Protocol for the DeFOG trial: A randomized controlled trial on the effects of smartphone-based, on-demand cueing for freezing of gait in Parkinson's disease

Background: Freezing of gait (FOG) is a highly incapacitating symptom that affects many people with Parkinson's disease (PD). Cueing triggered upon real-time FOG detection (on-demand cueing) shows promise for FOG treatment. Yet, the feasibility of implementation and efficacy in daily life is still unknown. Therefore, this study aims to investigate the effectiveness of DeFOG: a smartphone and sensor-based on-demand cueing solution for FOG. Methods: Sixty-two PD patients with FOG will be recruited for this single-blind, multi-center, randomized controlled phase II trial. Patients will be randomized into either the intervention group or the active control group. For four weeks, both groups will receive feedback about their physical activity using the wearable DeFOG system in daily life. In addition, the intervention group will also receive on-demand auditory cueing and instructions. Before and after the intervention, home-based assessments will be performed to evaluate the primary outcome, i.e., “percentage time frozen” during a FOG-provoking protocol. Secondary outcomes include the training effects on physical activity monitored over 7 days and the user-friendliness of the technology. Discussion: The DeFOG trial will investigate the effectiveness of personalized on-demand cueing in a controlled design, delivered for 4 weeks in the patient's home environment. We anticipate that DeFOG will reduce FOG to a greater degree than in the control group and we will explore the impact of the intervention on physical activity levels. We expect to gain in-depth insight into whether and how patients control FOG using cueing methods in their daily lives. Trial registration: Clinicaltrials.gov NCT03978507

Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization

Towards Naturalistic Interfaces of Virtual Reality Systems

Interaction plays a key role in achieving realistic experience in virtual reality (VR). Its realization depends on interpreting the intents of human motions to give inputs to VR systems. Thus, understanding human motion from the computational perspective is essential to the design of naturalistic interfaces for VR. This dissertation studied three types of human motions, including locomotion (walking), head motion and hand motion in the context of VR. For locomotion, the dissertation presented a machine learning approach for developing a mechanical repositioning technique based on a 1-D treadmill for interacting with a unique new large-scale projective display, called the Wide-Field Immersive Stereoscopic Environment (WISE). The usability of the proposed approach was assessed through a novel user study that asked participants to pursue a rolling ball at variable speed in a virtual scene. In addition, the dissertation studied the role of stereopsis in avoiding virtual obstacles while walking by asking participants to step over obstacles and gaps under both stereoscopic and non-stereoscopic viewing conditions in VR experiments. In terms of head motion, the dissertation presented a head gesture interface for interaction in VR that recognizes real-time head gestures on head-mounted displays (HMDs) using Cascaded Hidden Markov Models. Two experiments were conducted to evaluate the proposed approach. The first assessed its offline classification performance while the second estimated the latency of the algorithm to recognize head gestures. The dissertation also conducted a user study that investigated the effects of visual and control latency on teleoperation of a quadcopter using head motion tracked by a head-mounted display. As part of the study, a method for objectively estimating the end-to-end latency in HMDs was presented. For hand motion, the dissertation presented an approach that recognizes dynamic hand gestures to implement a hand gesture interface for VR based on a static head gesture recognition algorithm. The proposed algorithm was evaluated offline in terms of its classification performance. A user study was conducted to compare the performance and the usability of the head gesture interface, the hand gesture interface and a conventional gamepad interface for answering Yes/No questions in VR. Overall, the dissertation has two main contributions towards the improvement of naturalism of interaction in VR systems. Firstly, the interaction techniques presented in the dissertation can be directly integrated into existing VR systems offering more choices for interaction to end users of VR technology. Secondly, the results of the user studies of the presented VR interfaces in the dissertation also serve as guidelines to VR researchers and engineers for designing future VR systems

Stereo-vision-based navigation of a six-legged walking robot in unknown rough terrain

In this paper we presents a visual navigation algorithm for the six-legged walking robot DLR Crawler in rough terrain. The algorithm is based on stereo images from which depth images are computed using the semi- global matching (SGM) method. Further, a visual odometry is calculated along with an error measure. Pose estimates are obtained by fusing iner- tial data with relative leg odometry and visual odometry measurements using an indirect information filter. The visual odometry error measure is used in the filtering process to put lower weights on erroneous visual odometry data, hence, improving the robustness of pose estimation. From the estimated poses and the depth images, a dense digital terrain map is created by applying the locus method. The traversability of the terrain is estimated by a plane fitting approach and paths are planned using a D* Lite planner taking the traversability of the terrain and the current motion capabilities of the robot into account. Motion commands and the traversability measures of the upcoming terrain are sent to the walking layer of the robot so that it can choose an appropriate gait for the terrain. Experimental results show the accuracy of the navigation algorithm and its robustness against visual disturbances

Climbing and Walking Robots

With the advancement of technology, new exciting approaches enable us to render mobile robotic systems more versatile, robust and cost-efficient. Some researchers combine climbing and walking techniques with a modular approach, a reconfigurable approach, or a swarm approach to realize novel prototypes as flexible mobile robotic platforms featuring all necessary locomotion capabilities. The purpose of this book is to provide an overview of the latest wide-range achievements in climbing and walking robotic technology to researchers, scientists, and engineers throughout the world. Different aspects including control simulation, locomotion realization, methodology, and system integration are presented from the scientific and from the technical point of view. This book consists of two main parts, one dealing with walking robots, the second with climbing robots. The content is also grouped by theoretical research and applicative realization. Every chapter offers a considerable amount of interesting and useful information

Mechatronic Systems

Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools

Robust localization with wearable sensors

Measuring physical movements of humans and understanding human behaviour is useful in a variety of areas and disciplines. Human inertial tracking is a method that can be leveraged for monitoring complex actions that emerge from interactions between human actors and their environment. An accurate estimation of motion trajectories can support new approaches to pedestrian navigation, emergency rescue, athlete management, and medicine. However, tracking with wearable inertial sensors has several problems that need to be overcome, such as the low accuracy of consumer-grade inertial measurement units (IMUs), the error accumulation problem in long-term tracking, and the artefacts generated by movements that are less common. This thesis focusses on measuring human movements with wearable head-mounted sensors to accurately estimate the physical location of a person over time. The research consisted of (i) providing an overview of the current state of research for inertial tracking with wearable sensors, (ii) investigating the performance of new tracking algorithms that combine sensor fusion and data-driven machine learning, (iii) eliminating the effect of random head motion during tracking, (iv) creating robust long-term tracking systems with a Bayesian neural network and sequential Monte Carlo method, and (v) verifying that the system can be applied with changing modes of behaviour, defined as natural transitions from walking to running and vice versa. This research introduces a new system for inertial tracking with head-mounted sensors (which can be placed in, e.g. helmets, caps, or glasses). This technology can be used for long-term positional tracking to explore complex behaviours