PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology

With the advancement of deep neural networks and computer vision-based Human Activity Recognition, employment of Point-Cloud Data technologies (LiDAR, mmWave) has seen a lot interests due to its privacy preserving nature. Given the high promise of accurate PCD technologies, we develop, PALMAR, a multiple-inhabitant activity recognition system by employing efficient signal processing and novel machine learning techniques to track individual person towards developing an adaptive multi-inhabitant tracking and HAR system. More specifically, we propose (i) a voxelized feature representation-based real-time PCD fine-tuning method, (ii) efficient clustering (DBSCAN and BIRCH), Adaptive Order Hidden Markov Model based multi-person tracking and crossover ambiguity reduction techniques and (iii) novel adaptive deep learning-based domain adaptation technique to improve the accuracy of HAR in presence of data scarcity and diversity (device, location and population diversity). We experimentally evaluate our framework and systems using (i) a real-time PCD collected by three devices (3D LiDAR and 79 GHz mmWave) from 6 participants, (ii) one publicly available 3D LiDAR activity data (28 participants) and (iii) an embedded hardware prototype system which provided promising HAR performances in multi-inhabitants (96%) scenario with a 63% improvement of multi-person tracking than state-of-art framework without losing significant system performances in the edge computing device.Comment: Accepted in IEEE International Conference on Computer Communications 202

Development of a simulation tool for measurements and analysis of simulated and real data to identify ADLs and behavioral trends through statistics techniques and ML algorithms

openCon una popolazione di anziani in crescita, il numero di soggetti a rischio di patologia è in rapido aumento. Molti gruppi di ricerca stanno studiando soluzioni pervasive per monitorare continuamente e discretamente i soggetti fragili nelle loro case, riducendo i costi sanitari e supportando la diagnosi medica. Comportamenti anomali durante l'esecuzione di attività di vita quotidiana (ADL) o variazioni sulle tendenze comportamentali sono di grande importanza.With a growing population of elderly people, the number of subjects at risk of pathology is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Anomalous behaviors while performing activities of daily living (ADLs) or variations on behavioral trends are of great importance. To measure ADLs a significant number of parameters need to be considering affecting the measurement such as sensors and environment characteristics or sensors disposition. To face the impossibility to study in the real context the best configuration of sensors able to minimize costs and maximize accuracy, simulation tools are being developed as powerful means. This thesis presents several contributions on this topic. In the following research work, a study of a measurement chain aimed to measure ADLs and represented by PIRs sensors and ML algorithm is conducted and a simulation tool in form of Web Application has been developed to generate datasets and to simulate how the measurement chain reacts varying the configuration of the sensors. Starting from eWare project results, the simulation tool has been thought to provide support for technicians, developers and installers being able to speed up analysis and monitoring times, to allow rapid identification of changes in behavioral trends, to guarantee system performance monitoring and to study the best configuration of the sensors network for a given environment. The UNIVPM Home Care Web App offers the chance to create ad hoc datasets related to ADLs and to conduct analysis thanks to statistical algorithms applied on data. To measure ADLs, machine learning algorithms have been implemented in the tool. Five different tasks have been identified. To test the validity of the developed instrument six case studies divided into two categories have been considered. To the first category belong those studies related to: 1) discover the best configuration of the sensors keeping environmental characteristics and user behavior as constants; 2) define the most performant ML algorithms. The second category aims to proof the stability of the algorithm implemented and its collapse condition by varying user habits. Noise perturbation on data has been applied to all case studies. Results show the validity of the generated datasets. By maximizing the sensors network is it possible to minimize the ML error to 0.8%. Due to cost is a key factor in this scenario, the fourth case studied considered has shown that minimizing the configuration of the sensors it is possible to reduce drastically the cost with a more than reasonable value for the ML error around 11.8%. Results in ADLs measurement can be considered more than satisfactory.INGEGNERIA INDUSTRIALEopenPirozzi, Michel

A vision-based system for intelligent monitoring: human behaviour analysis and privacy by context

Due to progress and demographic change, society is facing a crucial challenge related to increased life expectancy and a higher number of people in situations of dependency. As a consequence, there exists a significant demand for support systems for personal autonomy. This article outlines the vision@home project, whose goal is to extend independent living at home for elderly and impaired people, providing care and safety services by means of vision-based monitoring. Different kinds of ambient-assisted living services are supported, from the detection of home accidents, to telecare services. In this contribution, the specification of the system is presented, and novel contributions are made regarding human behaviour analysis and privacy protection. By means of a multi-view setup of cameras, people's behaviour is recognised based on human action recognition. For this purpose, a weighted feature fusion scheme is proposed to learn from multiple views. In order to protect the right to privacy of the inhabitants when a remote connection occurs, a privacy-by-context method is proposed. The experimental results of the behaviour recognition method show an outstanding performance, as well as support for multi-view scenarios and real-time execution, which are required in order to provide the proposed services

Usable privacy and security in smart homes

Ubiquitous computing devices increasingly dominate our everyday lives, including our most private places: our homes. Homes that are equipped with interconnected, context-aware computing devices, are considered “smart” homes. To provide their functionality and features, these devices are typically equipped with sensors and, thus, are capable of collecting, storing, and processing sensitive user data, such as presence in the home. At the same time, these devices are prone to novel threats, making our homes vulnerable by opening them for attackers from outside, but also from within the home. For instance, remote attackers who digitally gain access to presence data can plan for physical burglary. Attackers who are physically present with access to devices could access associated (sensitive) user data and exploit it for further cyberattacks. As such, users’ privacy and security are at risk in their homes. Even worse, many users are unaware of this and/or have limited means to take action. This raises the need to think about usable mechanisms that can support users in protecting their smart home setups. The design of such mechanisms, however, is challenging due to the variety and heterogeneity of devices available on the consumer market and the complex interplay of user roles within this context. This thesis contributes to usable privacy and security research in the context of smart homes by a) understanding users’ privacy perceptions and requirements for usable mechanisms and b) investigating concepts and prototypes for privacy and security mechanisms. Hereby, the focus is on two specific target groups, that are inhabitants and guests of smart homes. In particular, this thesis targets their awareness of potential privacy and security risks, enables them to take control over their personal privacy and security, and illustrates considerations for usable authentication mechanisms. This thesis provides valuable insights to help researchers and practitioners in designing and evaluating privacy and security mechanisms for future smart devices and homes, particularly targeting awareness, control, and authentication, as well as various roles.Computer und andere „intelligente“, vernetzte Geräte sind allgegenwärtig und machen auch vor unserem privatesten Zufluchtsort keinen Halt: unserem Zuhause. Ein „intelligentes Heim“ verspricht viele Vorteile und nützliche Funktionen. Um diese zu erfüllen, sind die Geräte mit diversen Sensoren ausgestattet – sie können also in unserem Zuhause sensitive Daten sammeln, speichern und verarbeiten (bspw. Anwesenheit). Gleichzeitig sind die Geräte anfällig für (neuartige) Cyberangriffe, gefährden somit unser Zuhause und öffnen es für potenzielle – interne sowie externe – Angreifer. Beispielsweise könnten Angreifer, die digital Zugriff auf sensitive Daten wie Präsenz erhalten, einen physischen Überfall in Abwesenheit der Hausbewohner planen. Angreifer, die physischen Zugriff auf ein Gerät erhalten, könnten auf assoziierte Daten und Accounts zugreifen und diese für weitere Cyberangriffe ausnutzen. Damit werden die Privatsphäre und Sicherheit der Nutzenden in deren eigenem Zuhause gefährdet. Erschwerend kommt hinzu, dass viele Nutzenden sich dessen nicht bewusst sind und/oder nur limitierte Möglichkeiten haben, effiziente Gegenmaßnahmen zu ergreifen. Dies macht es unabdingbar, über benutzbare Mechanismen nachzudenken, die Nutzende beim Schutz ihres intelligenten Zuhauses unterstützen. Die Umsetzung solcher Mechanismen ist allerdings eine große Herausforderung. Das liegt unter anderem an der großen Vielfalt erhältlicher Geräte von verschiedensten Herstellern, was das Finden einer einheitlichen Lösung erschwert. Darüber hinaus interagieren im Heimkontext meist mehrere Nutzende in verschieden Rollen (bspw. Bewohner und Gäste), was die Gestaltung von Mechanismen zusätzlich erschwert. Diese Doktorarbeit trägt dazu bei, benutzbare Privatsphäre- und Sicherheitsmechanismen im Kontext des „intelligenten Zuhauses“ zu entwickeln. Insbesondere werden a) die Wahrnehmung von Privatsphäre sowie Anforderungen an potenzielle Mechanismen untersucht, sowie b) Konzepte und Prototypen für Privatsphäre- und Sicherheitsmechanismen vorgestellt. Der Fokus liegt hierbei auf zwei Zielgruppen, den Bewohnern sowie den Gästen eines intelligenten Zuhauses. Insbesondere werden in dieser Arbeit deren Bewusstsein für potenzielle Privatsphäre- und Sicherheits-Risiken adressiert, ihnen Kontrolle über ihre persönliche Privatsphäre und Sicherheit ermöglicht, sowie Möglichkeiten für benutzbare Authentifizierungsmechanismen für beide Zielgruppen aufgezeigt. Die Ergebnisse dieser Doktorarbeit legen den Grundstein für zukünftige Entwicklung und Evaluierung von benutzbaren Privatsphäre und Sicherheitsmechanismen im intelligenten Zuhause

A Multi-Resident Number Estimation Method for Smart Homes

Population aging requires innovative solutions to increase the quality of life and preserve autonomous and independent living at home. A need of particular significance is the identification of behavioral drifts. A relevant behavioral drift concerns sociality: older people tend to isolate themselves. There is therefore the need to find methodologies to identify if, when, and how long the person is in the company of other people (possibly, also considering the number). The challenge is to address this task in poorly sensorized apartments, with non-intrusive sensors that are typically wireless and can only provide local and simple information. The proposed method addresses technological issues, such as PIR (Passive InfraRed) blind times, topological issues, such as sensor interference due to the inability to separate detection areas, and algorithmic issues. The house is modeled as a graph to constrain transitions between adjacent rooms. Each room is associated with a set of values, for each identified person. These values decay over time and represent the probability that each person is still in the room. Because the used sensors cannot determine the number of people, the approach is based on a multi-branch inference that, over time, differentiates the movements in the apartment and estimates the number of people. The proposed algorithm has been validated with real data obtaining an accuracy of 86.8%

New Approach of Indoor and Outdoor Localization Systems

Accurate determination of the mobile position constitutes the basis of many new applications. This book provides a detailed account of wireless systems for positioning, signal processing, radio localization techniques (Time Difference Of Arrival), performances evaluation, and localization applications. The first section is dedicated to Satellite systems for positioning like GPS, GNSS. The second section addresses the localization applications using the wireless sensor networks. Some techniques are introduced for localization systems, especially for indoor positioning, such as Ultra Wide Band (UWB), WIFI. The last section is dedicated to Coupled GPS and other sensors. Some results of simulations, implementation and tests are given to help readers grasp the presented techniques. This is an ideal book for students, PhD students, academics and engineers in the field of Communication, localization & Signal Processing, especially in indoor and outdoor localization domains

An Investigation and Application of Biology and Bioinformatics for Activity Recognition

Activity recognition in a smart home context is inherently difficult due to the variable nature of human activities and tracking artifacts introduced by video-based tracking systems. This thesis addresses the activity recognition problem via introducing a biologically-inspired chemotactic approach and bioinformatics-inspired sequence alignment techniques to recognise spatial activities. The approaches are demonstrated in real world conditions to improve robustness and recognise activities in the presence of innate activity variability and tracking noise

Innovative Wireless Localization Techniques and Applications

Innovative methodologies for the wireless localization of users and related applications are addressed in this thesis. In last years, the widespread diffusion of pervasive wireless communication (e.g., Wi-Fi) and global localization services (e.g., GPS) has boosted the interest and the research on location information and services. Location-aware applications are becoming fundamental to a growing number of consumers (e.g., navigation, advertising, seamless user interaction with smart places), private and public institutions in the fields of energy efficiency, security, safety, fleet management, emergency response. In this context, the position of the user - where is often more valuable for deploying services of interest than the identity of the user itself - who. In detail, opportunistic approaches based on the analysis of electromagnetic field indicators (i.e., received signal strength and channel state information) for the presence detection, the localization, the tracking and the posture recognition of cooperative and non-cooperative (device-free) users in indoor environments are proposed and validated in real world test sites. The methodologies are designed to exploit existing wireless infrastructures and commodity devices without any hardware modification. In outdoor environments, global positioning technologies are already available in commodity devices and vehicles, the research and knowledge transfer activities are actually focused on the design and validation of algorithms and systems devoted to support decision makers and operators for increasing efficiency, operations security, and management of large fleets as well as localized sensed information in order to gain situation awareness. In this field, a decision support system for emergency response and Civil Defense assets management (i.e., personnel and vehicles equipped with TETRA mobile radio) is described in terms of architecture and results of two-years of experimental validation