Autonomous Flight Using UWB-Based Positioning System with Optical Flow Sensors in a GPS-Denied Environment

This study presents the positioning method and autonomous flight of a quadrotor drone using ultra-wideband (UWB) communication and an optical flow sensor. UWB communication obtains the distance between multiple ground stations and a mobile station on a robot, and the position is calculated based on a multilateration method similar to global positioning system (GPS). The update rate of positioning using only UWB communication devices is slow;hence, we improved the update rate by combining the UWB and inertial measurement unit (IMU) sensor in the prior study. This study demonstrates the improvement of the positioning method and accuracy by sensor fusion of the UWB device, an IMU, and an optical flow sensor using the extended Kalman filter. The proposed method is validated by hovering and position control experiments and also realizes a sufficient rate and accuracy for autonomous flight.

Wearable Sensor Data for Classification and Analysis of Functional Fitness Exercises Using Unsupervised Deep Learning Methodologies

Healthcare institutions, policymakers, and leaders around the world all agree that improving people’s health and livelihoods is our number one priority. Aging, disability, long-term care, and palliative care all pose significant challenges to the burden of illness and the health system. Wearable technology has a number of healthcare applications, from patient care to personal health. Wearable devices, sensors, mobile apps, and tracking technologies are essential for the diagnosis, prevention, monitoring, and treatment of chronic diseases. Create and test a method to automatically classify four functional fitness exercises commonly used in current circuit training routines. The proposed algorithm, fuzzy local feature C-means algorithm (FLFCM), enhanced with information-maximizing generative adversarial network, was used to locate five inertial measurement units on the upper and lower limbs, as well as the trunk, of fourteen participants (INFOGAN). The proposed method is suitable for this situation because it yields promising results.

Real-Time Map Matching with a Backtracking Particle Filter Using Geospatial Analysis

Inertial odometry is a typical localization method that is widely and easily accessible in many devices. Pedestrian positioning can benefit from this approach based on inertial measurement unit (IMU) values embedded in smartphones. Fitting the inertial odometry outputs, namely step length and step heading of a human for instance, with spatial information is an ubiquitous way to correct for the cumulative noises. This so-called map-matching process can be achieved in several ways. In this paper, a novel real-time map-matching approach was developed, using a backtracking particle filter that benefits from the implemented geospatial analysis, which reduces the complexity of spatial queries and provides flexibility in the use of different kinds of spatial constraints. The goal was to generalize the algorithm to permit the use of any kind of odometry data calculated by different sensors and approaches as the input. Further research, development, and comparisons have been done by the easy implementation of different spatial constraints and use cases due to the modular structure. Additionally, a simple map-based optimization using transition areas between floors has been developed. The developed algorithm could achieve accuracies of up to 3 m at approximately the 90th percentile for two different experiments in a complex building structure.