Inertial sensors integrated with clothing to localize people inside buildings

This article presents a wearable system that localizes people in the indoor environment, using data from inertial sensors. The sensors measure the parameters of human motion, tracking the movements of the torso and foot. For this purpose, they were integrated with shirt and the shoe insole. The values of acceleration measured by the sensors are sent via Bluetooth to a smartphone. The localization algorithm implemented on the smartphone, presented here, merges data from the shirt and the shoe to track the steps made by the user and filter out the artefacts caused by movements the shirt and torso. The experimental verification of the algorithm is also presented

Adaptive Geolocation of IoT devices for Active and Assisted Living

Recent developments in IoT devices and communication systems, have brought to light new solutions capable of offering advanced sensing of the surrounding environments. On the other hand, during the last decades, the average life expectancy has increased, which translates into a considerable rise in the number of elderly people. Consequently, in view of all these factors, there is currently a constant demand for solutions to support an Active and Assisted Living (AAL) of such people. The presented thesis intends to propose a solution to help to know the location of IoT devices that may be assisting people. The proposed solution should take into consideration the risk factors of the target group at each moment, as well as the technical constraints of the device, such as its available power energy and means of communications. Thus, ultimately, a profile-based decision should autonomously be made by the device or its integrated system, in order to ensure the usage of the best geolocation technology for each situation.Desenvolvimentos recentes em dispositivos IoT e em sistemas de comunicação, trouxeram consigo novas soluções capazes de oferecer uma deteção avançada dos ambientes circundantes. Por outro lado, no decorrer das últimas décadas, a esperança média de vida aumentou, o que se traduz também num considerável aumento do número de pessoas idosas. Por conseguinte, perante o conjunto destes factores, existe actualmente uma procura constante de soluções de suporte a uma Active and Assisted Living desse grupo de pessoas. A presente tese tenciona propor uma solução que ajude a conhecer a localização dos dispositivos IoT que possam estar a ajudar pessoas. A solução proposta deve ter em consideração os fatores de risco do grupo-alvo em cada momento e também as restrições técnicas do dispositivo, como a energia disponível e os meios de comunicação. Deste modo, em última instância, uma decisão baseada num perfil deve ser tomada autonomamente pelo dispositivo ou pelo seu sistema, para garantir a utilização da tecnologia de geolocalização mais adequada em cada situação

A Wearable Fall Detection System based on LoRa LPWAN Technology

Several technological solutions now available in the market offer the possibility of increasing the independent life of people who by age or pathologies otherwise need assistance. In particular, internet-connected wearable solutions are of considerable interest, as they allow continuous monitoring of the user. However, their use poses different challenges, from the real usability of a device that must still be worn to the performance achievable in terms of radio connectivity and battery life. The acceptability of a technology solution, by a user who would still benefit from its use, is in fact often conditioned by practical problems that impact the person’s normal lifestyle. The technological choices adopted in fact strongly determine the success of the proposed solution, as they may imply limitations both to the person who uses it and to the achievable performance. In this document, targeting the case of a fall detection sensor based on a pair of sensorized shoes, the effectiveness of a real implementation of an Internet of Things technology is examined. It is shown how alarming events, generated in a metropolitan context, are effectively sent to a supervision system through Low Power Wide Area Network technology without the need for a portable gateway. The experimental results demonstrate the effectiveness of the chosen technology, which allows the user to take advantage of the support of a wearable sensor without being forced to substantially change his lifestyle

Electronic design for a fully wireless smart insole

Aquesta tesi representa el disseny d'una plantilla dissenyada per detectar caigudes i monitoritzar i caracteritzar els passos al caminar amb l'objectiu de minimitzar el risc de caigudes, especialment en gent gran. La plantilla és prou prima com per ser comfortable per a l'usuari i no te cap contacte elèctric. El prototip incorpora tecnologies avançades com Bluetooth 5, plaques de circuit imprès flexi-rígides, sensors MEMS i tèxtils, càrrega per inducció i interruptors magnètics. Aquestes característiques permeten que la plantilla funcioni en una àmplia varietat d'entorns sense el risc d'un mal funcionament a causa de la humitat o la suor

On Analyzing User Location Discovery Methods in Smart Homes: A Taxonomy and Survey

User Location Discovery (ULD) is a key issue in smart home ecosystems, as it plays a critical role in many applications. If a smart home management system cannot detect the actual location of the users, the desired applications may not be able to work successfully. This article proposes a new taxonomy with a broad coverage of ULD methods in terms of user satisfaction and technical features. In addition, we provide a state-of-the-art survey of ULD methods and apply our taxonomy to map these methods. Mapping contributes to gap analysis for existing ULDs and also validates the applicability and accuracy of the taxonomy. Using this systematic approach, the features and characteristics of the current ULD methods are identified (i.e., equipment and algorithms). Next, the weaknesses and advantages of these methods are analyzed utilizing ten important evaluation metrics. Although we mainly focus on smart homes, the results of this article can be generalized to other spaces such as smart offices and eHealth environments

SHELDON Smart habitat for the elderly.

An insightful document concerning active and assisted living under different perspectives: Furniture and habitat, ICT solutions and Healthcare

Comparison of the Depth Accuracy of a Plenoptic Camera and a Stereo Camera System in Spatially Tracking Single Refuse-derived Fuel Particles in a Drop Shaft

With the development of depth cameras in the last decades, several cameras are able to acquire 3D information of the captured scenes, such as plenoptic camera and stereo camera system. Because of the differences in principle and construction of various depth cameras, different cameras own particular advantages and disadvantages. Therefore, a comprehensive and detailed comparison of different cameras is essential to select the right camera for the application. Our research compared the depth accuracy and stability of a stereo camera system and a plenoptic camera by monitoring the settling processes of various refuse-derived fuel particles in a drop shaft. The particles are detected at first using detection approaches, and the particle detections are subsequently associated in accordance with data association algorithms. The spatial particle trajectories are obtained by the tracking-by-detection approach, based on which the performances of the cameras are evaluated

Foot Motion-Based Falling Risk Evaluation for Patients with Parkinson’s Disease

Parkinson’s disease (PD) affects motor functionalities, which are closely associated with increased risks of falling and decreased quality of life. However, there is no easy-to-use definitive tools for PD patients to quantify their falling risks at home. To address this, in this dissertation, we develop Monitoring Insoles (MONI) with advanced data processing techniques to score falling risks of PD patients following Falling Risk Questionnaire (FRQ) developed by the U.S. Centers for Disease Control and Prevention (CDC). To achieve this, we extract motion tasks from daily activities and select the most representative features associated with PD that facilitate accurate falling risk scoring. To address the challenge in uncontrolled daily life environments and to identify the most representative features associated with PD and falling risks, the proposed data processing method firstly recognizes foot motions such as walking and toe tapping from continuous movements with stride detection and fast labeling framework, and then extracts time-axis and acceleration-axis features from the motion tasks, at the end provides a score of falling risks using regression. The data processing method can be integrated into a mobile game to be used at home with MONI. The main contributions of this dissertation includes: (i) developing MONI as a low power solution for daily life use; (ii) utilizing stride detection and developing fast labeling framework for motion recognition that improves recognition accuracy for daily life applications; (iii) analyzing two walking and two toe tapping tasks that are close to real life scenarios and identifying important features associated with PD and falling risks; (iv) providing falling scores as quantitative evaluation to PD patients in daily life through simple foot motion tasks and setups