A realistic evaluation of indoor positioning systems based on Wi-Fi fingerprinting: The 2015 EvAAL–ETRI competition

Pre-print versionThis paper presents results from comparing different Wi-Fi fingerprinting algorithms on the same private dataset. The algorithms where realized by independent teams in the frame of the off-site track of the EvAAL-ETRI Indoor Localization Competition which was part of the Sixth International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015). Competitors designed and validated their algorithms against the publicly available UJIIndoorLoc database which contains a huge reference- and validation data set. All competing systems were evaluated using the mean error in positioning, with penalties, using a private test dataset. The authors believe that this is the first work in which Wi-Fi fingerprinting algorithm results delivered by several independent and competing teams are fairly compared under the same evaluation conditions. The analysis also comprises a combined approach: Results indicate that the competing systems where complementary, since an ensemble that combines three competing methods reported the overall best results.We would like to thank Francesco Potortì, Paolo Barsocchi, Michele Girolami and Kyle O’Keefe for their valuable help in organizing and spread the EVAALETRI competition and the off-site track. We would also like to thank the TPC members Machaj Juraj, Christos Laoudias, Antoni Pérez-Navarro and Robert Piché for their valuable comments, suggestions and reviews. Parts of this work were funded in the frame of the Spanish Ministry of Economy and Competitiveness through the “Metodologiías avanzadas para el diseño, desarrollo, evaluación e integración de algoritmos de localización en interiores” project (Proyectos I+D Excelencia, código TIN2015-70202-P) and the “Red de Posicionamiento y Navegación en Interiores” network (Redes de Excelencia, código TEC2015-71426- REDT). Parts of this work were funded in the frame of the German federal Ministry of Education and Research programme "FHprofUnt2013" under contract 03FH035PB3 (Project SPIRIT).info:eu-repo/semantics/acceptedVersio

Using smartphone accelerometry to assess the relationship between cognitive load and gait dynamics during outdoor walking

Abstract Research has demonstrated that an increase in cognitive load can result in increased gait variability and slower overall walking speed, both of which are indicators of gait instability. The external environment also imposes load on our cognitive systems; however, most gait research has been conducted in a laboratory setting and little work has demonstrated how load imposed by natural environments impact gait dynamics during outdoor walking. Across four experiments, young adults were exposed to varying levels of cognitive load while walking through indoor and outdoor environments. Gait dynamics were concurrently recorded using smartphone-based accelerometry. Results suggest that, during indoor walking, increased cognitive load impacted a range of gait parameters such as step time and step time variability. The impact of environmental load on gait, however, was not as pronounced, with increased load associated only with step time changes during outdoor walking. Overall, the present work shows that cognitive load is related to young adult gait during both indoor and outdoor walking, and importantly, smartphones can be used as gait assessment tools in environments where gait dynamics have traditionally been difficult to measure

Technologies that assess the location of physical activity and sedentary behavior: a systematic review

Background: The location in which physical activity and sedentary behavior are performed can provide valuable behavioral information, both in isolation and synergistically with other areas of physical activity and sedentary behavior research. Global positioning systems (GPS) have been used in physical activity research to identify outdoor location; however, while GPS can receive signals in certain indoor environments, it is not able to provide room- or subroom-level location. On average, adults spend a high proportion of their time indoors. A measure of indoor location would, therefore, provide valuable behavioral information. Objective: This systematic review sought to identify and critique technology which has been or could be used to assess the location of physical activity and sedentary behavior. Methods: To identify published research papers, four electronic databases were searched using key terms built around behavior, technology, and location. To be eligible for inclusion, papers were required to be published in English and describe a wearable or portable technology or device capable of measuring location. Searches were performed up to February 4, 2015. This was supplemented by backward and forward reference searching. In an attempt to include novel devices which may not yet have made their way into the published research, searches were also performed using three Internet search engines. Specialized software was used to download search results and thus mitigate the potential pitfalls of changing search algorithms. Results: A total of 188 research papers met the inclusion criteria. Global positioning systems were the most widely used location technology in the published research, followed by wearable cameras, and radio-frequency identification. Internet search engines identified 81 global positioning systems, 35 real-time locating systems, and 21 wearable cameras. Real-time locating systems determine the indoor location of a wearable tag via the known location of reference nodes. Although the type of reference node and location determination method varies between manufacturers, Wi-Fi appears to be the most popular method. Conclusions: The addition of location information to existing measures of physical activity and sedentary behavior will provide important behavioral information

An Automatic Gait Feature Extraction Method for Identifying Gait Asymmetry Using Wearable Sensors

This paper aims to assess the use of Inertial Measurement Unit (IMU) sensors to identify gait asymmetry by extracting automatic gait features. We design and develop an android app to collect real time synchronous IMU data from legs. The results from our method are validated using a Qualisys Motion Capture System. The data are collected from 10 young and 10 older subjects. Each performed a trial in a straight corridor comprising 15 strides of normal walking, a turn around and another 15 strides. We analyse the data for total distance, total time, total velocity, stride, step, cadence, step ratio, stance, and swing. The accuracy of detecting the stride number using the proposed method is 100% for young and 92.67% for older subjects. The accuracy of estimating travelled distance using the proposed method for young subjects is 97.73% and 98.82% for right and left legs; and for the older, is 88.71% and 89.88% for right and left legs. The average travelled distance is 37.77 (95% CI ± 3.57) meters for young subjects and is 22.50 (95% CI ± 2.34) meters for older subjects. The average travelled time for young subjects is 51.85 (95% CI ± 3.08) seconds and for older subjects is 84.02 (95% CI ± 9.98) seconds. The results show that wearable sensors can be used for identifying gait asymmetry without the requirement and expense of an elaborate laboratory setup. This can serve as a tool in diagnosing gait abnormalities in individuals and opens the possibilities for home based self-gait asymmetry assessment

Mapa de la recerca del Campus de Vilanova i la Geltrú

Postprint (author’s final draft

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research

Uma metodologia de localização Indoor híbrida para sistemas móveis

In recent years technological development has allowed the emergence of several applications and location services, with emphasis on localization solutions in indoor environments (tracking of objects, assets and people, precision marketing, security and others). It is known that traditional localization solutions, such as the Global Positioning System (GPS) do not present satisfactory results for indoor scenarios and that other solutions already exist in the literature. On this work, a new solution is provided to the indoor location problem based on multiplicity of communication sources and protocols, trilateration method, motion tracking, inertial sensors and algorithms for mitigation of signal propagation misrepresentations with the intention increase the accuracy of the solution. Results indicated the feasibility of the proposal with average errors of 1,224 meters in typical scenarios of densely populated buildings.Nos últimos anos o desenvolvimento tecnológico tem permitido o surgimento de diversas aplicações e serviços de localização, com destaque para as soluções de localização em ambientes internos - indoor (rastreamento de objetos, ativos e pessoas, marketing de precisão, segurança, entre outros). É sabido que soluções tradicionais de localização, como por exemplo o Sistema Global de posicionamento (Global Positioning System - GPS) não apresentam resultados satisfatórios para cenários indoor e que outras soluções já existem na literatura. Nesse trabalho é fornecido uma nova solução para o problema de localização indoor baseado em multiplicidade de fontes e protocolos de comunicação, método de trilateração, rastreio de movimentação, sensores inerciais e algoritmos para mitigação de deturpações de propagação de sinais com o intuito de aumentar a precisão da solução. Resultados indicaram a factibilidade da proposta com erros médios de 1,224 metros em cenários típicos de prédios densamente populados

On Calibrating the Sensor Errors of a PDR-Based Indoor Localization System

Many studies utilize the signal strength of short-range radio systems (such as WiFi, ultrasound and infrared) to build a radio map for indoor localization, by deploying a large number of beacon nodes within a building. The drawback of such an infrastructure-based approach is that the deployment and calibration of the system are costly and labor-intensive. Some prior studies proposed the use of Pedestrian Dead Reckoning (PDR) for indoor localization, which does not require the deployment of beacon nodes. In a PDR system, a small number of sensors are put on the pedestrian. These sensors (such as a G-sensor and gyroscope) are used to estimate the distance and direction that a user travels. The effectiveness of a PDR system lies in its success in accurately estimating the user’s moving distance and direction. In this work, we propose a novel waist-mounted based PDR that can measure the user’s step lengths with a high accuracy. We utilize vertical acceleration of the body to calculate the user’s change in height during walking. Based on the Pythagorean Theorem, we can then estimate each step length using this data. Furthermore, we design a map matching algorithm to calibrate the direction errors from the gyro using building floor plans. The results of our experiment show that we can achieve about 98.26% accuracy in estimating the user’s walking distance, with an overall location error of about 0.48 m

Advancing the objective measurement of physical activity and sedentary behaviour context

Objective data from national surveillance programmes show that, on average, individuals accumulate high amounts of sedentary time per day and only a small minority of adults achieve physical activity guidelines. One potential explanation for the failure of interventions to increase population levels of physical activity or decrease sedentary time is that research to date has been unable to identify the specific behavioural levers in specific contexts needed to change behaviour. Novel technology is emerging with the potential to elucidate these specific behavioural contexts and thus identify these specific behavioural levers. Therefore the aims of this four study thesis were to identify novel technologies capable of measuring the behavioural context, to evaluate and validate the most promising technology and to then pilot this technology to assess the behavioural context of older adults, shown by surveillance programmes to be the least physically active and most sedentary age group. Study one Purpose: To identify, via a systematic review, technologies which have been used or could be used to measure the location of physical activity or sedentary behaviour. Methods: Four electronic databases were searched using key terms built around behaviour, technology and location. To be eligible for inclusion papers were required to be published in English and describe a wearable or portable technology or device capable of measuring location. Searches were performed from the inception of the database up to 04/02/2015. Searches were also performed using three internet search engines. Specialised software was used to download search results and thus mitigate the potential pitfalls of changing search algorithms. Results: 188 research papers met the inclusion criteria. Global positioning systems were the most widely used location technology in the published research, followed by wearable cameras and Radio-frequency identification. Internet search engines identified 81 global positioning systems, 35 real-time locating systems and 21 wearable cameras. Conclusion: The addition of location information to existing measures of physical activity and sedentary behaviour will provide important behavioural information. Study Two Purpose: This study investigated the Actigraph proximity feature across three experiments. The aim of Experiment One was to assess the basic characteristics of the Actigraph RSSI signal across a range of straight line distances. Experiment Two aimed to assess the level of receiver device signal detection in a single room under unobstructed conditions, when various obstructions are introduced and the impacts these obstructions have on the intra and inter unit variability of the RSSI signal. Finally, Experiment Three aimed to assess signal contamination across multiple rooms (i.e. one beacon being detected in multiple rooms). Methods: Across all experiments, the receiver(s) collected data at 10 second epochs, the highest resolution possible. In Experiment One two devices, one receiver and one beacon, were placed opposite each other at 10cm increments for one minute at each distance. The RSSI-distance relationship was then visually assessed for linearity. In Experiment Two, a test room was demarcated into 0.5 x 0.5 m grids with receivers simultaneously placed in each demarcated grid. This process was then repeated under wood, metal and human obstruction conditions. Descriptive tallies were used to assess the signal detection achieved for each receiver from each beacon in each grid. Mean RSSI signal was calculated for each condition alongside intra and inter-unit standard deviation, coefficient of variation and standard error of the measurement. In Experiment Three, a test apartment was used with three beacons placed across two rooms. The researcher then completed simulated conditions for 10 minutes each across the two rooms. The percentage of epochs where a signal was detected from each of the three beacons across each test condition was then calculated. Results: In Experiment One, the relationship between RSSI and distance was found to be non-linear. In Experiment Two, high signal detection was achieved in all conditions; however, there was a large degree of intra and inter-unit variability in RSSI. In Experiment Three, there was a large degree of multi-room signal contamination. Conclusion: The Actigraph proximity feature can provide a binary indicator of room level location. Study Three Purpose: To use novel technology in three small feasibility trials to ascertain where the greatest utility can be demonstrated. Methods: Feasibility Trial One assessed the concurrent validity of electrical energy monitoring and wearable cameras as measures of television viewing. Feasibility Trial Two utilised indoor location monitoring to assess where older adult care home residents accumulate their sedentary time. Lastly, Feasibility Trial Three investigated the use of proximity sensors to quantify exposure to a height adjustable desk Results: Feasibility Trial One found that on average the television is switched on for 202 minutes per day but is visible in just 90 minutes of wearable camera images with a further 52 minutes where the participant is in their living room but the television is not visible in the image. Feasibility Trial Two found that residents were highly sedentary (sitting for an average of 720 minutes per day) and spent the majority of their time in their own rooms with more time spent in communal areas in the morning than in the afternoon. Feasibility Trial Three found a discrepancy between self-reported work hours and objectively measured office dwell time. Conclusion: The feasibility trials outlined in this study show the utility of objectively measuring context to provide more detailed and refined data. Study Four Purpose: To objectively measure the context of sedentary behaviour in the most sedentary age group, older adults. Methods: 26 residents and 13 staff were recruited from two care homes. Each participant wore an Actigraph GT9X on their non-dominant wrist and a LumoBack posture sensor on their lower back for one week. The Actigraph recorded proximity every 10 seconds and acceleration at 100 Hz. LumoBack data were provided as summaries per 5 minutes. Beacon Actigraphs were placed around each care home in the resident s rooms, communal areas and corridors. Proximity and posture data were combined in 5 minute epochs with descriptive analysis of average time spent sitting in each area produced. Acceleration data were summarised into 10 second epochs and combined with proximity data to show the average count per epoch in each area of the care home. Mann-Whitney tests were performed to test for differences between care homes. Results: No significant differences were found between Care Home One and Care Home Two in the amount of time spent sitting in communal areas of the care home (301 minutes per day and 39 minutes per day respectively, U=23, p=0.057) or in the amount of time residents spent sitting in their own room (215 minutes per day and 337 minutes per day in Care Home One and Two respectively, U=32, p=0.238). In both care homes, accelerometer measured average movement increases with the number of residents in the communal area. Conclusion: The Actigraph proximity system was able to quantify the context of sedentary behaviour in older adults. This enabled the identification of levers for behaviour change which can be used to reduce sedentary time in this group. Overall conclusion: There are a large number of technologies available with the potential to measure the context of physical activity or sedentary time. The Actigraph proximity feature is one such technology. This technology is able to provide a binary measure of proximity via the detection or non-detection of Bluetooth signal: however, the variability of the signal prohibits distance estimation. The Actigraph proximity feature, in combination with a posture sensor, is able to elucidate the context of physical activity and sedentary time

An automatic wearable multi-sensor based gait analysis system for older adults.

Gait abnormalities in older adults are very common in clinical practice. They lead to serious adverse consequences such as falls and injury resulting in increased care cost. There is therefore a national imperative to address this challenge. Currently gait assessment is done using standardized clinical tools dependent on subjective evaluation. More objective gold standard methods (motion capture systems such as Qualisys and Vicon) to analyse gait rely on access to expensive complex equipment based in gait laboratories. These are not widely available for several reasons including a scarcity of equipment, need for technical staff, need for patients to attend in person, complicated time consuming procedures and overall expense. To broaden the use of accurate quantitative gait monitoring and assessment, the major goal of this thesis is to develop an affordable automatic gait analysis system that will provide comprehensive gait information and allow use in clinic or at home. It will also be able to quantify and visualize gait parameters, identify gait variables and changes, monitor abnormal gait patterns of older people in order to reduce the potential for falling and support falls risk management. A research program based on conducting experiments on volunteers is developed in collaboration with other researchers in Bournemouth University, The Royal Bournemouth Hospital and care homes. This thesis consists of five different studies toward addressing our major goal. Firstly, a study on the effects on sensor output from an Inertial Measurement Unit (IMU) attached to different anatomical foot locations. Placing an IMU over the bony prominence of the first cuboid bone is the best place as it delivers the most accurate data. Secondly, an automatic gait feature extraction method for analysing spatiotemporal gait features which shows that it is possible to extract gait features automatically outside of a gait laboratory. Thirdly, user friendly and easy to interpret visualization approaches are proposed to demonstrate real time spatiotemporal gait information. Four proposed approaches have the potential of helping professionals detect and interpret gait asymmetry. Fourthly, a validation study of spatiotemporal IMU extracted features compared with gold standard Motion Capture System and Treadmill measurements in young and older adults is conducted. The results obtained from three experimental conditions demonstrate that our IMU gait extracted features are highly valid for spatiotemporal gait variables in young and older adults. In the last study, an evaluation system using Procrustes and Euclidean distance matrix analysis is proposed to provide a comprehensive interpretation of shape and form differences between individual gaits. The results show that older gaits are distinguishable from young gaits. A pictorial and numerical system is proposed which indicates whether the assessed gait is normal or abnormal depending on their total feature values. This offers several advantages: 1) it is user friendly and is easy to set up and implement; 2) it does not require complex equipment with segmentation of body parts; 3) it is relatively inexpensive and therefore increases its affordability decreasing health inequality; and 4) its versatility increases its usability at home supporting inclusivity of patients who are home bound. A digital transformation strategy framework is proposed where stakeholders such as patients, health care professionals and industry partners can collaborate through development of new technologies, value creation, structural change, affordability and sustainability to improve the diagnosis and treatment of gait abnormalities