5 research outputs found
Novel Approaches in Landslide Monitoring and Data Analysis
Significant progress has been made in the last few years that has expanded the knowledge of landslide processes. It is, therefore, necessary to summarize, share and disseminate the latest knowledge and expertise. This Special Issue brings together novel research focused on landslide monitoring, modelling and data analysis
Remote Sensing Applications in Coastal Environment
Coastal regions are susceptible to rapid changes, as they constitute the boundary between the land and the sea. The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment. This book contains 12 high-quality and innovative scientific papers that explore, evaluate, and implement the use of remote sensing sensors within both natural and built coastal environments
Tailoring Interaction. Sensing Social Signals with Textiles.
Nonverbal behaviour is an important part of conversation and can reveal much about the nature of an interaction. It includes phenomena ranging from large-scale posture shifts to small scale nods. Capturing these often spontaneous phenomena requires unobtrusive sensing techniques that do not interfere with the interaction. We propose an underexploited sensing modality for sensing nonverbal behaviours: textiles. As a material in close contact with the body, they provide ubiquitous, large surfaces that make them a suitable soft interface. Although the literature on nonverbal communication focuses on upper body movements such as gestures, observations of multi-party, seated conversations suggest that sitting postures, leg and foot movements are also systematically related to patterns of social interaction. This thesis addressees the following questions: Can the textiles surrounding us measure social engagement? Can they tell who is speaking, and who, if anyone, is listening? Furthermore, how should wearable textile sensing systems be designed and what behavioural signals could textiles reveal? To address these questions, we have designed and manufactured bespoke chairs and trousers with integrated textile pressure sensors, that are introduced here. The designs are evaluated in three user studies that produce multi-modal datasets for the exploration of fine-grained interactional signals. Two approaches to using these bespoke textile sensors are explored. First, hand crafted sensor patches in chair covers serve to distinguish speakers and listeners. Second, a pressure sensitive matrix in custom-made smart trousers is developed to detect static sitting postures, dynamic bodily movement, as well as basic conversational states. Statistical analyses, machine learning approaches, and ethnographic methods show that by moni- toring patterns of pressure change alone it is possible to not only classify postures with high accuracy, but also to identify a wide range of behaviours reliably in individuals and groups. These findings es- tablish textiles as a novel, wearable sensing system for applications in social sciences, and contribute towards a better understanding of nonverbal communication, especially the significance of posture shifts when seated. If chairs know who is speaking, if our trousers can capture our social engagement, what role can smart textiles have in the future of human interaction? How can we build new ways to map social ecologies and tailor interactions
Applications of Photogrammetry for Environmental Research
ISPRS International Journal of Geo-Information: special issue entitled "Applications of Photogrammetry for Environmental Research
Pressure ulcer risk assessment and prevention system design.
Pressure ulcer (PU, bedsore, ischemia, decubitus ulcer) has become a global healthcare problem. In United Kingdom 412,000 people develop pressure ulcer annually and it costs the National Health Service (NHS) £1.4-£2.1 billion pounds (4% of total NHS budget). Pressure ulcers are a combined result of multiple factors such as prolonged external load applied to the skin, reduced blood flow in tissues, the patient’s physiological parameters (body mass index, age, mobility) and body support surface properties. The aetiology of pressure ulcer formation includes both mechanical and biological properties of skin and soft tissues. In order to prevent PU formation in the human body, a new type of risk predicting tool is required where identification of PU risk is based on combined effect of patient’s physiological parameters and support surface properties. Previous research suggests that interface pressure (IP) of 32 mmHg (4.2kPa) can cause PU but there is no strong evidence to show when that pressure is reached. Also IP varies from person to person due to their physiology. There are three risk assessment scales available to predict the occurrence of PU formation; however, none of these scales take interaction of body support surface material into account. Also they do not provide any information at which area a person is at risk of ulceration. In order to identify the harmful IP, biomechanical behaviour of skin and soft tissue is modelled and interaction of body support surface is studied. A mathematical model has been developed to characterise a new type of body support surface material (viscoelastic) and validated by conducting experiments. The relationship between patient’s physiological parameters and surface material are identified along with risk assessment scales for pressure ulcer prediction by conducting experiments. External load at different bony areas are measured using eleven volunteers. By measuring the external load for eleven subjects (age =33±7) and (BMI =25.0±3.01 kg/m2) at different bony areas, the relationship between IP with the total body weight and BMI was developed. A mathematical model is proposed to predict the risk of PU formation combining the Waterlow risk assessment scale and risk prediction algorithms on a user friendly interface