LAYING THE GROUNDWORK: FITNESS TRACKER SECURITY FOR USE BY DEPARTMENT OF THE NAVY PERSONNEL

Bluetooth-capable Internet of Things devices have become prevalent within modern society and continuous connectivity has become an expectation. Recently, wearable fitness trackers have been authorized for use by service members while in uniform. The objective of this work was to determine if vulnerabilities persist in fitness trackers and if their ecosystems render them a security risk to the Department of Defense. The devices selected for this study were readily available, low-cost products, spanning several vendors. Five device models were selected from among the following vendors: Huawei, Amazfit, Garmin, and Fitbit. These devices use Bluetooth to transmit sensitive data to the paired central device. This Bluetooth traffic was captured from two separate perspectives, through passive eavesdropping and directly from the central device. This Bluetooth traffic was captured from two perspectives. The traffic was captured passively, using Project Ubertooth, and actively, logged on the central device, as the devices conducted pairing and various feature invocations. The resulting packet capture files were analyzed to determine vendor-specific security implementation and to determine susceptibility to attack. Three of the five devices studied, those from Huawei and Amazfit, utilized no Bluetooth security features. They did not conduct pairing and routinely operated in the most vulnerable stage of Bluetooth communication. This poses significant risk to the user.Lieutenant Commander, United States NavyApproved for public release. Distribution is unlimited

Sensor technology and applications to a real-time monitoring system

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2001.Includes bibliographical references (p. 85-87).Large-scale structures such as bridges, dams and buildings have caused countless fatalities in the past decades because engineers were not able to detect the early signs of failure. It is believed that with the implementation of a distributed sensor network, many of these unfortunate events could have been avoided. The ultimate goal in applying distributed sensors is for structures to combine mechanical systems and computer processing to allow them to adapt themselves in extreme conditions without human assistance. The Flagpole project is attempting to build such a monitoring system by instrumenting a model of a flagpole in a laboratory environment. The selected sensors, accelerometers, strain gauges and thermocouples, provide a complete description of the model's behavior to the physical environment. These sensors stream data into a data acquisition system, which buffers the data and directs it to a database for storage. Visualization software allows for Internet users to view the data in real-time and analyze the model's reaction to current external forces. For this system to become more automated, new sensor technology must be explored. Recent advances in the field of MEMS technology and wireless communication should be examined to build a system that incorporates decision-making at the sensor level and is expandable to larger scale systems.by David C. Greene.M.Eng

Hand Pattern Recognition Using Smart Band

The Importance of gesture recognition has widely spread around the world. Many research strategies have been proposed to study and recognize gestures, especially facial and hand gestures. Distinguishing and recognizing hand gestures is vital in hotspot fields such as bionic parts, powered exoskeleton, diagnosing muscle disorders, etc. Recognizing such gesture patterns can also create a stress-free and fancy user interface for mobile phones, gaming consoles and other such devices. The objective is to design a simple yet efficient wearable hand gesture recognizing system. This thesis also shows that by taking both EMG and accelerometer data into account, can improve the system to recognize more patterns with higher accuracy levels. For this, a hand band embedded with a triple axis accelerometer and three surface EMG electrodes is employed to source the system. The non-invasive surface EMG electrodes senses muscle action while the accelerometer senses the hand motions. The EMG signal is passed through analog front-end module for noise filtering and signal amplification. An ARM Cortex processor converts the analog EMG and accelerometer signal into digital and transmits to a PC via Bluetooth protocol. On the receiver section, the raw EMG and acceleration data is further processed and decomposed offline using MATLAB tools to extract features such as root mean square, waveform length, threshold crossing, variance and mean. Extracted features are then fed through multi-class SVM (Support Vector Machine) process for pattern recognition. The chapters below discuss in greater detail on pattern recognition technique and other modules involved

Smartphone-based food diagnostic technologies: A review

A new generation of mobile sensing approaches offers significant advantages over traditional platforms in terms of test speed, control, low cost, ease-of-operation, and data management, and requires minimal equipment and user involvement. The marriage of novel sensing technologies with cellphones enables the development of powerful lab-on-smartphone platforms for many important applications including medical diagnosis, environmental monitoring, and food safety analysis. This paper reviews the recent advancements and developments in the field of smartphone-based food diagnostic technologies, with an emphasis on custom modules to enhance smartphone sensing capabilities. These devices typically comprise multiple components such as detectors, sample processors, disposable chips, batteries and software, which are integrated with a commercial smartphone. One of the most important aspects of developing these systems is the integration of these components onto a compact and lightweight platform that requires minimal power. To date, researchers have demonstrated several promising approaches employing various sensing techniques and device configurations. We aim to provide a systematic classification according to the detection strategy, providing a critical discussion of strengths and weaknesses. We have also extended the analysis to the food scanning devices that are increasingly populating the Internet of Things (IoT) market, demonstrating how this field is indeed promising, as the research outputs are quickly capitalized on new start-up companies

On the design of Remote Health Monitoring System

With improvement in technology and miniaturization of sensors, there have been attempts to utilize the new technology in various areas to improve the quality of human life. One main area of research that has seen adoption of the technology is the healthcare sector. The people in need of healthcare services find it very expensive, this is particularly true in developing countries. With improvement in technology previously expensive hospital equipment have been redesigned using current technology. The developments have seen a trend known as remote healthcare or previously known as Telemedicine. As a result, this paper is an attempt to solve a healthcare problem facing the society. The main objective of the paper is to design a remote healthcare system. It is comprised of three main parts. The first part being detection of a fall, second being detection of electrocardiogram commonly referred to as ECG or EKG( heartbeat detection) and the last part is providing the detected data for remote viewing. Remote viewing of the data enables a doctor or health specialist to monitor a patient’s health progress away from hospital premises

A Mobile ECG Monitoring System with Context Collection

An objective of a health process is one where patients can stay healthy with the support of expert medical advice when they need it, at any location and any time. An associated aim would be the development of a system which places increased emphasis on preventative measures as a first point of contact with the patient. This research is a step along the road towards this type of preventative healthcare for cardiac patients. It seeks to develop a smart mobile ECG monitoring system that requests and records context information about what is happening around the subject when an arrhythmia event occurs. Context information about the subject’s activities of daily living will, it is hoped, provide an enriched data set for clinicians and so improve clinical decision making. As a first step towards a mobile cardiac wellness guidelines system, the focus of this work is to develop a system that can receive bio-signals wirelessly, analyzing and storing the bio-signal in a handheld device and can collect context information when there are significant changes in bio-signs. For this purpose the author will use a low cost development environment to program a state of the art wireless prototype on a handheld computer that detects and responds to changes in the heart rate as calculated form the interval between successive heart beats. Although the general approach take in this work could be applied to a wide range of bio-signals, the research will focus on ECG signals. The pieces of the system are, A wireless receiver, data collection and storage module An efficient real time ECG beat detection algorithm A rule based (Event-Condition-Action) interactive system A simple user interface, which can request additional information form the user. A selection of real-time ECG detection algorithms have been investigated and one algorithm was implemented in MATLAB [110] and then in Java [142] for this project. In order to collect ECG signals (and in principle any signals) the generalised data collection architecture has also been developed utilizing Java [142] and Bluetooth [5] technology. This architecture uses an implementation of the abstract factory pattern [91] to ensure that the communication channel can be changed conveniently. Another core part of this project is a “wellness” guideline based on Event-Condition-Action (E-C-A) [68] production rule approach that originated in active databases. The work also focuses on design of a guideline based expert system which an E-C-A based implementation will be fully event driven using the Java programming language. Based on the author’s experience and the literature review, some important issues in mobile healthcare along with the corresponding reasons, consequences and possible solutions will be presented

Multimodal Wearable Sensors for Human-Machine Interfaces

Certain areas of the body, such as the hands, eyes and organs of speech production, provide high-bandwidth information channels from the conscious mind to the outside world. The objective of this research was to develop an innovative wearable sensor device that records signals from these areas more conveniently than has previously been possible, so that they can be harnessed for communication. A novel bioelectrical and biomechanical sensing device, the wearable endogenous biosignal sensor (WEBS), was developed and tested in various communication and clinical measurement applications. One ground-breaking feature of the WEBS system is that it digitises biopotentials almost at the point of measurement. Its electrode connects directly to a high-resolution analog-to-digital converter. A second major advance is that, unlike previous active biopotential electrodes, the WEBS electrode connects to a shared data bus, allowing a large or small number of them to work together with relatively few physical interconnections. Another unique feature is its ability to switch dynamically between recording and signal source modes. An accelerometer within the device captures real-time information about its physical movement, not only facilitating the measurement of biomechanical signals of interest, but also allowing motion artefacts in the bioelectrical signal to be detected. Each of these innovative features has potentially far-reaching implications in biopotential measurement, both in clinical recording and in other applications. Weighing under 0.45 g and being remarkably low-cost, the WEBS is ideally suited for integration into disposable electrodes. Several such devices can be combined to form an inexpensive digital body sensor network, with shorter set-up time than conventional equipment, more flexible topology, and fewer physical interconnections. One phase of this study evaluated areas of the body as communication channels. The throat was selected for detailed study since it yields a range of voluntarily controllable signals, including laryngeal vibrations and gross movements associated with vocal tract articulation. A WEBS device recorded these signals and several novel methods of human-to-machine communication were demonstrated. To evaluate the performance of the WEBS system, recordings were validated against a high-end biopotential recording system for a number of biopotential signal types. To demonstrate an application for use by a clinician, the WEBS system was used to record 12‑lead electrocardiogram with augmented mechanical movement information

Unmanned aerial vehicle based wireless sensor network for marine-coastal environment monitoring

Marine environments are delicate ecosystems which directly influence local climates, flora, fauna, and human activities. Their monitorization plays a key role in their preservation, which is most commonly done through the use of environmental sensing buoy networks. These devices transmit data by means of satellite communications or close-range base stations, which present several limitations and elevated infrastructure costs. Unmanned Aerial Vehicles (UAV) are another alternative for remote environmental monitoring which provide new types of data and ease of use. These aircraft are mainly used in video capture related applications, in its various light spectrums, and do not provide the same data as sensing buoys, nor can they be used for such extended periods of time. The aim of this research is to provide a flexible, easy to deploy and cost-effectiveWireless Sensor Network (WSN) for monitoring marine environments. This proposal uses a UAV as a mobile data collector, low-power long-range communications and sensing buoys as part of a single WSN. A complete description of the design, development, and implementation of the various parts of this system is presented, as well as its validation in a real-world scenario

Pulse Signal System: Sensing, Data Acquisition and Body Area Network

Heart rate variability (HRV) is an important physiological signal of the human body, which can serve as a useful biomarker for the cardiovascular health status of an individual. There are many methods to measure the HRV using electrical devices, such as ECG and PPG etc. This work presents a novel HRV detection method which is based on pressure detection on the human wrist. This method has been compared with existing HRV detection methods. In this work, the proposed system for HRV detection is based on polyvinylidene difluoride (PVDF) sensor, which can measure tiny pressure on its surface. Three PVDF sensors are mounted on the wrist, and a three-channel conditioning circuit is used to amplify signals generated by the sensors. An analog-to-digital converter and Arduino microcontroller are used to sample and process the signal. Based on the obtained signals, the HRV can be processed and detected by the proposed PVDF-sensor-based system. Another contribution of this work is in designing a wireless body area network (WBAN) to transmit data acquired on the human body. This WBAN combines two different wireless network protocols, for both efficient power consumption and data rate. Bluetooth Low Energy protocol is used for transmitting data from the microcontroller to a personal device, and Wi-Fi is used to send data to other terminals. This provides the potential for remote HRV signal monitoring. A dataset consisting of two subjects was used to experimentally validate the proposed system design and signal processing method. ECG signals are acquired from subjects with wrist pulse signals for comparison as standard signal. The waveforms of ECG signals and wrist pulse signals are compared and HRV values are calculated from these two signals separately. The result shows that HRV calculated by wrist pulse has low error rate. A test of movement effect shows the sensor can resist mild motions of wrist. Some future improvements of system design and further signal processing methods are also discussed in the last chapter