In-home monitoring system based on WiFi fingerprints for ambient assisted living

This paper presents an in-home monitoring system based on WiFi fingerprints for Ambient Assisted Living. WiFi fingerprints are used to continuously locate a patient at the different rooms in her/his home. The experiments performed provide a correctly location rate of 96% in the best case of all studied scenarios. The behavior obtained by location monitoring allows to detect anomalous behavior such as long stays in rooms out of the common schedule. The main characteristics of the presented system are: a) it is robust enough to work without an own WiFi access point, which in turn means a very affordable solution; b) low obtrusiveness, as it is based on the use of a mobile phone; c) highly interoperable with other wireless connections (bluetooth, RFID) present in current mobile phones; d) alarms are triggered when any anomalous behavior is detected

Indoor Positioning for Monitoring Older Adults at Home: Wi-Fi and BLE Technologies in Real Scenarios

This paper presents our experience on a real case of applying an indoor localization system formonitoringolderadultsintheirownhomes. Sincethesystemisdesignedtobeusedbyrealusers, therearemanysituationsthatcannotbecontrolledbysystemdevelopersandcanbeasourceoferrors. This paper presents some of the problems that arise when real non-expert users use localization systems and discusses some strategies to deal with such situations. Two technologies were tested to provide indoor localization: Wi-Fi and Bluetooth Low Energy. The results shown in the paper suggest that the Bluetooth Low Energy based one is preferable in the proposed task

An Indoor Positioning System Based on Wearables for Ambient-Assisted Living

The urban population is growing at such a rate that by 2050 it is estimated that 84% of the world’s population will live in cities, with flats being the most common living place. Moreover, WiFi technology is present in most developed country urban areas, with a quick growth in developing countries. New Ambient-Assisted Living applications will be developed in the near future having user positioning as ground technology: elderly tele-care, energy consumption, security and the like are strongly based on indoor positioning information. We present an indoor positioning system for wearable devices based on WiFi fingerprinting. Smart-watch wearable devices are used to acquire the WiFi strength signals of the surrounding Wireless Access Points used to build an ensemble of Machine Learning classification algorithms. Once built, the ensemble algorithm is used to locate a user based on the WiFi strength signals provided by the wearable device. Experimental results for five different urban flats are reported, showing that the system is robust and reliable enough for locating a user at room level into his/her home. Another interesting characteristic of the presented system is that it does not require deployment of any infrastructure, and it is unobtrusive, the only device required for it to work is a smart-watch.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness through the “Proyectos I + D Excelencia” programme (TIN2015-70202-P) and the “Redes de Excelencia” programme (TEC2015-71426-REDT), and from the Regional Government of Valencia (‘Proyectos de I + D para Grupos de Investigación Emergentes’ GV/2016/159). Special thanks to Víctor, Maricarmen, Inma and Daniel who lent their houses for performing the experiments

IoT driven ambient intelligence architecture for indoor intelligent mobility

Personal robots are set to assist humans in their daily tasks. Assisted living is one of the major applications of personal assistive robots, where the robots will support health and wellbeing of the humans in need, especially elderly and disabled. Indoor environments are extremely challenging from a robot perception and navigation point of view, because of the ever-changing decorations, internal organizations and clutter. Furthermore, human-robot-interaction in personal assistive robots demands intuitive and human-like intelligence and interactions. Above challenges are aggravated by stringent and often tacit requirements surrounding personal privacy that may be invaded by continuous monitoring through sensors. Towards addressing the above problems, in this paper we present an architecture for "Ambient Intelligence" for indoor intelligent mobility by leveraging IoTs within a framework of Scalable Multi-layered Context Mapping Framework. Our objective is to utilize sensors in home settings in the least invasive manner for the robot to learn about its dynamic surroundings and interact in a human-like manner. The paper takes a semi-survey approach to presenting and illustrating preliminary results from our in-house built fully autonomous electric quadbike

Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments

A reliable Indoor Positioning System (IPS) is a crucial part of the Ambient-Assisted Living (AAL) concept. The use of Wi-Fi fingerprinting techniques to determine the location of the user, based on the Received Signal Strength Indication (RSSI) mapping, avoids the need to deploy a dedicated positioning infrastructure but comes with its own issues. Heterogeneity of devices and RSSI variability in space and time due to environment changing conditions pose a challenge to positioning systems based on this technique. The primary purpose of this research is to examine the viability of leveraging other sensors in aiding the positioning system to provide more accurate predictions. In particular, the experiments presented in this work show that Inertial Motion Units (IMU), which are present by default in smart devices such as smartphones or smartwatches, can increase the performance of Indoor Positioning Systems in AAL environments. Furthermore, this paper assesses a set of techniques to predict the future performance of the positioning system based on the training data, as well as complementary strategies such as data scaling and the use of consecutive Wi-Fi scanning to further improve the reliability of the IPS predictions. This research shows that a robust positioning estimation can be derived from such strategies

Requirements and metrics for location and tracking for ambient assisted living

Location and tracking services and technologies are becoming fundamental components for supporting healthcare solutions. They facilitate patients’ tracking and monitoring processes and also allow for better and long-term daily activity recognition. Various location and tracking services have been developed, over the last years, to provide real time localization for different applications. However, most of these services are not designed particularly to comply with all the requirements of Ambient Assisted Living (AAL) and, as a result, they reduce the viability of adopting AAL services as an alternative for continuous healthcare services. In this paper we set out the general requirements for location and tracking services for AAL. The requirements are extracted from a typical scenario of AAL. From the scenario, we define the requirements and also we identify a set of metrics to be used as evaluation criteria. If the identified requirements and metrics are adopted widely, potential location and tracking services will fit the real needs of AAL, and thus will increase the accessibility to AAL services by a larger sector of people. Moreover, in the paper, we evaluate two of the existing location techniques through the use of the proposed metrics. The aim is to asses to which level these solutions fulfill the identified requirements.This work was supported by the FEDER program through the COMPETE and the Portuguese Science and Technology Foundation (FCT), within the context of the AAL4ALL (COMPETE 13852) and FCOMP-01-FEDER-0124-022674 projects

Low-Cost Indoor Localisation Based on Inertial Sensors, Wi-Fi and Sound

The average life expectancy has been increasing in the last decades, creating the need for new technologies to improve the quality of life of the elderly. In the Ambient Assisted Living scope, indoor location systems emerged as a promising technology capable of sup porting the elderly, providing them a safer environment to live in, and promoting their autonomy. Current indoor location technologies are divided into two categories, depend ing on their need for additional infrastructure. Infrastructure-based solutions require expensive deployment and maintenance. On the other hand, most infrastructure-free systems rely on a single source of information, being highly dependent on its availability. Such systems will hardly be deployed in real-life scenarios, as they cannot handle the absence of their source of information. An efficient solution must, thus, guarantee the continuous indoor positioning of the elderly. This work proposes a new room-level low-cost indoor location algorithm. It relies on three information sources: inertial sensors, to reconstruct users’ trajectories; environ mental sound, to exploit the unique characteristics of each home division; and Wi-Fi, to estimate the distance to the Access Point in the neighbourhood. Two data collection protocols were designed to resemble a real living scenario, and a data processing stage was applied to the collected data. Then, each source was used to train individual Ma chine Learning (including Deep Learning) algorithms to identify room-level positions. As each source provides different information to the classification, the data were merged to produce a more robust localization. Three data fusion approaches (input-level, early, and late fusion) were implemented for this goal, providing a final output containing complementary contributions from all data sources. Experimental results show that the performance improved when more than one source was used, attaining a weighted F1-score of 81.8% in the localization between seven home divisions. In conclusion, the evaluation of the developed algorithm shows that it can achieve accurate room-level indoor localization, being, thus, suitable to be applied in Ambient Assisted Living scenarios.O aumento da esperança média de vida nas últimas décadas, criou a necessidade de desenvolvimento de tecnologias que permitam melhorar a qualidade de vida dos idosos. No âmbito da Assistência à Autonomia no Domicílio, sistemas de localização indoor têm emergido como uma tecnologia promissora capaz de acompanhar os idosos e as suas atividades, proporcionando-lhes um ambiente seguro e promovendo a sua autonomia. As tecnologias de localização indoor atuais podem ser divididas em duas categorias, aquelas que necessitam de infrastruturas adicionais e aquelas que não. Sistemas dependentes de infrastrutura necessitam de implementação e manutenção que são muitas vezes dispendiosas. Por outro lado, a maioria das soluções que não requerem infrastrutura, dependem de apenas uma fonte de informação, sendo crucial a sua disponibilidade. Um sistema que não consegue lidar com a falta de informação de um sensor dificilmente será implementado em cenários reais. Uma solução eficiente deverá assim garantir o acompanhamento contínuo dos idosos. A solução proposta consiste no desenvolvimento de um algoritmo de localização indoor de baixo custo, baseando-se nas seguintes fontes de informação: sensores inerciais, capazes de reconstruir a trajetória do utilizador; som, explorando as características dis tintas de cada divisão da casa; e Wi-Fi, responsável pela estimativa da distância entre o ponto de acesso e o smartphone. Cada fonte sensorial, extraída dos sensores incorpora dos no dispositivo, foi, numa primeira abordagem, individualmente otimizada através de algoritmos de Machine Learning (incluindo Deep Learning). Como os dados das diversas fontes contêm informação diferente acerca das mesmas características do sistema, a sua fusão torna a classificação mais informada e robusta. Com este objetivo, foram implementadas três abordagens de fusão de dados (input data, early and late fusion), fornecendo um resultado final derivado de contribuições complementares de todas as fontes de dados. Os resultados experimentais mostram que o desempenho do algoritmo desenvolvido melhorou com a inclusão de informação multi-sensor, alcançando um valor para F1- score de 81.8% na distinção entre sete divisões domésticas. Concluindo, o algoritmo de localização indoor, combinando informações de três fontes diferentes através de métodos de fusão de dados, alcançou uma localização room-level e está apto para ser aplicado num cenário de Assistência à Autonomia no Domicílio

Wi-Fi For Indoor Device Free Passive Localization (DfPL): An Overview

The world is moving towards an interconnected and intercommunicable network of animate and inanimate objects with the emergence of Internet of Things (IoT) concept which is expected to have 50 billion connected devices by 2020. The wireless communication enabled devices play a major role in the realization of IoT. In Malaysia, home and business Internet Service Providers (ISP) bundle Wi-Fi modems working in 2.4 GHz Industrial, Scientific and Medical (ISM) radio band with their internet services. This makes Wi-Fi the most eligible protocol to serve as a local as well as internet data link for the IoT devices. Besides serving as a data link, human entity presence and location information in a multipath rich indoor environment can be harvested by monitoring and processing the changes in the Wi-Fi Radio Frequency (RF) signals. This paper comprehensively discusses the initiation and evolution of Wi-Fi based Indoor Device free Passive Localization (DfPL) since the concept was first introduced by Youssef et al. in 2007. Alongside the overview, future directions of DfPL in line with ongoing evolution of Wi-Fi based IoT devices are briefly discussed in this paper