Activities of daily living are important for assessing changes in physical and behavioral profiles of the general population over time, particularly for the elderly and patients with chronic diseases. Although accelerometers have been used widely in wearable devices for activity classification, the positioning of the sensors and the selection of relevant features for different activity groups still pose significant research challenges. This paper investigates wearable sensor placement at different body positions and aims to provide a systematic framework that can answer the following questions: 1) What is the ideal sensor location for a given group of activities? and 2) Of the different time-frequency features that can be extracted from wearable accelerometers, which ones are the most relevant for discriminating different activity types?

No abstract

Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market.

The Jovian Trojans are two swarms of objects located around the L4 and L5 Lagrange points. The population is thought to have been captured by Jupiter during the Solar system’s youth. Within the swarms, six collisional families have been identified in previous work, with four in the L4 swarm, and two in the L5. Our aim is to investigate the stability of the two Trojan swarms, with a particular focus on these collisional families. We find that the members of Trojan swarms escape the population at a linear rate, with the primordial L4 (23.35 per cent escape) and L5 (24.89 per cent escape) population sizes likely 1.31 and 1.35 times larger than today. Given that the escape rates were approximately equal between the two Trojan swarms, our results do not explain the observed asymmetry between the two groups, suggesting that the numerical differences are primordial in nature, supporting previous studies. Upon leaving the Trojan population, the escaped objects move on to orbits that resemble those of the Centaur and short-period comet populations. Within the Trojan collisional families, the 1996 RJ and 2001 UV209 families are found to be dynamically stable over the lifetime of the Solar system, whilst the Hektor, Arkesilos and Ennomos families exhibit various degrees of instability. The larger Eurybates family shows 18.81 per cent of simulated members escaping the Trojan population. Unlike the L4 swarm, the escape rate from the Eurybates family is found to increase as a function of time, allowing an age estimation of approximately 1.045 ± 0.364 × 109 yr.

Self-report underpins our understanding of falls among people with Parkinson's (PwP) as they largely happen unwitnessed at home. In this qualitative study, we used an ethnographic approach to investigate which in-home sensors, in which locations, could gather useful data about fall risk. Over six weeks, we observed five independently mobile PwP at high risk of falling, at home. We made field notes about falls (prior events and concerns) and recorded movement with video, Kinect, and wearable sensors. The three women and two men (aged 71 to 79 years) having moderate or severe Parkinson's were dependent on others and highly sedentary. We most commonly noted balance protection, loss, and restoration during chair transfers, walks across open spaces and through gaps, turns, steps up and down, and tasks in standing (all evident walking between chair and stairs, e.g.). Our unobtrusive sensors were acceptable to participants: they could detect instability during everyday activity at home and potentially guide intervention. Monitoring the route between chair and stairs is likely to give information without invading the privacy of people at high risk of falling, with very limited mobility, who spend most of the day in their sitting rooms.

New approaches to chronic disease management within a home or community setting offer patients the prospect of more individually focused care and improved quality of life. This paper investigates the use of a light-weight ear worn activity recognition device combined with wireless ambient sensors for identifying common activities of daily living. A two-stage Bayesian classifier that uses information from both types of sensors is presented. Detailed experimental validation is provided for datasets collected in a laboratory setting as well as in a home environment. Issues concerning the effective use of the relatively limited discriminative power of the ambient sensors are discussed. The proposed framework bodes well for a multi-dwelling environment, and offers a pervasive sensing environment for both patients and care-takers.

No abstract

No abstract