Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

BIOMECHANICAL MARKERS AS INDICATORS OF POSTURAL INSTABILITY PROGRESSION IN PARKINSON'S DISEASE

The long term objective of this research is to identify quantitative biomechanical parameters of postural instability in patients with Parkinson’s disease (PD) that can in turn be used to assess fall risk. Currently, clinical assessments in PD are not sufficiently sensitive to predict fall risk, making a history of falls to be the best predictor of a future fall. Identifying biomechanical measures to predict risk of falls in PD would provide a quantitative justification to implement fall-reducing therapies prior to a first fall and help prevent the associated debilitating fractures or even morbidity. While past biomechanical studies have shown the presence of balance deficits in PD patients, which often include a broad spectrum of disease stages, compared to healthy controls (HC), no studies have assessed whether such parameters can distinguish the onset of postural instability prior to clinical presentation, and if such parameters persist following clinical presentation of postural instability. Toward this end this study had three goals: • Determine if biomechanical assessment of a quasi-static task, postural sway, could provide preclinical indication of postural instability in PD. • Define a mathematical model (based on principal component analysis, PCA) with biomechanical and clinical measures as inputs to quantitatively score earlier postural instability presence and progression in PD. • Investigate if biomechanical assessment of a dynamic task, gait initiation, could provide preclinical indication of postural instability in PD. Specific Aim 1 determined that some biomechanical postural sway variables showed evidence of preclinical postural instability and increased with PD progression. This aim distinguished mild PD (Hoehn and Yahr stage (H&Y) 2, without postural deficits) compared to HC suggesting preclinical indication of postural instability, and confirmed these parameters persisted in moderate PD (H&Y 3, with postural deficits). Specifically, trajectory, variation, and peak measures of the center of pressure (COP) during postural sway showed significant differences (p < .05) in mild PD compared to healthy controls, and these differences persisted in moderate PD. Schwab and England clinical score best correlated with the COP biomechanical measures. These results suggest that postural sway COP measures may provide preclinical indication of balance deficits in PD and increase with clinical PD progression. Specific Aim 2 defined a PCA model based on biomechanical measures of postural sway and clinical measures in mild PD, moderate PD, and HC. PCA modeling based on a correlation matrix structure identified both biomechanical and clinical measures as the primary drivers of variation in the data set. Further, a PCA model based on these selected parameters was able to significantly differentiate (p < .05) all 3 groups, suggesting PCA scores may help with preclinical indication of postural instability (mild PD versus HC) and could be sensitive to clinical disease progression (mild PD versus moderate PD and moderate PD versus HC). AP sway path length and a velocity parameter were the 2 primary measures that explained the variability in the data set, suggesting further investigation of these parameters and mathematical models for scoring postural instability progression is warranted. Specific Aim 3 determined that a velocity measure from biomechanical assessment of gait initiation (peak COP velocity towards the swing foot during locomotion) showed evidence of preclinical postural instability in PD. Because balance is a complex task, having a better understanding of both quasi-static (postural sway) and dynamic (gait initiation) tasks can provide further insight about balance deficits resulting from PD. Several temporal and kinematic parameters changed with increasing disease progression, with significant difference in moderate PD versus HC, but missed significance in mild PD compared to HC. Total Unified Parkinson’s Disease Rating Scale (UPDRS) and Pull Test clinical scores best correlated with the biomechanical measures of the gait initiation response. These results suggest dynamic biomechanical assessment may provide additional information in quantifying preclinical postural instability and progression in PD. In summary, reducing fall risk in PD is a high priority effort to maintain quality of life by allowing continued independence and safe mobility. Since no effective screening method exists to measure fall risk, our team is developing a multi-factorial method to detect postural instability through clinical balance assessment, and in doing so, provide the justification for implementing fall reducing therapies before potentially debilitating falls begin

Human spatial navigation in the digital era: Effects of landmark depiction on mobile maps on navigators’ spatial learning and brain activity during assisted navigation

Navigation was an essential survival skill for our ancestors and is still a fundamental activity in our everyday lives. To stay oriented and assist navigation, our ancestors had a long history of developing and employing physical maps that communicated an enormous amount of spatial and visual information about their surroundings. Today, in the digital era, we are increasingly turning to mobile navigation devices to ease daily navigation tasks, surrendering our spatial and navigational skills to the hand-held device. On the flip side, the conveniences of such devices lead us to pay less attention to our surroundings, make fewer spatial decisions, and remember less about the surroundings we have traversed. As navigational skills and spatial memory are related to adult neurogenesis, healthy aging, education, and survival, scientists and researchers from multidisciplinary fields have made calls to develop a new account of mobile navigation assistance to preserve human navigational abilities and spatial memory. Landmarks have been advocated for special attention in developing cognitively supportive navigation systems, as landmarks are widely accepted as key features to support spatial navigation and spatial learning of an environment. Turn-by-turn direction instructions without reference to surrounding landmarks, such as those provided by most existing navigation systems, can be one of the reasons for navigators’ spatial memory deterioration during assisted navigation. Despite the benefit of landmarks in navigation and spatial learning, long-standing literature on cognitive psychology has pointed out that individuals have only a limited cognitive capacity to process presented information for a task. When the learning items exceed learners’ capacity, the performance may reach a plateau or even drop. This leads to an unexamined yet important research question on how to visualize landmarks on a mobile map to optimize navigators’ cognitive resource exertion and thus optimize their spatial learning. To investigate this question, I leveraged neuropsychological and hypothesis-driven approaches and investigated whether and how different numbers of landmarks depicted on a mobile map affected navigators’ spatial learning, cognitive load, and visuospatial encoding. Specifically, I set out a navigation experiment in three virtual urban environments, in which participants were asked to follow a given route to a specific destination with the aid of a mobile map. Three different numbers of landmarks—3, 5, and 7—along the given route were selected based on cognitive capacity literature and presented to 48 participants during map-assisted navigation. Their brain activity was recorded both during the phase of map consultation and during that of active locomotion. After navigation in each virtual city, their spatial knowledge of the traversed routes was assessed. The statistical results revealed that spatial learning improved when a medium number of landmarks (i.e., five) was depicted on a mobile map compared to the lowest evaluated number (i.e., three) of landmarks, and there was no further improvement when the highest number (i.e., seven) of landmarks were provided on the mobile map. The neural correlates that were interpreted to reflect cognitive load during map consultation increased when participants were processing seven landmarks depicted on a mobile map compared to the other two landmark conditions; by contrast, the neural correlates that indicated visuospatial encoding increased with a higher number of presented landmarks. In line with the cognitive load changes during map consultation, cognitive load during active locomotion also increased when participants were in the seven-landmark condition, compared to the other two landmark conditions. This thesis provides an exemplary paradigm to investigate navigators’ behavior and cognitive processing during map-assisted navigation and to utilize neuropsychological approaches to solve cartographic design problems. The findings contribute to a better understanding of the effects of landmark depiction (3, 5, and 7 landmarks) on navigators’ spatial learning outcomes and their cognitive processing (cognitive load and visuospatial encoding) during map-assisted navigation. Of these insights, I conclude with two main takeaways for audiences including navigation researchers and navigation system designers. First, the thesis suggests a boundary effect of the proposed benefits of landmarks in spatial learning: providing landmarks on maps benefits users’ spatial learning only to a certain extent when the number of landmarks does not increase cognitive load. Medium number (i.e., 5) of landmarks seems to be the best option in the current experiment, as five landmarks facilitate spatial learning without taxing additional cognitive resources. The second takeaway is that the increased cognitive load during map use might also spill over into the locomotion phase through the environment; thus, the locomotion phase in the environment should also be carefully considered while designing a mobile map to support navigation and environmental learning

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Recent Advances in Motion Analysis

The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application

Autonomous Systems, Robotics, and Computing Systems Capability Roadmap: NRC Dialogue

Contents include the following: Introduction. Process, Mission Drivers, Deliverables, and Interfaces. Autonomy. Crew-Centered and Remote Operations. Integrated Systems Health Management. Autonomous Vehicle Control. Autonomous Process Control. Robotics. Robotics for Solar System Exploration. Robotics for Lunar and Planetary Habitation. Robotics for In-Space Operations. Computing Systems. Conclusion