Brain-computer interfaces (BCIs) provide direct access to the brain, serving crucial roles in treating neurological disorders and developing neural prostheses. Recent clinical successes include diagnosing and treating epilepsy and advancing prosthesis for visual and limb impairments. Achieving high spatial and temporal resolution is essential for accurately localizing seizures, mapping brain functions, and controlling neuronal activity. However, existing solutions have substantial form factors, necessitating large-size craniotomy, permanent removal of a part of the skull, or wires running through the body, which limits real-world applicability and complicates post-surgery recovery. We present a minimally invasive, high-bandwidth, and fully wireless brain-machine interface platform that addresses these challenges through a combination of an implantable application-specific integrated circuit (ASIC) chip and a wearable relay station. The platform supports an aggregate sampling rate of 8.68 MSPS at 10-bit resolution and a 108.48/54.24 Mbps data rate using impulse radio ultra-wideband (IR-UWB). A high-density microelectrode array (HD-MEA) with configurable electrode options is integrated into the ASIC implant, enabling simultaneous readout of 1024/256 channels at 8.48/33.9 KSPS. By reducing the ASIC implant to a thickness of 25 µm, the total volume of the implant is only 3.6 mm³, making it thinner than a strand of human hair and occupying less than a third of the volume of a grain of rice. We conducted in-vivo experiments in the cortices of pigs and monkeys and successfully achieved ultra-high resolution receptive field mapping. This work sets a new standard for volumetric efficiency in implantable brain-computer interfaces.

Wearable activity classifiers, so far, have been able to perform well with simple activities, strictly-scripted activities, and application-specific activities. In addition, current classification systems suffer from using impractical tight-fitting sensor networks, or only use one loose-fitting sensor node that cannot capture much movement information (e.g., smartphone sensors and wrist-worn sensors). These classifiers either do not address the bigger picture of making activity recognition more practical and being able to recognize more complex and naturalistic activities, or try to address this issue but still perform poorly on many fronts. This dissertation works toward having practical, in-the-wild, and reusable wearable activity classifiers by taking several steps that include the four following main contributions. The dissertation starts by quantifying users' needs and expectations from wearable activity classifiers to set a framework for designing ideal wearable activity classifiers. Data collected from user studies and interviews is gathered and analyzed, then several conclusions are made to set a framework of essential characteristics that ideal wearable activity classification systems should have. Afterwards, this dissertation introduces a group of datasets that can be used to benchmark different types of activity classifiers and can accommodate for a variety of goals. These datasets help comparing different algorithms in activity classification to assess their performance under various circumstances and with different types of activities. The third main contribution consists of developing a technique that can classify complex activities with wide variations. Testing this technique shows that it is able to accurately classify eight complex daily-life activities with wide variations at an accuracy rate of 93.33%, significantly outperforming the state-of-the-art. This technique is a step forward toward classifying real-life natural activities performed in an environment that allows for wide variations within the activity. Finally, this dissertation introduces a method that can be used on top of any activity classifier that allows access to its matching scores in order to improve its classification accuracy. Testing this method shows that it improves classification results by 11.86% and outperforms the state-of-the-art, therefore taking a step forward toward having reusable activity classification techniques that can be used across users, sensor domains, garments, and applications. / Ph. D. / Wearable activity classifiers are wearable systems that can recognize human activities. These systems are needed in many applications. Nowadays, they are mainly used for fitness purposes – e.g., Fitbits and Apple Watches – and in gaming consoles – e.g., Microsoft Kinect. However, these systems are still far from being ideal. They still miss many characteristics that would make them practical and usable for different purposes, such as in medical applications, industrial applications, and other types of applications where recognizing human activities can be useful. This dissertation works toward having practical wearable activity classifiers that can be reused for different purposes in real-life scenarios. Four contributions are introduced in this dissertation. The dissertation starts by quantifying users’ needs and expectations from wearable activity classifiers and sets a framework for designing them. Afterward, this dissertation introduces a group of datasets that can be used to benchmark and compare different types of activity classifiers. The third main contribution consists of a technique that enables activity classifiers to recognize complex activities having a wide range of variations within each activity. Finally, this dissertation introduces a method that can be used to improve the recognition accuracy of activity classifiers.

Yes / Many children are not engaging in sufficient physical activity and there are substantial between-children physical activity inequalities. In addition to their primary role as educators, teachers are often regarded as being well-placed to make vital contributions to inclusive visions of physical activity promotion. With the dramatic increase in popularity of wearable technologies for physical activity promotion in recent years, there is a need to better understand teachers' perspectives about using such devices, and the data they produce, to support physical activity promotion in schools. Method: Semi-structured interviews were conducted with 26 UK-based primary school teachers, exploring their responses to children's physical activity data and their views about using wearable technologies during the school day. Interview discussions were facilitated by an elicitation technique whereby participants were presented with graphs illustrating children's in-school physical activity obtained from secondary wearable technology data. Interview transcripts were thematically analyzed. Results: Most teachers spoke positively about the use of wearable technologies specifically designed for school use, highlighting potential benefits and considerations. Many teachers were able to understand and critically interpret data showing unequal physical activity patterns both within-and between-schools. Being presented with the data prompted teachers to provide explanations about observable patterns, emotional reactions-particularly about inequalities-and express motivations to change the current situations in schools. Conclusion: These findings suggest that primary school teachers in the UK are open to integrating wearable technology for measuring children's physical activity into their practices and can interpret the data produced by such devices. Visual representations of physical activity elicited strong responses and thus could be used when working with teachers as an effective trigger to inform school practices and policies seeking to address in-school physical inactivity and inequalities.

Upper Limb work-related musculoskeletal disorders (WMSDs) present a significant health risk to industrial workers. To address this, rigid-body exoskeletons have been widely used in industrial settings to mitigate these risks while exosuits offer advantages such as reduced weight, lower inertia, and no need for precise joint alignment, However, they remain in the early stages of development, especially for reducing muscular effort in repetitive and forceful tasks like heavy lifting and overhead work. This study introduces a multiple degrees-of-freedom cable-driven upper limb bilateral exosuit for human power augmentation. Two control schemes were developed and compared: an IMU based controller, and a myoelectric controller to compensate for joint torque exerted by the wearer. The results of preliminary experiments showed a substantial reduction in muscular effort with the exosuit's assistance, with the myoelectric control scheme exhibiting reduced operational delay. In parallel, the neuromusculoskeletal modeling and simulator (NMMS) has been widely applied in various fields. Most of the research works implements the PD-based internal model of human’s central nervous system to simulate the generated muscle activation. However, the PD-based internal models in recent works are tuned by the empirical data which requires empirical data from human subject experiments. In this dissertation, an off-policy DRL algorithm, Deep Deterministic Policy Gradient was implemented to tune the PD-based internal model of human’s central nervous system. Compared to the conventional approaches, the DRL-based auto-tuner can learn the optimal policy through trial-and-error which doesn’t require human subject experiment and empirical data. The experiment this work showed promising results of this DRL-based auto-tuner for internal-model of human’s central nervous system.

During walking, arm swing helps maintain postural balance and stability, but it does not aid in body propulsion. Human bipedal locomotion makes the upper limbs passively swing without engaging much upper limb muscle force or effort. The central idea of this thesis is to capture the kinetic energy of the arm swing during walking and transfer it to the lower limbs via a wearable exosuit to reduce the lower limb muscle efforts during walking. The Arm-Leg Interactive Coupling Exosuit (ALICE) is designed with cable-pulley system to harness shoulder and elbow movements to support the hips and ankles during walking. ALICE employs two coupling methods, shoulder-hip coupling and elbow-ankle coupling. The shoulder is coupled with the hip contralaterally, while the elbow is coupled with the ankle ipsilaterally to match the natural walking pattern. Therefore, shoulder flexion results in hip flexion, and elbow flexion initiates plantarflexion of the ankle during toe-off. The proposed concept was validated through human subject experiments involving 15 healthy young adults who walked on a treadmill for 5 minutes with and without the device. Walking kinematics, muscle activity, foot pressure, and metabolic cost were recorded to compare differences in walking biomechanics and energetics between three conditions - BL, S1 (device worn but disengaged) and S2 (device worn and engaged). A repeated measures analysis of variance (ANOVA) followed by post hoc analysis was used to identify the effects of shoulder-hip coupling, elbow-ankle coupling, and their combined effects. The results indicate that the proposed concepts indeed generate the expected outcomes of reducing lower limb muscle activity in exchange for the increased effort of upper limb muscles.

Internet of Things is expanding to the areas such as healthcare, home management, industrial, agriculture, and becoming pervasive in our life, resulting in improved efficiency, accuracy and economic benefits. Smart sensors with embedded interfacing integrated circuits (ICs) are important enablers, hence, variety of smart sensors are required. However, each type of sensor requires specific interfacing chips, which divides the huge market of sensors’ interface chips into lots of niche markets, resulting in high develop cost and long time-to-market period for each type. Software defined sensor is regarded as a promising solution, which is expected to use a flexible interface platform to cover different sensors, deliver specificity through software programming, and integrate easily into the Internet of Things. In this work, research is carried out on the design and implementations of ultra low power analog interface circuits toward software defined sensors for healthcare services based on Internet of Things.    This thesis first explores architectures and circuit techniques for energy-efficient and flexible analog to digital conversion. A time-spreading digital calibration, to calibrate the errors due to finite gain and capacitor mismatch in multi-bit/stage pipelined converters, is developed with short convergence time. The effectiveness of the proposed technique is demonstrated with intensive simulations. Two novel circuit level techniques, which can be combined with digital calibration techniques to further improve the energy efficiency of the converters, are also presented. One is the Common-Mode-Sensing-and-Input-Interchanging (CSII) operational-transconductance-amplifier (OTA) sharing technique to enable eliminating potential memory effects. The other is a workload-balanced multiplying digital-to-analog converter (MDAC) architecture to improve the settling efficiency of a high linear multi-bit stage. Two prototype converters have been designed and fabricated in 0.13 μm CMOS technology. The first one is a 14 bit 50 MS/s digital calibrated pipelined analog to digital converter that employs the workload-balanced MDAC architecture and time-spreading digital calibration technique to achieve improved power-linearity tradeoff. The second one is a 1.2 V 12 bit 5~45 MS/s speed and power-scalable ADC incorporating the CSII OTA-sharing technique, sample-and-hold-amplifier-free topology and adjustable current bias of the building blocks to minimize the power consumption. The detailed measurement results of both converters are reported and deliver the experimental verification of the proposed techniques.     Secondly, this research investigates ultra-low-power analog front-end circuits providing programmability and being suitable for different types of sensors. A pulse-width- -modulation-based architecture with a folded reference is proposed and proven in a 0.18 μm technology to achieve high sensitivity and enlarged dynamic range when sensing the weak current signals. A 8-channel bio-electric sensing front-end, fabricated in a 0.35 μm CMOS technology is also presented that achieves an input impedance of 1 GΩ, input referred noise of 0.97 Vrms and common mode rejection ratio of 114 dB. With the programmable gain and cut-off frequency, the front-end can be configured to monitor for long-term a variety of bio-electric signals, such as electrooculogram (EOG), electromyogram (EMG), electroencephalogram (EEG) and electrocardiogram (ECG) signals. The proposed front-end is integrated with dry electrodes, a microprocessor and wireless link to build a battery powered E-patch for long-term and continuous monitoring. In-vivo test results with dry electrodes in the field trials of sitting, standing, walking and running slowly, show that the quality of ECG signal sensed by the E-patch satisfies the requirements for preventive cardiac care.    Finally, a wireless multimodal bio-electric sensor system is presented. Enabled by a customized flexible mixed-signal system on chip (SoC), this bio-electric sensor system is able to be configured for ECG/EMG/EEG recording, bio-impedance sensing, weak current stimulation, and other promising functions with biofeedback. The customized SoC, fabricated in a 0.18 μm CMOS technology, integrates a tunable analog front-end, a 10 bit ADC, a 14 bit sigma-delta digital to current converter, a 12 bit digital to voltage converter, a digital accelerator for wavelet transformation and data compression, and a serial communication protocol. Measurement results indicate that the SoC could support the versatile bio-electric sensor to operate in various applications according to specific requirements. / <p>QC 20151221</p>

Recent advances in camera technology have made it possible to build a comfortable, wearable system which can capture the scene in front of the user throughout the day. Products based on this technology, such as GoPro and Google Glass, have generated substantial interest. In this thesis, I present my work on egocentric vision, which leverages wearable camera technology and provides a new line of attack on classical computer vision problems such as object categorization and activity recognition. The dominant paradigm for object and activity recognition over the last decade has been based on using the web. In this paradigm, in order to learn a model for an object category like coffee jar, various images of that object type are fetched from the web (e.g. through Google image search), features are extracted and then classifiers are learned. This paradigm has led to great advances in the field and has produced state-of-the-art results for object recognition. However, it has two main shortcomings: a) objects on the web appear in isolation and they miss the context of daily usage; and b) web data does not represent what we see every day. In this thesis, I demonstrate that egocentric vision can address these limitations as an alternative paradigm. I will demonstrate that contextual cues and the actions of a user can be exploited in an egocentric vision system to learn models of objects under very weak supervision. In addition, I will show that measurements of a subject's gaze during object manipulation tasks can provide novel feature representations to support activity recognition. Moving beyond surface-level categorization, I will showcase a method for automatically discovering object state changes during actions, and an approach to building descriptive models of social interactions between groups of individuals. These new capabilities for egocentric video analysis will enable new applications in life logging, elder care, human-robot interaction, developmental screening, augmented reality and social media.

How can vibrotactile stimuli be used to create a technology-mediated somatic learning experience? This question motivates this practice-based research, which explores how the Feldenkrais Method and cognate neuroscience research can be applied to technology design. Supported by somaesthetic philosophy, soma-based design theories, and a critical acknowledgement of the socially-inflected body, the research develops a systematic method grounded in first- and third-person accounts of embodied experience to inform the creation and evaluation of design of Haplós, a wearable, user-customisable, remote-controlled technology that plays methodically composed vibrotactile patterns on the skin in order to facilitate body awareness—the major outcome of this research and a significant contribution to soma-based creative work. The research also contributes to design theory and somatic practice by developing the notion of a somatic learning affordance, which emerged during course of the research and which describes the capacity of a material object to facilitate somatic learning. Two interdisciplinary collaborations involving Haplós contribute to additional fields and disciplines. In partnership with experimental psychologists, Haplós was used in a randomised controlled study that contributes to cognitive psychology by showing that vibrotactile compositions can reduce, with statistical significance, intrusive food-related thoughts. Haplós was also used in Bisensorial, an award-winning, collaboratively developed proof-of-concept of a neuroadaptive vibroacoustic therapeutic device that uses music and vibrotactile stimuli to induce desired mental states. Finally, this research contributes to cognitive science and embodied philosophy by advancing a neuroscientific understanding of vibrotactile somaesthetics, a novel extension of somaesthetic philosophy.

Industrie 4.0 und aktuelle Entwicklungen in dem Bereich der produzierenden Unternehmen erfordern hohe Anpassungsleistungen von Menschen und von Maschinen gleichermaßen. In Smart Factories werden Produktionsmitarbeiter zu Wissensarbeitern. Dazu bedarf es neben neuen, intelligenten, technischen Lösungen auch neuer Ansätze für Arbeitsorganisation, Trainings- und Qualifizierungskonzepte, die mit adaptierbaren technischen Systemen flexibel zusammenarbeiten. Das durch die EU geförderte Projekt Factory2Fit entwickelt Lösungen für die Mensch-Technik-Interaktion in automatisierten Produktionssystemen, welche eine hohe Anpassungsfähigkeit an die Fähigkeiten, Kompetenzen und Präferenzen der individuellen Mitarbeiter bieten und damit gleichzeitig den Herausforderungen einer höchst kundenindividuellen Produktion gewachsen sind. Im vorliegenden Beitrag werden die grundlegenden Ziele und Ideen des Projektes vorgestellt sowie die Ansätze des Quantified-self im Arbeitskontext, die adaptive Automatisierung inklusive der verschiedenen Level der Automation sowie die spezifische Anwendung des partizipatorischen Designs näher beleuchtet. In den nächsten Arbeitsschritten innerhalb des Projektes gilt es nun, diese Konzepte um- und einzusetzen sowie zu validieren. Die interdisziplinäre Arbeitsweise sowie der enge Kontakt zwischen Wissenschafts-, Entwicklungs- und Anwendungspartnern sollten dazu beitragen, den Herausforderungen bei der Realisierung erfolgreich zu begegnen und zukunftsträchtige Smart Factory-Lösungen zu implementieren. Das Projekt Factory2Fit wird im Rahmen von Horizon 2020, dem EU Rahmenprogramm für Forschung und Innovation (H2020/2014-2020), mit dem Förderkennzeichen 723277 gefördert.

En el Perú, según estudios realizados en el 2010, el 42% de los pacientes hipertensos son tratados, pero solo el 14% de los pacientes logran ser controlados. Esto se debe a que el proceso actual de control de la hipertensión no es completamente eficiente debido a que el paciente no se adhiere completamente al tratamiento y que los controles de la tensión arterial resultan ser muy puntuales tras periodos de tiempo largos de los cuales no se tiene información confiable relacionada con el progreso del paciente. Se plantea un sistema para el control y monitoreo de alteraciones hipertensivas en el embarazo con el uso de sensores biomédicos no invasivos. De esta manera aseguramos que la medición continua brinde la información precisa y confiable para que las mujeres gestantes puedan detectar a tiempo alguna alteración hipertensiva. Además, en segunda instancia, el sistema alerta a los familiares y al médico encargado sobre los niveles de presión arterial en caso de emergencia. El aporte del proyecto es reducir el aumento en la prevalencia de las enfermedades crónicas mediante la integración de los servicios de salud con la tecnología, y gestionar la información desde la colección de datos a través del wearable hasta la exposición. En base a las pruebas realizadas con pacientes gestantes, se obtiene que el 38.64% son controladas y monitoreadas el 75% del tiempo. Estos resultados indican que el uso de la tecnología puede influenciar positivamente en la reducción de la hipertensión en general o en enfermedades crónicas similares. / In Peru, according to studies conducted in 2010, 42% of hypertensive patients are treated, but only 14% of patients manage to be controlled. This is due to the fact that the current process of hypertension control is not completely efficient because the patient does not completely adhere to the treatment and that blood pressure controls turn out to be very punctual after long periods of time from which there is no reliable information related to the progress of the patient. A system is proposed for the control and monitoring of hypertensive disorders in pregnancy with the use of non-invasive biomedical sensors. In this way we ensure that continuous measurement provides accurate and reliable information so that pregnant women can detect any hypertensive disorder on time. In addition, the system alerts the family members and the doctor in charge about the blood pressure levels in case of emergency. The contribution of the project is to reduce the increase in the prevalence of chronic diseases by integrating health services with technology, and to manage information from data collection through wearable until data exposure. Based on the tests carried out with pregnant patients, 38.64% are controlled and monitored 75% of the time. These results indicate that the use of technology can positively influence the reduction of hypertension in general or in similar chronic diseases. / Tesis