James Pinchin

A particle filter approach to indoor navigation using a foot mounted inertial navigation system and heuristic heading information

Foot mounted inertial navigation is an effective method for obtaining high quality pedestrian navigation solutions from MEMS sensors. Zero-Velocity information from stationary periods in the step-cycle can be used to regularly correct position drift and update estimates of the inertial sensor biases, hence dramatically improving the navigation solution. However the causes of heading error remain poorly observable and so foot mounted inertial navigation suffers from considerable drift over time. To address this problem the authors previously developed Cardinal Heading Aided Inertial Navigation (CHAIN). CHAIN makes use of the fact that when in a building, obstacles such as corridors and furniture constrain pedestrians to move in one of four directions parallel to the outside walls of the building. This knowledge is then appropriately weighted and used in an Extended Kalman Filter to improve error estimation. Although the CHAIN method is very effective at improving the quality of the heading estimates, position errors still accumulate with time, and threshold tests are required to cope with periods of motion away from the cardinal headings. In this work we investigate the use of a building floor plan to further aid navigation. This is achieved using a particle filter approach whereby particles which cross walls are removed and those which navigate in open spaces are allowed to continue. Previously the particle filter approach has been computationally intensive process requiring many particles to effectively model the navigation errors. In our work we recognise that heading is the primary source of navigation error and therefore incorporate heuristic heading information into the particle filter design. By weighting particles according to their heading we reduce the number of particles required to maintain a small failure rate and improve system performance in more open areas where there are few mapped walls to aid navigation. This paper will describe the design of our particle filter and the heuristic heading approach. Results from a number of representative test walks using a MEMS IMU will be used to demonstrate the system performance. The use of CHAIN is shown to be capable of significantly reducing the filter failure rate from 44% to 14% when a small number of particles is used in the filter (250) and the initial position is poorly known.

PRO: confronting resistance to rule-based medicine is essential to improving outcomes

The past 20 years have seen two great changes in the practice of medicine: the widespread adoption of evidence-based medicine, and the increasing challenge of managing complex multimorbid patients. Both these developments have resulted in clinical rules and protocols becoming ever more abundant and increasingly critical to delivering safe and effective patient care. These evidence-based clinical rules perform at least as well as expert opinion, and the increasing volume and quality of available clinical data suggests their performance could continue to improve. This article considers why clinicians deviate from effective rules, highlighting key issues such as the persisting culture of heroism, institutional inertia, deference to authority and personal heuristics. We argue that better rules can be created, and that clinical improvements will follow if there is a ‘common knowledge’ of these rules. Furthermore, we argue that there is a ceiling to the effectiveness of any rule, even one as simple as ensuring hand hygiene, unless individuals are held accountable for transgressions.

Exploring the Relationship between Location and Behaviour in Out of Hours Hospital Care

‘Out of Hours’ (OoH) hospital care involves a small number of doctors covering a very large number of patients. These doctors are working in stressful environments, performing complex tasks and making difficult task prioritisation decisions, yet little data exists to aid in improving the working practices or to ensure junior doctors are adequately prepared for OoH working. Historically, this has been owing to complex and expensive processes to capture this data; however recent advances in indoor positioning technologies has the potential to automate and improve the capture and availability of data that may help alleviate the burden of OoH care on at a personal and hospital level. This paper describes our work to combine cutting edge indoor positioning technologies from OoH working with and a newly deployed in-ward electronic tasking system. Here we describe data collection via traditional methods, clinical tasking systems, and indoor positioning solutions. We further describe our understanding from such data of the effect of physical layout and current working practices on task completion and time spent in transit, which ultimately may inform improvements to working practice within OoH care. Finally we discuss potential relevance to other work domains.

Wi-Fi fingerprinting based on collaborative confidence level training

Wi-Fi fingerprinting has been a popular indoor positioning technique with the advantage that infrastructures are readily available in most urban areas. However wireless signals are prone to fluctuation and noise, introducing errors in the final positioning result. This paper proposes a new fingerprint training method where a number of users train collaboratively and a confidence factor is generated for each fingerprint. Fingerprinting is carried out where potential fingerprints are extracted based on the confidence factor. Positioning accuracy improves by 40% when the new fingerprinting method is implemented and maximum error is reduced by 35%.

Unsupervised labelling of sequential data for location identification in indoor environments

Presents indoor positioning as an unsupervised labelling task on sequential data.Forms a spatial classifier without resorting to pre-determined maps.Differentiates location between unknown closely spaced zones indoors.Presents a valuable working framework for real-world positioning problems.Extends literature studying applications of graphical models. In this paper we present indoor positioning within unknown environments as an unsupervised labelling task on sequential data. We explore a probabilistic framework relying on wireless network radio signals and contextual information, which is increasingly available in large environments. Thus, we form an informative spatial classifier without resorting to a pre-determined map, and show the potential of the approach using both simulated and real data sets.Results demonstrate the ability of the procedure to segregate structures of radio signal observations and form clustered regions in association to areas of interest to the user; thus, we show it is possible to differentiate location between closely spaced zones of variable size and shape.

On the impact of intra-system interference for ranging and positioning with Bluetooth low energy

This paper focuses on the study of intra-system interference for ranging and positioning applications using Bluetooth Low Energy (BLE). While BLE tries to avoid interference with other protocols in the same frequency band, such as Wi-Fi, the intra-system interference is unavoidable, either due to multipath or simultaneous transmissions in the same channel. This study shows that intra-system interference contributes with a deviation of approximately 5 dBm in the Received Signal Strength (RSS) and by taking this into account the ranging and positioning accuracy can be significantly improved. The study uses data collected from two different environments.

Decision-making within missing person search

This paper reports the findings of a series of interviews with search and rescue volunteers. Participants were asked to recall accounts of particular incidents which involved searching for a missing adult who could be considered ‘vulnerable’. The purpose of this study was to discover what types of decisions are made during missing incidents; including a consideration of the factors which affect these decisions and the main focuses of attention throughout the incident. Such an understanding may help to shed light on best practices which could inform decision-making support tools for families of the missing and identify the user-requirements of a future technology designed to help find missing people. Interviews were conducted using the critical decision method (CDM) to elicit specific information about the decisions and challenges faced by search and rescue teams during missing person searches. Critical decision points were identified and sequenced for each incident. Emergent thematic analysis (EMA) was applied to the transcripts to identify themes across various incidents; these themes were explored in detail using a mixed-method approach. This study builds upon the methodological approach of CDM using a two-tiered approach to analysis which seeks to discover the focus of practitioners’ attention as they progress through missing person searches. A decision-sequence diagram was created to clearly show the sequence of each decision and trends across all incidents; a table was produced to show the relative importance of each aspect across decisions. Finally, strengths and weaknesses of this approach to incident analysis are discussed.

An adaptive weighting based on modified DOP for collaborative indoor positioning

Indoor localisation has always been a challenging problem due to poor Global Navigation Satellite System (GNSS) availability in such environments. While inertial measurement sensors have become popular solutions for indoor positioning, they suffer large drifts after initialisation. Collaborative positioning enhances positioning robustness by integrating multiple localisation information, especially relative ranging measurements between local users and transmitters. However, not all ranging measurements are useful throughout the whole positioning process and integrating too much data will increase the computation cost. To enable a more reliable positioning system, an adaptive collaborative positioning algorithm is proposed which selects units for the collaborative network and integrates ranging measurement to constrain inertial measurement errors. The algorithm selects the network adaptively from three perspectives: the network geometry, the network size and the accuracy level of the ranging measurements between the units. The collaborative relative constraint is then defined according to the selected network geometry and anticipated measurement quality. In the case of trials with real data, the positioning accuracy is improved by 60% by adjusting the range constraint adaptively according to the selected network situation, while also improving the system robustness.

Out of hours workload management

OBJECTIVE In this paper, we aim to evaluate the use of electronic technologies in out of hours (OoH) task-management for assisting the design of effective support systems in health care; targeting local facilities, wards or specific working groups. In addition, we seek to draw and validate conclusions with relevance to a frequently revised service, subject to increasing pressures. METHODS AND MATERIAL We have analysed 4 years of digitised demand-data extracted from a recently deployed electronic task-management system, within the Hospital at Night setting in two jointly coordinated hospitals in the United Kingdom. The methodology employed relies on Bayesian inference methods and parameter-driven state-space models for multivariate series of count data. RESULTS Main results support claims relating to (i) the importance of data-driven staffing alternatives and (ii) demand forecasts serving as a basis to intelligent scheduling within working groups. We have displayed a split in workload patterns across groups of medical and surgical specialities, and sustained assertions regarding staff behaviour and work-need changes according to shifts or days of the week. Also, we have provided evidence regarding the relevance of day-to-day planning and prioritisation. CONCLUSIONS The work exhibits potential contributions of electronic tasking alternatives for the purpose of data-driven support systems design; for scheduling, prioritisation and management of care delivery. Electronic tasking technologies provide means to design intelligent systems specific to a ward, speciality or task-type; hence, the paper emphasizes the importance of replacing traditional pager-based approaches to management for modern alternatives.

Investigating the integration of a foot-mounted IMU and GNSS antenna

Low cost MEMS sensors have been shown to provide high accuracy positioning when mounted on a users foot through the use of zero velocity updates (ZUPT) every time the user takes a step. Although position drift is greatly reduced using ZUPTs, position errors will still accumulate over time, particularly due to heading errors which are weakly observable through zero velocity updates. Measurements from GNSS can be used to restrict position drift; however, for good GNSS reception, the GNSS antenna is usually placed on the users back which results in a non-constant separation between the GNSS antenna and IMU. This reduces the effectiveness of GNSS measurements since the lever arm is unknown. Instead, it is much better for IMU integration if the IMU and GNSS antenna are collocated. This is because the GNSS receiver and IMU experience the same dynamics, and the GNSS measurements can be used directly to correct the IMU. This should improve INS error observability, however, the antenna position results in a significantly compromised view of the sky. This paper examines the use of GPS and GLONASS measurements from a foot mounted antenna and explores two different methods for integration with the foot mounted IMU. The first method uses GNSS derived course over ground measurements to reduce INS drift, and the second tightly integrates carrier phase measurements and attempts to resolve ambiguities. It is shown that the combination of a MEMS IMU with GNSS carrier phase measurements can be used to maintain a centimeter level accuracy trajectory even though the sky view is compromised.