Academic literature on the topic 'Wearable devices'

There is a current trend towards the use of wearable biomedical devices for the purpose of recording various biosignals, such as electrocardiograms (ECG). Wearable devices have different issues and challenges compared to nonwearable ones, including motion artifacts and contact characteristics related to body-conforming materials. Due to this susceptibility to noise and artifacts, signals acquired from wearable devices may lead to incorrect interpretations, including false alarms and misdiagnoses. This research addresses two challenges of wearable devices. First, it investigates the effect of applied pressure on biopotential electrodes that are in contact with the skin. The pressure affects skin–electrode impedance, which impacts the quality of the acquired signal. We propose a setup for measuring skin–electrode impedance during a sequence of applied calibrated pressures. The Cole–Cole impedance model is utilized to model the skin–electrode interface. Model parameters are extracted and compared in each state of measurement with respect to the amount of pressure applied. The results indicate that there is a large change in the magnitude of skin–electrode impedance when the pressure is applied for the first time, and slight changes in impedance are observed with successive application and release of pressure. Second, this research assesses the quality of ECG signals to reduce issues related to poor-quality signals, such as false alarms. We design an algorithm based on Deep Belief Networks (DBN) to distinguish clean from contaminated ECGs and validate it by applying real clean ECG signals taken from the MIT-BIH arrhythmia database of Physionet and contaminated signals with motion artifacts at varying signal-to-noise ratios (SNR). The results demonstrate that the algorithm can recognize clean from contaminated signals with an accuracy of 99.5% for signals with an SNR of -10 dB. Once low- and high-quality signals are separated, low-quality signals can undergo additional pre-processing to mitigate the contaminants, or they can simply be discarded. This approach is applied to reduce the false alarms caused by poor-quality ECG signals in atrial fibrillation (AFib) detection algorithms. We propose a signal quality gating system based on DBN and validate it with AFib signals taken from the MIT-BIH Atrial Fibrillation database of Physionet. Without gating, the AFib detection accuracy was 87% for clean ECGs, but it markedly decreased as the SNR decreased, with an accuracy of 58.7% at an SNR of -20 dB. With signal quality gating, the accuracy remained high for clean ECGs (87%) and increased for low SNR signals (81% for an SNR of -20 dB). Furthermore, since the desired level of quality is application dependent, we design a DBN-based algorithm to quantify the quality of ECG signals. Real ECG signals with various types of arrhythmias, contaminated with motion artifacts at several SNR levels, are thereby classified based on their SNRs. The results show that our algorithm can perform a multi-class classification with an accuracy of 99.4% for signals with an SNR of -20 dB and an accuracy of 91.2% for signals with an SNR of 10 dB.

Although current head imaging technologies such as magnetic resonance imaging (MRI) and computed tomography (CT) are capable of providing accurate diagnosis of brain injuries such as stroke and brain tumour, they have several limitations including high cost, long scanning time, bulky and mostly stationary. On the other hand, radar-based microwave imaging technology can offer a low cost, non-invasive and non-ionisation method to complement these existing imaging techniques. Moreover, a compact and wearable device for microwave head imaging is required to facilitate frequent or real-time monitoring of a patient by providing more comfort to the patient. Therefore, a wearable head imaging device would be a significant advantage compared to the existing wideband microwave head sensing devices which typically utilise rigid antenna structure. Furthermore, the wearable device can be integrated into different microwave imaging setups such as real-time wearable head imaging systems, portable systems and conventional stationary imaging tools for use in hospitals and clinics. This thesis presents the design and development of wearable devices utilising flexible antenna arrays and compact radio frequency (RF) switching circuits for wideband microwave head imaging applications. The design and characterisation of sensing antennas using flexible materials for the wearable head imaging device are presented in the first stage of this study. There are two main variations of monopole antennas that have been developed in this research, namely trapezoidal and elliptical configurations. The antennas have been fabricated using different flexible substrate materials such as flexible FR-4, polyethylene terephthalate (PET) and textile. Wideband performances of the antennas have been achieved by optimising their co-planar waveguide feeding line structures. Importantly, the efficiencies of the fabricated antennas have been tested using a realistic human head phantom by evaluating their impedance matching performances when operating in close proximity to the head phantom. The second stage of the study presents the development of wearable antenna arrays using the proposed flexible antennas. The first prototype has been built using an array of 12 flexible antennas and a conformal absorbing material backed with a conductive sheet to suppress the back lobe radiation of the monopole antennas. Additionally, the absorber also acts as a mounting base to hold the antennas where the wearable device can be comfortably worn like a hat during the measurement and monitoring processes. The effect of mutual coupling between adjacent antennas in the array has been investigated and optimised. However, the use of the absorbing material makes the device slightly rigid where it can only be fitted on a specific head size. Thus, a second prototype has been developed by using a head band to realise a stretchable configuration that can be mounted on different sizes of human heads. Furthermore, due to the stretchable characteristic of the prototype, the antennas can be firmly held in their positions when measurements are made. In addition, fully textile based sensing antennas are employed in this prototype making it perfectly suitable for monitoring purposes. Low cost and compact switching circuits to provide switching mechanism for the wearable antenna array are presented in the third stage of this study. The switching circuit is integrated with the antenna array to form a novel wearable microwave head imaging device eliminating the use of external bulky switching network. The switching circuit has been built using off-the-shelf components where it can be controlled wirelessly over Bluetooth connection. Then, a new integrated switching circuit prototype has been fabricated using 6-layer printed circuit board (PCB) technology. For the purpose of impedance matching for the radio-frequency (RF) routing lines on the circuit, a wideband Microstrip-to-Microstrip transition is utilised. The final stage of this study investigates the efficacy and sensitivity of the proposed wearable devices by performing experiments on developed realistic human head phantoms. Initially, a human head phantom has been fabricated using food-based ingredients such as tap water, sugar, salt, and agar. Subsequently, lamb's brains have been used to improve the head phantom employed in the experiments to better mimic the heterogeneous human brain. In terms of imaging process, an interpolation technique developed using experimental data has been proposed to assist the localisation of a haemorrhage stroke location using the confocal delay-and-sum algorithm. This new technique is able to provide sensible accuracy of the location of the blood clot inside the brain. The wearable antenna arrays using flexible antennas and their integrations with compact and low cost switching circuits reported in this thesis make valuable contribution to microwave head imaging field. It is expected that a low-cost, compact and wearable radar-based microwave head imaging can be fully realised in the future for wide range of applications including static scanning setup in hospitals, portable equipment in ambulances and as a standalone wearable head monitoring system for remote and real-time monitoring purposes.

In this thesis we explore if a proposed random binary sequence generation algorithm can be combined with a separately proposed symmetric key agreement protocol to provide usable security for communications in Wireless Body Area Networks (WBAN). Other previous works in this area fall short by only considering key generation between two of the same signals or allowing for key generation between two different types of signals but with the cost of a significant signal collection time requirement. We hoped to advance this area of research by making secure key generation more efficient with less signal collection time and allowing keys to be generated between two sensors that measure two different physiological signals. However, while the binary sequence generation algorithm and key agreement protocol perform well separately, they do not perform well together. The combined approach yields keys that have good properties for use in a WBAN, but the generation rate is low.

With the rapid growth and use of wearable devices over the last decade, the advantages of using portable wearable devices are now been utilised for day to day activities. These wearable devices are designed to be flexible, low profile, light-weight and smoothly integrated into daily life. Wearable transmission lines are required to transport RF signals between various pieces of wearable communication equipment and to connect fabric based antennas to transmitters and receivers; the stitched transmission line is one of the hardware solutions developed to enhance the connectivity between these wearable devices. Textile manufacturing techniques that employ the use of sewing machines alongside conductive textile materials can be used to fabricate the stitched transmission line. In this thesis the feasibility of using a sewing machine in fabrication of a novel stitched transmission line for wearable devices using the idea of a braided coaxial cable have been examined. The sewing machine used is capable of a zig-zag stitch with approximate width and length within the range of 0-6 mm and 0-4mm respectively. The inner conductor and the tubular insulated layer of the stitched transmission lines were selected as RG 174, while the stitched shields were made up of copper wires and conductive threads from Light Stiches®. For shielding purpose, the structure is stitched onto a denim material with a conductive thread with the aid of a novel manufacturing technique using a standard hardware. The Scattering Parameters of the stitched transmission line were investigated with three different stitch angles 85°, 65° and 31° through simulation and experiments, with the results demonstrating that the stitched transmission line can work usefully and consistently from 0.04 to 4GHz. The extracted Scattering parameters indicated a decrease in DC loss with increased stitch angle and an increase in radiation loses, which tends to increase with increase in frequency. The proposed stitched transmission line makes a viable transmission line but a short stitch length is associated with larger losses through resistance. The DC losses observed are mainly influenced by the resistance of the conductive threads at lower frequencies while the radiation losses are influenced by the wider apertures related to the stitch angles and increase in frequency along the line. The performances of the stitched transmission line with different stitch patterns, when subjected to washing cycles and when bent through curved angles 90° and 180° were also investigated and results presented. Also, the sensitivity of the design to manufacturing tolerances was also considered. First the behaviour of the stitched transmission line with two different substrates Denim and Felt were investigated with the results indicating an insignificant increase in losses with the Denim material. Secondly, the sensitivity of the design with variations in cross section dimensions was investigated using numerical modelling techniques and the results showed that the impedance of the stitched transmission line increases when the cross sectional dimensions are decreased by 0.40mm and decreases when the cross sectional dimensions are increased by 0.40mm. Equally, repeatability of the stitched transmission line with three different stitch angles 85°, 65° and 31° were carried out. The results were seen to be consistent up to 2.5GHz, with slight deviations above that, which are mainly as a result of multiple reflections along the line resulting in loss ripples. The DC resistance of the stitched transmission line with three different stitch angles 85°, 65° and 31° corresponding to the number of stitches 60,90 and 162 were computed and a mathematical relationship was derived for computing the DC resistance of the stitch transmission line for any given number of stitches. The DC resistance computed results of 25.6Ω, 17.3Ω and 13.1Ω, for 31°, 65° and 85° stitch angles, indicated an increase in DC resistance of the stitch with decrease in stitch angle which gives rise to an increase in number of stitches. The transfer impedance of the stitched transmission line was also computed at low frequency (< 1GHz) to be ZT=(0.24+j1.09)Ω, with the result showing the effectiveness of the shield of the stitched transmission line at low frequency (< 1GHz).

Wearable technologies need a smooth and unobtrusive integration of electronics and smart materials into textiles. The integration of sensors, actuators and computing technologies able to sense, react and adapt to external stimuli, is the expression of a new generation of wearable devices. The vision of wearable computing describes a system made by embedded, low power and wireless electronics coupled with smart and reliable sensors - as an integrated part of textile structure or directly in contact with the human body. Therefore, such system must maintain its sensing capabilities under the demand of normal clothing or textile substrate, which can impose severe mechanical deformation to the underlying garment/substrate. The objective of this thesis is to introduce a novel technological contribution for the next generation of wearable devices adopting a multidisciplinary approach in which knowledge of circuit design with Ultra-Wide Band and Bluetooth Low Energy technology, realization of smart piezoresistive / piezocapacitive and electro-active material, electro-mechanical characterization, design of read-out circuits and system integration find a fundamental and necessary synergy. The context and the results presented in this thesis follow an “applications driven” method in terms of wearable technology. A proof of concept has been designed and developed for each addressed issue. The solutions proposed are aimed to demonstrate the integration of a touch/pressure sensor into a fabric for space debris detection (CApture DEorbiting Target project), the effectiveness of the Ultra-Wide Band technology as an ultra-low power data transmission option compared with well known Bluetooth (IR-UWB data transmission project) and to solve issues concerning human proximity estimation (IR-UWB Face-to-Face Interaction and Proximity Sensor), wearable actuator for medical applications (EAPtics project) and aerospace physiology countermeasure (Gravity Loading Countermeasure Skinsuit project).

Background Wearable technology, which is a part of the Internet of Things (IoT), appears to be an upcoming trend with increasing importance within the business world. Nevertheless, no clear business model for companies working with wearables had been defined yet taking the influences wearables have on businesses and especially their value proposition into consideration. Purpose The purpose of this thesis is to offer input to the lack of existing literature within business models and wearables technology. The aim is to unfold a general business model that can be used within wearable companies/IoT businesses and show the influence these technologies have on them. Methodology In order to conduct an empirical research a multiple case study has been conducted, based on semi-structured interviews with eight companies, which core business consists out of wearable technology. The frameworks on business models by Gassmann et al (2014) and Osterwalder and Pigneur (2010) serve as the basis for this study and its analysis, which is based on a grounded theory approach. Results It appears that a great amount of similarities can be found through the cross-case analysis between the cases. This makes the construction of a new business model possible. The unfolded model gives also a new contribution to the theory of Hui (2014) regarding a new area of value creation and value capture within IoT businesses.

Industrial robots are delivering more and more manipulation services in manufacturing. However, when the task is complex, it is difficult to programme a robot to fulfil all the requirements because even a relatively simple task such as a peg-in-hole insertion contains many uncertainties, e.g. clearance, initial grasping position and insertion path. Humans, on the other hand, can deal with these variations using their vision and haptic feedback. Although humans can adapt to uncertainties easily, most of the time, the skilled based performances that relate to their tacit knowledge cannot be easily articulated. Even though the automation solution may not fully imitate human motion since some of them are not necessary, it would be useful if the skill based performance from a human could be firstly interpreted and modelled, which will then allow it to be transferred to the robot. This thesis aims to reduce robot programming efforts significantly by developing a methodology to capture, model and transfer the manual manufacturing skills from a human demonstrator to the robot. Recently, Learning from Demonstration (LfD) is gaining interest as a framework to transfer skills from human teacher to robot using probability encoding approaches to model observations and state transition uncertainties. In close or actual contact manipulation tasks, it is difficult to reliabley record the state-action examples without interfering with the human senses and activities. Therefore, wearable sensors are investigated as a promising device to record the state-action examples without restricting the human experts during the skilled execution of their tasks. Firstly to track human motions accurately and reliably in a defined 3-dimensional workspace, a hybrid system of Vicon and IMUs is proposed to compensate for the known limitations of the individual system. The data fusion method was able to overcome occlusion and frame flipping problems in the two camera Vicon setup and the drifting problem associated with the IMUs. The results indicated that occlusion and frame flipping problems associated with Vicon can be mitigated by using the IMU measurements. Furthermore, the proposed method improves the Mean Square Error (MSE) tracking accuracy range from 0.8˚ to 6.4˚ compared with the IMU only method. Secondly, to record haptic feedback from a teacher without physically obstructing their interactions with the workpiece, wearable surface electromyography (sEMG) armbands were used as an indirect method to indicate contact feedback during manual manipulations. A muscle-force model using a Time Delayed Neural Network (TDNN) was built to map the sEMG signals to the known contact force. The results indicated that the model was capable of estimating the force from the sEMG armbands in the applications of interest, namely in peg-in-hole and beater winding tasks, with MSE of 2.75N and 0.18N respectively. Finally, given the force estimation and the motion trajectories, a Hidden Markov Model (HMM) based approach was utilised as a state recognition method to encode and generalise the spatial and temporal information of the skilled executions. This method would allow a more representative control policy to be derived. A modified Gaussian Mixture Regression (GMR) method was then applied to enable motions reproduction by using the learned state-action policy. To simplify the validation procedure, instead of using the robot, additional demonstrations from the teacher were used to verify the reproduction performance of the policy, by assuming human teacher and robot learner are physical identical systems. The results confirmed the generalisation capability of the HMM model across a number of demonstrations from different subjects; and the reproduced motions from GMR were acceptable in these additional tests. The proposed methodology provides a framework for producing a state-action model from skilled demonstrations that can be translated into robot kinematics and joint states for the robot to execute. The implication to industry is reduced efforts and time in programming the robots for applications where human skilled performances are required to cope robustly with various uncertainties during tasks execution.

Mobile and smart devices (ranging from popular smartphones and tablets to wearable fitness trackers equipped with sensing, computing and networking capabilities) have proliferated lately and redefined the way users carry out their day-to-day activities. These devices bring immense benefits to society and boast improved quality of life for users. As mobile and smart technologies become increasingly ubiquitous, the security of these devices becomes more urgent, and users should take precautions to keep their personal information secure. Privacy has also been called into question as so many of mobile and smart devices collect, process huge quantities of data, and store them on the cloud as a matter of fact. Ensuring confidentiality, integrity, and authenticity of the information is a cybersecurity challenge with no easy solution. Unfortunately, current security controls have not kept pace with the risks posed by mobile and smart devices, and have proven patently insufficient so far. Thwarting attacks is also a thriving research area with a substantial amount of still unsolved problems. The pervasiveness of smart devices, the growing attack vectors, and the current lack of security call for an effective and efficient way of protecting mobile and smart devices. This thesis deals with the security problems of mobile and smart devices, providing specific methods for improving current security solutions. Our contributions are grouped into two related areas which present natural intersections and corresponds to the two central parts of this document: (1) Tackling Mobile Malware, and (2) Security Analysis on Wearable and Smart Devices. In the first part of this thesis, we study methods and techniques to assist security analysts to tackle mobile malware and automate the identification of malicious applications. We provide threefold contributions in tackling mobile malware: First, we introduce a Secure Message Delivery (SMD) protocol for Device-to-Device (D2D) networks, with primary objective of choosing the most secure path to deliver a message from a sender to a destination in a multi-hop D2D network. Second, we illustrate a survey to investigate concrete and relevant questions concerning Android code obfuscation and protection techniques, where the purpose is to review code obfuscation and code protection practices. We evaluate efficacy of existing code de-obfuscation tools to tackle obfuscated Android malware (which provide attackers with the ability to evade detection mechanisms). Finally, we propose a Machine Learning-based detection framework to hunt malicious Android apps by introducing a system to detect and classify newly-discovered malware through analyzing applications. The proposed system classifies different types of malware from each other and helps to better understanding how malware can infect devices, the threat level they pose and how to protect against them. Our designed system leverages more complete coverage of apps’ behavioral characteristics than the state-of-the-art, integrates the most performant classifier, and utilizes the robustness of extracted features. The second part of this dissertation conducts an in-depth security analysis of the most popular wearable fitness trackers on the market. Our contributions are grouped into four central parts in this domain: First, we analyze the primitives governing the communication between fitness tracker and cloud-based services. In addition, we investigate communication requirements in this setting such as: (i) Data Confidentiality, (ii) Data Integrity, and (iii) Data Authenticity. Second, we show real-world demos on how modern wearable devices are vulnerable to false data injection attacks. Also, we document successful injection of falsified data to cloud-based services that appears legitimate to the cloud to obtain personal benefits. Third, we circumvent End-to-End protocol encryption implemented in the most advanced and secure fitness trackers (e.g., Fitbit, as the market leader) through Hardware-based reverse engineering. Last but not least, we provide guidelines for avoiding similar vulnerabilities in future system designs.<br>I dispositivi mobili e intelligenti (dai popolari smartphone e tablet ai braccialetti per il fitness indossabili dotati di capacita' di rilevamento, elaborazione e connessione Internet) si sono recentemente diffusi e hanno ridefinito il modo in cui gli utenti svolgono le loro attivita' quotidiane. Questi dispositivi introducono enormi benefici nella societa' e portano a un miglioramento della qualita' della vita degli utenti. Man mano che le tecnologie mobili e intelligenti diventano sempre piu' diffuse, la sicurezza di questi dispositivi diventa pero' piu' urgente e gli utenti devono prendere precauzioni per mantenere le loro informazioni personali al sicuro. Anche la privacy e' stata presa in considerazione dal momento che cosi' tanti dispositivi mobili e intelligenti raccolgono, elaborano e memorizzano sul cloud enormi quantita' di dati. Garantire la riservatezza, l'integrita' e l'autenticita' delle informazioni e' una sfida nell'ambito della sicurezza informatica di non facile soluzione. Sfortunatamente, gli attuali controlli di sicurezza non hanno mantenuto il passo con i rischi introdotti dai dispositivi mobili e intelligenti, e si sono finora rivelati chiaramente insufficienti. Inoltre, la prevenzione di attacchi e' di per se' un'area di ricerca in crescita, ma con una notevole quantita' di problemi ancora irrisolti. La pervasivita' dei dispositivi intelligenti, il crescente numero di vettori di attacco e l'attuale mancanza di sicurezza richiedono un modo efficace ed efficiente di proteggere i dispositivi mobili e intelligenti. Questa tesi affronta i problemi di sicurezza dei dispositivi mobili e intelligenti, fornendo metodi specifici per migliorare le attuali soluzioni di sicurezza. I nostri contributi si raggruppano in due aree correlate, che presentano naturali sovrapposizioni e corrispondono alle due componenti centrali di questo documento: (1) il confronto con i malware mobile e (2) l'analisi della sicurezza per dispositivi indossabili e intelligenti. Nella prima parte di questa tesi, si affrontano metodi e tecniche per aiutare gli analisti della sicurezza ad affrontare i malware mobile e ad automatizzare l'identificazione di applicazioni dannose. Nell'ambito dei malware mobile, forniamo tre contributi. Per prima cosa, introduciamo un protocollo Secure Message Delivery (SMD) per reti Device-to-Device (D2D), con l'obiettivo principale di individuare il percorso piu' sicuro per inviare un messaggio dal mittente al destinatario in una rete D2D multi-hop. In secondo luogo, presentiamo un'indagine condotta col fine di indagare i problemi concreti e rilevanti che riguardano le tecniche di offuscamento e protezione del codice Android, indagine il cui scopo e' esaminare le pratiche di offuscamento e di protezione del codice. Valutiamo l'efficacia degli strumenti di de-offuscamento del codice esistenti per confrontarci con i malware Android offuscati (quelli che permettono agli hacker di sfuggire ai meccanismi di rilevamento). Infine, proponiamo un framework di rilevamento basato sul Machine Learning, che identifica le applicazioni Android maligne attraverso l'introduzione di un sistema per il rilevamento e la classificazione dei malware piu' recentemente scoperti mediante analisi delle applicazioni. Il sistema proposto classifica i malware in differenti tipi e aiuta a capire meglio come i malware possano infettare i dispositivi, il livello di minaccia che rappresentano e come ci si possa proteggere da essi. Il sistema progettato sfrutta in maniera piu' completa le caratteristiche comportamentali delle app rispetto allo stato dell'arte, integra il classificatore piu' performante e utilizza la robustezza delle funzionalita' individuate. La seconda parte di questa tesi illustra un'analisi approfondita degli aspetti di sicurezza per i braccialetti per il fitness indossabili piu' popolari sul mercato. I nostri contributi si raggruppano in quattro parti all'interno di questo contesto: come primo contributo, analizziamo le primitive che regolano la comunicazione tra i braccialetti per il fitness e i servizi sul cloud. Successivamente, esaminiamo i requisiti di comunicazione di questo contesto, quali: (i) Riservatezza dei dati, (ii) Integrita' dei dati e (iii) Autenticita' dei dati. Come secondo contributo, presentiamo delle reali dimostrazioni su come i moderni dispositivi indossabili siano vulnerabili agli attacchi di false data injection. Inoltre, documentiamo il successo di un'injection di dati falsificati all'interno servizi basati su cloud, dati che vengono considerati legittimi dal cloud e permettono di ottenere vantaggi personali. Come terzo contributo, aggiriamo la crittografia del protocollo End-to-End implementato nei piu' avanzati e sicuri braccialetti per il fitness (ad esempio, Fitbit, che e' il leader del mercato) attraverso il reverse engineering dell'hardware. Ultimo ma non meno importante, forniamo linee guida per prevenire vulnerabilita' simili nelle future progettazioni di sistemi.