Smartphone Household Wireless Electroencephalogram Hat

Rudimentary brain machine interface has existed for the gaming industry. Here, we propose a wireless, real-time, and smartphone-based electroencephalogram (EEG) system for homecare applications. The system uses high-density dry electrodes and compressive sensing strategies to overcome conflicting requirements between spatial electrode density, temporal resolution, and spatiotemporal throughput rate. Spatial sparseness is addressed by close proximity between active electrodes and desired source locations and using an adaptive selection of N active among 10N passive electrodes to form m-organized random linear combinations of readouts, m≪N≪10N. Temporal sparseness is addressed via parallel frame differences in hardware. During the design phase, we took tethered laboratory EEG dataset and applied fuzzy logic to compute (a) spatiotemporal average of larger magnitude EEG data centers in 0.3 second intervals and (b) inside brainwave sources by Independent Component Analysis blind deconvolution without knowing the impulse response function. Our main contributions are the fidelity of quality wireless EEG data compared to original tethered data and the speed of compressive image recovery. We have compared our recovery of ill-posed inverse data against results using Block Sparse Code. Future work includes development of strategies to filter unwanted artifact from high-density EEGs (i.e., facial muscle-related events and wireless environmental electromagnetic interferences)

Brain Waves, A Cultural History: Oscillations of Neuroscience, Technology, Telepathy, and Transcendence

This project proceeds from a narrow question: What, if anything, is a brain wave? Beguiling in its simplicity, this question prompts a cultural-historical investigation that spans over 150 years of science, technology, and society. Proposed in 1869, the original theory of brain waves cites etheric undulations to explain reports of apparent thought transference. Though most modern thinkers no longer believe in outright telepathy, I argue that dreams of thought transmission and other mental miracles subtly persist—not in obscure and occult circles, but at the forefront of technoscience. A hybrid of science and fiction, brain waves represent an ideal subject through which to explore the ways in which technical language shrouds spiritual dreams. Today, the phrase “brain waves” often function as shorthand for electrical changes in the brain, particularly in the context of technologies that purport to “read” some aspect of mental function, or to transmit neural data to a digital device. While such technologies appear uniquely modern, the history of brain waves reveals that they are merely the millennial incarnation of a much older hope—a hope for transmission and transcendence via the brain’s emanations

Cybersecurity in implantable medical devices

Mención Internacional en el título de doctorImplantable Medical Devices (IMDs) are electronic devices implanted within the body to treat a medical condition, monitor the state or improve the functioning of some body part, or just to provide the patient with a capability that he did not possess before [86]. Current examples of IMDs include pacemakers and defibrillators to monitor and treat cardiac conditions; neurostimulators for deep brain stimulation in cases such as epilepsy or Parkinson; drug delivery systems in the form of infusion pumps; and a variety of biosensors to acquire and process different biosignals. Some of the newest IMDs have started to incorporate numerous communication and networking functions—usually known as “telemetry”—, as well as increasingly more sophisticated computing capabilities. This has provided implants with more intelligence and patients with more autonomy, as medical personnel can access data and reconfigure the implant remotely (i.e., without the patient being physically present in medical facilities). Apart from a significant cost reduction, telemetry and computing capabilities also allow healthcare providers to constantly monitor the patient’s condition and to develop new diagnostic techniques based on an Intra Body Network (IBN) of medical devices [25, 26, 201]. Evolving from a mere electromechanical IMD to one with more advanced computing and communication capabilities has many benefits but also entails numerous security and privacy risks for the patient. The majority of such risks are relatively well known in classical computing scenarios, though in many respects their repercussions are far more critical in the case of implants. Attacks against an IMD can put at risk the safety of the patient who carries it, with fatal consequences in certain cases. Causing an intentional malfunction of an implant can lead to death and, as recognized by the U.S. Food and Drug Administration (FDA), such deliberate attacks could be far more difficult to detect than accidental ones [61]. Furthermore, these devices store and transmit very sensitive medical information that requires protection, as dictated by European (e.g., Directive 95/46/ECC) and U.S. (e.g., CFR 164.312) Directives [94, 204]. The wireless communication capabilities present in many modern IMDs are a major source of security risks, particularly while the patient is in open (i.e., non-medical) environments. To begin with, the implant becomes no longer “invisible”, as its presence could be remotely detected [48]. Furthermore, it facilitates the access to transmitted data by eavesdroppers who simply listen to the (insecure) channel [83]. This could result in a major privacy breach, as IMDs store sensitive information such as vital signals, diagnosed conditions, therapies, and a variety of personal data (e.g., birth date, name, and other medically relevant identifiers). A vulnerable communication channel also makes it easier to attack the implant in ways similar to those used against more common computing devices [118, 129, 156], i.e., by forging, altering, or replying previously captured messages [82]. This could potentially allow an adversary to monitor and modify the implant without necessarily being close to the victim [164]. In this regard, the concerns of former U.S. vice-president Dick Cheney constitute an excellent example: he had his Implantable Cardioverter Defibrillator (ICD) replaced by another without WiFi capability [219]. While there are still no known real-world incidents, several attacks on IMDs have been successfully demonstrated in the lab [83, 133, 143]. These attacks have shown how an adversary can disable or reprogram therapies on an ICD with wireless connectivity, and even inducing a shock state to the patient [65]. Other attacks deplete the battery and render the device inoperative [91], which often implies that the patient must undergo a surgical procedure to have the IMD replaced. Moreover, in the case of cardiac implants, they have a switch that can be turned off merely by applying a magnetic field [149]. The existence of this mechanism is motivated by the need to shield ICDs to electromagnetic fields, for instance when the patient undergoes cardiac surgery using electrocautery devices [47]. However, this could be easily exploited by an attacker, since activating such a primitive mechanism does not require any kind of authentication. In order to prevent attacks, it is imperative that the new generation of IMDs will be equipped with strong mechanisms guaranteeing basic security properties such as confidentiality, integrity, and availability. For example, mutual authentication between the IMD and medical personnel is essential, as both parties must be confident that the other end is who claims to be. In the case of the IMD, only commands coming from authenticated parties should be considered, while medical personnel should not trust any message claiming to come from the IMD unless sufficient guarantees are given. Preserving the confidentiality of the information stored in and transmitted by the IMD is another mandatory aspect. The device must implement appropriate security policies that restrict what entities can reconfigure the IMD or get access to the information stored in it, ensuring that only authorized operations are executed. Similarly, security mechanisms have to be implemented to protect the content of messages exchanged through an insecure wireless channel. Integrity protection is equally important to ensure that information has not been modified in transit. For example, if the information sent by the implant to the Programmer is altered, the doctor might make a wrong decision. Conversely, if a command sent to the implant is forged, modified, or simply contains errors, its execution could result in a compromise of the patient’s physical integrity. Technical security mechanisms should be incorporated in the design phase and complemented with appropriate legal and administrative measures. Current legislation is rather permissive in this regard, allowing the use of implants like ICDs that do not incorporate any security mechanisms. Regulatory authorities like the FDA in the U.S or the EMA (European Medicines Agency) in Europe should promote metrics and frameworks for assessing the security of IMDs. These assessments should be mandatory by law, requiring an adequate security level for an implant before approving its use. Moreover, both the security measures supported on each IMD and the security assessment results should be made public. Prudent engineering practices well known in the safety and security domains should be followed in the design of IMDs. If hardware errors are detected, it often entails a replacement of the implant, with the associated risks linked to a surgery. One of the main sources of failure when treating or monitoring a patient is precisely malfunctions of the device itself. These failures are known as “recalls” or “advisories”, and it is estimated that they affect around 2.6% of patients carrying an implant. Furthermore, the software running on the device should strictly support the functionalities required to perform the medical and operational tasks for what it was designed, and no more [66, 134, 213]. In Chapter 1, we present a survey of security and privacy issues in IMDs, discuss the most relevant mechanisms proposed to address these challenges, and analyze their suitability, advantages, and main drawbacks. In Chapter 2, we show how the use of highly compressed electrocardiogram (ECG) signals (only 24 coefficients of Hadamard Transform) is enough to unequivocally identify individuals with a high performance (classification accuracy of 97% and with identification system errors in the order of 10−2). In Chapter 3 we introduce a new Continuous Authentication scheme that, contrarily to previous works in this area, considers ECG signals as continuous data streams. The proposed ECG-based CA system is intended for real-time applications and is able to offer an accuracy up to 96%, with an almost perfect system performance (kappa statistic > 80%). In Chapter 4, we propose a distance bounding protocol to manage access control of IMDs: ACIMD. ACIMD combines two features namely identity verification (authentication) and proximity verification (distance checking). The authentication mechanism we developed conforms to the ISO/IEC 9798-2 standard and is performed using the whole ECG signal of a device holder, which is hardly replicable by a distant attacker. We evaluate the performance of ACIMD using ECG signals of 199 individuals over 24 hours, considering three adversary strategies. Results show that an accuracy of 87.07% in authentication can be achieved. Finally, in Chapter 5 we extract some conclusions and summarize the published works (i.e., scientific journals with high impact factor and prestigious international conferences).Los Dispositivos Médicos Implantables (DMIs) son dispositivos electrónicos implantados dentro del cuerpo para tratar una enfermedad, controlar el estado o mejorar el funcionamiento de alguna parte del cuerpo, o simplemente para proporcionar al paciente una capacidad que no poseía antes [86]. Ejemplos actuales de DMI incluyen marcapasos y desfibriladores para monitorear y tratar afecciones cardíacas; neuroestimuladores para la estimulación cerebral profunda en casos como la epilepsia o el Parkinson; sistemas de administración de fármacos en forma de bombas de infusión; y una variedad de biosensores para adquirir y procesar diferentes bioseñales. Los DMIs más modernos han comenzado a incorporar numerosas funciones de comunicación y redes (generalmente conocidas como telemetría) así como capacidades de computación cada vez más sofisticadas. Esto ha propiciado implantes con mayor inteligencia y pacientes con más autonomía, ya que el personal médico puede acceder a los datos y reconfigurar el implante de forma remota (es decir, sin que el paciente esté físicamente presente en las instalaciones médicas). Aparte de una importante reducción de costos, las capacidades de telemetría y cómputo también permiten a los profesionales de la atención médica monitorear constantemente la condición del paciente y desarrollar nuevas técnicas de diagnóstico basadas en una Intra Body Network (IBN) de dispositivos médicos [25, 26, 201]. Evolucionar desde un DMI electromecánico a uno con capacidades de cómputo y de comunicación más avanzadas tiene muchos beneficios pero también conlleva numerosos riesgos de seguridad y privacidad para el paciente. La mayoría de estos riesgos son relativamente bien conocidos en los escenarios clásicos de comunicaciones entre dispositivos, aunque en muchos aspectos sus repercusiones son mucho más críticas en el caso de los implantes. Los ataques contra un DMI pueden poner en riesgo la seguridad del paciente que lo porta, con consecuencias fatales en ciertos casos. Causar un mal funcionamiento intencionado en un implante puede causar la muerte y, tal como lo reconoce la Food and Drug Administration (FDA) de EE.UU, tales ataques deliberados podrían ser mucho más difíciles de detectar que los ataques accidentales [61]. Además, estos dispositivos almacenan y transmiten información médica muy delicada que requiere se protegida, según lo dictado por las directivas europeas (por ejemplo, la Directiva 95/46/ECC) y estadunidenses (por ejemplo, la Directiva CFR 164.312) [94, 204]. Si bien todavía no se conocen incidentes reales, se han demostrado con éxito varios ataques contra DMIs en el laboratorio [83, 133, 143]. Estos ataques han demostrado cómo un adversario puede desactivar o reprogramar terapias en un marcapasos con conectividad inalámbrica e incluso inducir un estado de shock al paciente [65]. Otros ataques agotan la batería y dejan al dispositivo inoperativo [91], lo que a menudo implica que el paciente deba someterse a un procedimiento quirúrgico para reemplazar la batería del DMI. Además, en el caso de los implantes cardíacos, tienen un interruptor cuya posición de desconexión se consigue simplemente aplicando un campo magnético intenso [149]. La existencia de este mecanismo está motivada por la necesidad de proteger a los DMIs frete a posibles campos electromagnéticos, por ejemplo, cuando el paciente se somete a una cirugía cardíaca usando dispositivos de electrocauterización [47]. Sin embargo, esto podría ser explotado fácilmente por un atacante, ya que la activación de dicho mecanismo primitivo no requiere ningún tipo de autenticación. Garantizar la confidencialidad de la información almacenada y transmitida por el DMI es otro aspecto obligatorio. El dispositivo debe implementar políticas de seguridad apropiadas que restrinjan qué entidades pueden reconfigurar el DMI o acceder a la información almacenada en él, asegurando que sólo se ejecuten las operaciones autorizadas. De la misma manera, mecanismos de seguridad deben ser implementados para proteger el contenido de los mensajes intercambiados a través de un canal inalámbrico no seguro. La protección de la integridad es igualmente importante para garantizar que la información no se haya modificado durante el tránsito. Por ejemplo, si la información enviada por el implante al programador se altera, el médico podría tomar una decisión equivocada. Por el contrario, si un comando enviado al implante se falsifica, modifica o simplemente contiene errores, su ejecución podría comprometer la integridad física del paciente. Los mecanismos de seguridad deberían incorporarse en la fase de diseño y complementarse con medidas legales y administrativas apropiadas. La legislación actual es bastante permisiva a este respecto, lo que permite el uso de implantes como marcapasos que no incorporen ningún mecanismo de seguridad. Las autoridades reguladoras como la FDA en los Estados Unidos o la EMA (Agencia Europea de Medicamentos) en Europa deberían promover métricas y marcos para evaluar la seguridad de los DMIs. Estas evaluaciones deberían ser obligatorias por ley, requiriendo un nivel de seguridad adecuado para un implante antes de aprobar su uso. Además, tanto las medidas de seguridad implementadas en cada DMI como los resultados de la evaluación de su seguridad deberían hacerse públicos. Buenas prácticas de ingeniería en los dominios de la protección y la seguridad deberían seguirse en el diseño de los DMIs. Si se detectan errores de hardware, a menudo esto implica un reemplazo del implante, con los riesgos asociados y vinculados a una cirugía. Una de las principales fuentes de fallo al tratar o monitorear a un paciente es precisamente el mal funcionamiento del dispositivo. Estos fallos se conocen como “retiradas”, y se estima que afectan a aproximadamente el 2,6 % de los pacientes que llevan un implante. Además, el software que se ejecuta en el dispositivo debe soportar estrictamente las funcionalidades requeridas para realizar las tareas médicas y operativas para las que fue diseñado, y no más [66, 134, 213]. En el Capítulo 1, presentamos un estado de la cuestión sobre cuestiones de seguridad y privacidad en DMIs, discutimos los mecanismos más relevantes propuestos para abordar estos desafíos y analizamos su idoneidad, ventajas y principales inconvenientes. En el Capítulo 2, mostramos cómo el uso de señales electrocardiográficas (ECGs) altamente comprimidas (sólo 24 coeficientes de la Transformada Hadamard) es suficiente para identificar inequívocamente individuos con un alto rendimiento (precisión de clasificación del 97% y errores del sistema de identificación del orden de 10−2). En el Capítulo 3 presentamos un nuevo esquema de Autenticación Continua (AC) que, contrariamente a los trabajos previos en esta área, considera las señales ECG como flujos de datos continuos. El sistema propuesto de AC basado en señales cardíacas está diseñado para aplicaciones en tiempo real y puede ofrecer una precisión de hasta el 96%, con un rendimiento del sistema casi perfecto (estadístico kappa > 80 %). En el Capítulo 4, proponemos un protocolo de verificación de la distancia para gestionar el control de acceso al DMI: ACIMD. ACIMD combina dos características, verificación de identidad (autenticación) y verificación de la proximidad (comprobación de la distancia). El mecanismo de autenticación es compatible con el estándar ISO/IEC 9798-2 y se realiza utilizando la señal ECG con todas sus ondas, lo cual es difícilmente replicable por un atacante que se encuentre distante. Hemos evaluado el rendimiento de ACIMD usando señales ECG de 199 individuos durante 24 horas, y hemos considerando tres estrategias posibles para el adversario. Los resultados muestran que se puede lograr una precisión del 87.07% en la au tenticación. Finalmente, en el Capítulo 5 extraemos algunas conclusiones y resumimos los trabajos publicados (es decir, revistas científicas con alto factor de impacto y conferencias internacionales prestigiosas).Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Arturo Ribagorda Garnacho.- Secretario: Jorge Blasco Alís.- Vocal: Jesús García López de Lacall

Mögliche gesundheitliche Auswirkungen verschiedener Frequenzbereiche elektromagnetischer Felder (HF-EMF). Endbericht zum TA-Projekt

Hochfrequente elektromagnetische Felder (HF-EMF) bilden die Grundlage aller digitalen, drahtlosen Kommunikation im gesamten öffentlichen Raum und in den privaten Haushalten. In den kommenden Jahren ist mit einer weiteren Zunahme von EMF-Quellen verschiedener Frequenzbereiche zu rechnen. Hauptgrund hierfür ist die rasant fortschreitende Digitalisierung nahezu aller Arbeits-, Lebens- und Wirtschaftsbereiche, die zugleich eng mit mobil zu nutzenden Technologien verbunden ist. Vor diesem Hintergrund stellt der vorliegende Bericht den aktuellen Wissensstand zu möglichen gesundheitlichen Risiken elektromagnetischer Felder – insbesondere des Mobilfunks – dar. Dazu wurde die neuere internationale wissenschaftliche Literatur umfassend gesichtet und die Ergebnisse aktueller nationaler und internationaler Forschungsprojekten daraufhin analysiert, ob relevante neue Erkenntnisse vorliegen, die die Diskussionen zu möglichen gesundheitlichen Risiken der HF-EMF substanziell verändern könnten. Ein weiterer Schwerpunkt lag auf Forschungsbemühungen, die einen substanziellen Beitrag zur verbesserten Risikobewertung der Exposition von jungen Menschen leisten wollen. Darüber hinaus diskutiert der Bericht relevante Aspekte der EMF-Risikogovernance (z.B. Öffentlichkeitsbeteiligung, Interessenkonflikte, Risikoinformation und -kommunikation) und beschreibt Optionen, wie im Kontext des EMF-Diskurses Barrieren für eine offene wechselseitige Kommunikation von Akteursgruppen – insbesondere zwischen Wissenschaft, Zivilgesellschaft und Politik – abgebaut werden können

Characterization of Physiological Biomarkers in Long-Term Kratom (Mitragyna speciosa Korth.) Users: A Preliminary Study

Doctor of Philosophy (Physiology), 2022A neurophysiological outcome associated with long-term kratom chewing in traditional use context is still unknown. Thus, the primary aim of this study was to investigate biomarkers of neurological response to the long-term kratom chewing. The fifty-two participants (controls; n=24 and long-term kratom users (LKU) who chewed kratom leaves; n = 28) were recruited with background-matched control group. Neurophysiological parameters with the proposed EEG (Theta/alpha ratio (TAR) and power function variance (PVFA), and all domains of ultra-short heart rate variability (HRV) heart rate variability were assessed during resting-state. Cognitive performance (Working memory) and kratom dependence score rating were also examined. All the proposed features were compared between the controls and long-term kratom chewers and determined in the relevant factors (age, duration, and daily quantity of kratom use). The statistically significant proposed features were proved by 1) path analysis for evaluating the causal relationship, and 2) the recognized machine-learning algorithms (Random Forest, Support vector machine, k Nearest neighbor, and Logistic regression) for binary classification. The results showed that only the proposed EEG feature (TAR) was significantly increased, compared to the control in the same age range of 50 years. The increased TAR and decreased PVF in the alpha band (PVFA) were direct effects of kratom leaves use and were significantly observed in LKU with a very high dose use. In addition, PVFA was a negative correlation with Kratom dependence. The results were also confirmed by the support vector machine achieved the highest performance to classify LKU with different doses of Kratom consuming by using the combination features TAR (both electrodes and average) and PVF in the alpha band. These preliminary results first highlighted the sensitive EEG biomarkers to characterize the LKU with a large effect size. These findings may lead to effective machine learning approaches based on EEG biomarkers for screening excessive Kratom users that might eventually develop Kratom dependence.1. Graduate School Dissertation Funding for Thesis and Revenue Budget Fund 2. Educational Institutions Scholarship for Outstanding GPAในปัจจุบันการศึกษาผลตอบสนองต่อการบริโภคพืชกระท่อมระยะเวลานานทางสรีรวิทยา ระบบประสาทที่ยังไม่ทราบเป็ นที่แน่ชัด การศึกษานี้จึงมีวัตถุประสงค์เพื่อหาตัวบ่งชี้ทางชีวภาพ ของการตอบสนองดังกล่าวจากอาสาสมัครจํานวน 52 คน ประกอบไปด้วย ผู้ใช้กระท่อมเป็ นระยะ เวลานานจํานวน 28 คน และกลุ่มควบคุมจํานวน 24 คน ทําการบันทึกสัญญาณ 1) คลื่นไฟฟ้าสมอง ด้วยอุปกรณ์พกพาอย่างง่ายเพื่อสกัดอัตราส่วนคลื่นพลังงานธีต้า/อัลฟ่ า และความแปรปรวนของ พลังงานคลื่นความถี่ และ 2) คลื่นไฟฟ้าหัวใจเพื่อสกัดความแปรปรวนของอัตราการเต้นของหัวใจ นอกจากนี้ยังทําการทดสอบเชิงประสาทพฤติกรรม เช่น ความสามารถทางด้านการรู้คิด(ความจําเพื่อ ใช้งาน) และ อาการติดกระท่อม จากนั้นทําการทดสอบทางสถิติเพื่อเปรียบเทียบผู้เคี้ยวพืชกระท่อม และกลุ่มควบคุม และศึกษาในด้านปัจจัยต่างๆ (อายุ ระยะเวลาที่บริโภค และ ปริมาณบริโภคพืช กระท่อมต่อวัน) ตัวชี้วัดที่มีนัยสําคัญทางสถิติจะถูกนําไปทดสอบด้วย 1) การวิเคราะห์เส้นทางเพื่อ ดูความสัมพันธ์เชิงเหตุผลระหว่างตัวชี้วัดดังกล่าวกับปัจจัยต่างๆ และ ตัวชี้วัดทางประสาท พฤติกรรม และ 2) การจําแนกกลุ่มแบบไบนารีด้วยแมชชีนเลิร์นนิ่งทัวไป ่ ผลการทดลองพบว่า ตัวชี้วัดทางคลื่นไฟฟ้าสมองเท่านั้นที่พบความแตกต่างอย่างมีนัยสําคัญระหว่างกลุ่ม โดยระดับ อัตราส่วนคลื่นพลังงานธีต้า/อัลฟ่ าเพิ่ มขึ้นอย่างมีนัยสําคัญในกลุ่มผู้บริโภคพืชกระท่อมเทียบกลับ กลุ่มควบคุมเมื่อพิจารณาในช่วงอายุ> 50 ปี และเพิ่ มขึ้นอย่างเห็นได้ชัดในกลุ่มบริโภคพืชกระท่อม ในปริ มาณมาก นอกจากนี้ความแปรปรวนของพลังงานคลื่นความถี่ของอัลฟ่ ายังลดลงในกลุ่ม ดังกล่าวอีกด้วย และมีความสัมพันธ์กับคะแนนที่ทําการประเมินอาการติดกระท่อม ผลการ เปลี่ยนแปลงดังกล่าวเป็ นผลโดยตรงจากปริมาณการใช้พืชกระท่อมวัดจากการวิเคราะห์ด้วยเส้นทาง อีกทั้งการรวมคุณลักษณะของตัวชี้วัดทางคลื่นสมองดังกล่าวยังสามารถใช้จําแนกกลุ่มผู้ที่บริโภค ในขนาดต่างกันโดยใช้อัลกอรึทึมของซัพพอร์ตเวกเตอร์แมชชีนในการประเมิน ดังนั้นการศึกษา เบื้องต้นนี้แสดงถึงความไวของตัวชี้วัดชีวภาพด้วยคลื่นไฟฟ้าสมองและในอนาคตอาจมีความ เป็ นไปได้ที่จะใช้แมชชีนเลิร์นิ่ งจําแนกผู้บริโภคพืชกระท่อมเกินขนาดที่อาจจะพัฒนาไปสู่อาการติด กระท่อมโดยด้วยคุณลักษณะของคลื่นไฟฟ้าสมองดังกล่า

2018 - The Twenty-third Annual Symposium of Student Scholars

The full program book from the Twenty-third Annual Symposium of Student Scholars, held on April 19, 2018. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1020/thumbnail.jp

Use of Smart Technology Tools for Supporting Public Health Surveillance: From Development of a Mobile Health Platform to Application in Stress Prediction

BACKGROUND Traditional public health data collection methods are typically based on self-reported data and may be subject to limitations such as biases, delays between collection and reporting, costs, and logistics. These may affect the quality of collected information and the ability of public health agencies to monitor and improve the health of populations. An alternative may be the use of personal, off-the-shelf smart devices (e.g., smartphones and smartwatches) as additional data collection tools. These devices can collect passive, continuous, real-time and objective health-related data, mitigating some of the limitations of self-reported information. The novel data types can then be used to further study and predict a condition in a population through advanced analytics. In this context, this thesis’ goal is to investigate new ways to support public health through the use of consumer-level smart technologies as complementary survey, monitoring and analyses tools, with a focus on perceived stress. To this end, a mobile health platform (MHP) that collects data from devices connected to Apple Health was developed and tested in a pilot study collecting self-reported and objective stress-related information, and a number of Machine Learning (ML) models were developed based on these data to monitor and predict the stress levels of participants. METHODS The mobile platform was created for iOS using the XCode software, allowing users to self-report their stress levels based on the stress subscale of the Depression, Anxiety and Stress Scale (DASS-21) as well as a single-item LIKERT-based scale. The platform also collects objective data from sensors that integrate with Apple Health, one of the most popular mobile health data repositories. A pilot study with 45 participants was conducted that uses the platform to collects stress self-reports and variables associated with stress from Apple Health, including heart rate, heart rate variability, ECG, sleep, blood pressure, weight, temperature, and steps. To this end, participants were given an iPhone with the platform installed as well as an Apple Watch, Withings Sleep, Withings Thermos, Withings BPM Connect, Withings Body+, and an Empatica E4 (the only device that does not connect to Apple Health but included due to its wide use in research). Participants were instructed to take device measurements and self-report stress levels 6 times per day for 14 days. Several experiments were conducted involving the development of ML models to predict stress based on the data, using Random Forests and Support Vector Machines. In each experiment, different subsets of the data from the full sample of 45 participants were used. 3 approaches to model development were followed: a) creating generalized models with all data; b) a hybrid approach using 80% of participants to train and 20% to test the model c) creating individualized user-specific models for each participant. In addition, statistical analyses of the data – specifically Spearman correlation and repeated measures ANOVA – were conducted. RESULTS Statistical analyses did not find significant differences between groups and only weak significant correlations. Among the Machine Learning models, the approach of using generalized models performed well, with f1-macro scores above 60% for several of the samples and features investigated. User-specific models also showed promise, with 82% achieving accuracies higher than 60% (the bottom limit of the state-of-the-art). While the hybrid approach had lower f1-macro scores, suggesting the models could not predict the two classes well, the accuracy of several of these models was in line with the state-of-the-art. Apple Watch sleep features, as well as weight, blood pressure, and temperature, were shown to be important in building the models. DISCUSSION AND CONCLUSION ML-based models built with data collected from the MHP in real-life conditions were able to predict stress with results often in line with state-of-the- art, showing that smart technology data can be a promising tool to support public health surveillance. In particular, the approaches of creating models for each participant or one generalized model were successful, although more validation is needed in future studies (e.g., with more purposeful sampling) for increased generalizability and validity on the use of these technologies in the real-world. The hybrid approach had good accuracy but lower f1-scores, indicating results could potentially be improved (e.g., possibly with less missing or noisy data, collected in more controlled conditions). For feature selection, important features included sleep data as well as weight, blood pressure and temperature from mobile and wearable devices. In summary, this study indicates that a platform such as the MHP, collecting data from smart technologies, could potentially be a novel tool to complement population-level public health data collection. The predictive stress modelling might be used to monitor stress levels in a population and provide personalized interventions. Although more validation may be needed, this work represents a step in this direction

Workload-aware systems and interfaces for cognitive augmentation

In today's society, our cognition is constantly influenced by information intake, attention switching, and task interruptions. This increases the difficulty of a given task, adding to the existing workload and leading to compromised cognitive performances. The human body expresses the use of cognitive resources through physiological responses when confronted with a plethora of cognitive workload. This temporarily mobilizes additional resources to deal with the workload at the cost of accelerated mental exhaustion. We predict that recent developments in physiological sensing will increasingly create user interfaces that are aware of the user’s cognitive capacities, hence able to intervene when high or low states of cognitive workload are detected. In this thesis, we initially focus on determining opportune moments for cognitive assistance. Subsequently, we investigate suitable feedback modalities in a user-centric design process which are desirable for cognitive assistance. We present design requirements for how cognitive augmentation can be achieved using interfaces that sense cognitive workload. We then investigate different physiological sensing modalities to enable suitable real-time assessments of cognitive workload. We provide empirical evidence that the human brain is sensitive to fluctuations in cognitive resting states, hence making cognitive effort measurable. Firstly, we show that electroencephalography is a reliable modality to assess the mental workload generated during the user interface operation. Secondly, we use eye tracking to evaluate changes in eye movements and pupil dilation to quantify different workload states. The combination of machine learning and physiological sensing resulted in suitable real-time assessments of cognitive workload. The use of physiological sensing enables us to derive when cognitive augmentation is suitable. Based on our inquiries, we present applications that regulate cognitive workload in home and work settings. We deployed an assistive system in a field study to investigate the validity of our derived design requirements. Finding that workload is mitigated, we investigated how cognitive workload can be visualized to the user. We present an implementation of a biofeedback visualization that helps to improve the understanding of brain activity. A final study shows how cognitive workload measurements can be used to predict the efficiency of information intake through reading interfaces. Here, we conclude with use cases and applications which benefit from cognitive augmentation. This thesis investigates how assistive systems can be designed to implicitly sense and utilize cognitive workload for input and output. To do so, we measure cognitive workload in real-time by collecting behavioral and physiological data from users and analyze this data to support users through assistive systems that adapt their interface according to the currently measured workload. Our overall goal is to extend new and existing context-aware applications by the factor cognitive workload. We envision Workload-Aware Systems and Workload-Aware Interfaces as an extension in the context-aware paradigm. To this end, we conducted eight research inquiries during this thesis to investigate how to design and create workload-aware systems. Finally, we present our vision of future workload-aware systems and workload-aware interfaces. Due to the scarce availability of open physiological data sets, reference implementations, and methods, previous context-aware systems were limited in their ability to utilize cognitive workload for user interaction. Together with the collected data sets, we expect this thesis to pave the way for methodical and technical tools that integrate workload-awareness as a factor for context-aware systems.Tagtäglich werden unsere kognitiven Fähigkeiten durch die Verarbeitung von unzähligen Informationen in Anspruch genommen. Dies kann die Schwierigkeit einer Aufgabe durch mehr oder weniger Arbeitslast beeinflussen. Der menschliche Körper drückt die Nutzung kognitiver Ressourcen durch physiologische Reaktionen aus, wenn dieser mit kognitiver Arbeitsbelastung konfrontiert oder überfordert wird. Dadurch werden weitere Ressourcen mobilisiert, um die Arbeitsbelastung vorübergehend zu bewältigen. Wir prognostizieren, dass die derzeitige Entwicklung physiologischer Messverfahren kognitive Leistungsmessungen stets möglich machen wird, um die kognitive Arbeitslast des Nutzers jederzeit zu messen. Diese sind in der Lage, einzugreifen wenn eine zu hohe oder zu niedrige kognitive Belastung erkannt wird. Wir konzentrieren uns zunächst auf die Erkennung passender Momente für kognitive Unterstützung welche sich der gegenwärtigen kognitiven Arbeitslast bewusst sind. Anschließend untersuchen wir in einem nutzerzentrierten Designprozess geeignete Feedbackmechanismen, die zur kognitiven Assistenz beitragen. Wir präsentieren Designanforderungen, welche zeigen wie Schnittstellen eine kognitive Augmentierung durch die Messung kognitiver Arbeitslast erreichen können. Anschließend untersuchen wir verschiedene physiologische Messmodalitäten, welche Bewertungen der kognitiven Arbeitsbelastung in Realzeit ermöglichen. Zunächst validieren wir empirisch, dass das menschliche Gehirn auf kognitive Arbeitslast reagiert. Es zeigt sich, dass die Ableitung der kognitiven Arbeitsbelastung über Elektroenzephalographie eine geeignete Methode ist, um den kognitiven Anspruch neuartiger Assistenzsysteme zu evaluieren. Anschließend verwenden wir Eye-Tracking, um Veränderungen in den Augenbewegungen und dem Durchmesser der Pupille unter verschiedenen Intensitäten kognitiver Arbeitslast zu bewerten. Das Anwenden von maschinellem Lernen führt zu zuverlässigen Echtzeit-Bewertungen kognitiver Arbeitsbelastung. Auf der Grundlage der bisherigen Forschungsarbeiten stellen wir Anwendungen vor, welche die Kognition im häuslichen und beruflichen Umfeld unterstützen. Die physiologischen Messungen stellen fest, wann eine kognitive Augmentierung sich als günstig erweist. In einer Feldstudie setzen wir ein Assistenzsystem ein, um die erhobenen Designanforderungen zur Reduktion kognitiver Arbeitslast zu validieren. Unsere Ergebnisse zeigen, dass die Arbeitsbelastung durch den Einsatz von Assistenzsystemen reduziert wird. Im Anschluss untersuchen wir, wie kognitive Arbeitsbelastung visualisiert werden kann. Wir stellen eine Implementierung einer Biofeedback-Visualisierung vor, die das Nutzerverständnis zum Verlauf und zur Entstehung von kognitiver Arbeitslast unterstützt. Eine abschließende Studie zeigt, wie Messungen kognitiver Arbeitslast zur Vorhersage der aktuellen Leseeffizienz benutzt werden können. Wir schließen hierbei mit einer Reihe von Applikationen ab, welche sich kognitive Arbeitslast als Eingabe zunutze machen. Die vorliegende wissenschaftliche Arbeit befasst sich mit dem Design von Assistenzsystemen, welche die kognitive Arbeitslast der Nutzer implizit erfasst und diese bei der Durchführung alltäglicher Aufgaben unterstützt. Dabei werden physiologische Daten erfasst, um Rückschlüsse in Realzeit auf die derzeitige kognitive Arbeitsbelastung zu erlauben. Anschließend werden diese Daten analysiert, um dem Nutzer strategisch zu assistieren. Das Ziel dieser Arbeit ist die Erweiterung neuartiger und bestehender kontextbewusster Benutzerschnittstellen um den Faktor kognitive Arbeitslast. Daher werden in dieser Arbeit arbeitslastbewusste Systeme und arbeitslastbewusste Benutzerschnittstellen als eine zusätzliche Dimension innerhalb des Paradigmas kontextbewusster Systeme präsentiert. Wir stellen acht Forschungsstudien vor, um die Designanforderungen und die Implementierung von kognitiv arbeitslastbewussten Systemen zu untersuchen. Schließlich stellen wir unsere Vision von zukünftigen kognitiven arbeitslastbewussten Systemen und Benutzerschnittstellen vor. Durch die knappe Verfügbarkeit öffentlich zugänglicher Datensätze, Referenzimplementierungen, und Methoden, waren Kontextbewusste Systeme in der Auswertung kognitiver Arbeitslast bezüglich der Nutzerinteraktion limitiert. Ergänzt durch die in dieser Arbeit gesammelten Datensätze erwarten wir, dass diese Arbeit den Weg für methodische und technische Werkzeuge ebnet, welche kognitive Arbeitslast als Faktor in das Kontextbewusstsein von Computersystemen integriert

The Largest Unethical Medical Experiment in Human History

This monograph describes the largest unethical medical experiment in human history: the implementation and operation of non-ionizing non-visible EMF radiation (hereafter called wireless radiation) infrastructure for communications, surveillance, weaponry, and other applications. It is unethical because it violates the key ethical medical experiment requirement for “informed consent” by the overwhelming majority of the participants. The monograph provides background on unethical medical research/experimentation, and frames the implementation of wireless radiation within that context. The monograph then identifies a wide spectrum of adverse effects of wireless radiation as reported in the premier biomedical literature for over seven decades. Even though many of these reported adverse effects are extremely severe, the true extent of their severity has been grossly underestimated. Most of the reported laboratory experiments that produced these effects are not reflective of the real-life environment in which wireless radiation operates. Many experiments do not include pulsing and modulation of the carrier signal, and most do not account for synergistic effects of other toxic stimuli acting in concert with the wireless radiation. These two additions greatly exacerbate the severity of the adverse effects from wireless radiation, and their neglect in current (and past) experimentation results in substantial under-estimation of the breadth and severity of adverse effects to be expected in a real-life situation. This lack of credible safety testing, combined with depriving the public of the opportunity to provide informed consent, contextualizes the wireless radiation infrastructure operation as an unethical medical experiment

Designing performance systems for audience inclusion

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 154-168).We define the concept of the Hyperaudience and a unique approach towards designing real-time interactive performance systems: the design of these systems encourages audience participation and augments the experience of audience members through interconnected networks. In doing so, it embraces concepts found in ubiquitous computing, affective computing, interactive arts, music, theatrical tradition, and pervasive gaming. In addition, five new systems are demonstrated to develop a framework for thinking about audience participation and orchestrating social co-presence in and beyond the performance space. Finally, the principles and challenges that shaped the design of these five systems are defined by measuring, comparing, and evaluating their expressiveness and communicability.by Akito Van Troyer.S.M