customers, and if so, is such a possibility even desirable after all economic factors have been considered? To answer these questions, this paper examines selected determinants for the recovery of terminated customer–bank relationships from the perspective of former customers. This study therefore evaluates for the first time, empirically and systematically with reference to a German Sparkasse as a case-study setting, whether lost customers have a sufficient general willingness to return (GWR) a retail banking relationship. From our results, a correlation is shown between the GWR a banking relationship and some specific determinants: seeking variety, attractiveness of alternatives and customer satisfaction with the former business relationship. In addition, we show that a customer’s GWR varies depending on the reason for churn and is surprisingly greater when the customer defected for reasons that lie within the scope of the customer himself. Despite the case-study character, however, our results provide relevant insights for other banks and, in particular, this applies to countries with a comparable banking system