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Microsleeps and other lapses of responsiveness can have severe, or even fatal, consequences for people who must maintain high levels of attention on monotonous tasks for long periods of time, e.g., commercial vehicle drivers, pilots, and air-traffic controllers. This thesis describes a head-mounted system which is the first prototype in the process of creating a system that can detect (and possibly predict) these lapses in real time. The system consists of a wearable device which captures multiple physiological signals from the wearer and an extensible software framework for imple- menting signal processing algorithms. Proof-of-concept algorithms are implemented and used to demonstrate that the system can detect simulated microsleeps in real time. The device has three sensing modalities in order to get a better estimate of the user＇s cognitive state than by any one alone. Firstly, it has 16 channels of EEG (8 currently in use) captured by 24-bit ADCs sampling at 250 Hz. The EEG is acquired by custom-built dry electrodes consisting of spring-loaded, gold-plated pins. Secondly, the device has a miniature video camera mounted below one eye, providing 320 x 240 px greyscale video of the eye at 60 fps. The camera module includes infrared illumination so that it can operate in the dark. Thirdly, the device has a six-axis IMU to measure the orientation and movement of the head. These sensors are connected to a Gumstix computer-on-module which transmits the captured data to a remote computer via Wi-Fi. The device has a battery life of about 7.4 h. In addition to this hardware, software to receive and analyse data from the head-mounted device was developed. The software is built around a signal processing pipeline that has been designed to encapsulate a wide variety of signal processing algorithms; feature extractors calculate salient properties of the input data and a classifier fuses these features to determine the user＇s cognitive state. A plug-in system is provided which allows users to write their own signal processing algorithms and to experiment with different combinations of feature extractors and classifiers. Because of this flexible modular design, the system could also be used for applications other than lapse detection‒any application which monitors EEG, eye video, and head movement can be implemented by writing appropriate signal processing plug-ins, e.g., augmented cognition or passive BCIs. The software also provides the ability to configure the device＇s hardware, to save data to disk, and to monitor the system in real time. Plug-ins can be implemented in C++ or Python. A series of validation tests were carried out to confirm that the system operates as intended. Most of the measured parameters were within the expected ranges: EEG amplifier noise = 0.14 μVRMS input-referred, EEG pass band = DC to 47 Hz, camera focus = 2.4 lp/mm at 40 mm, and total latency < 100 ms. Some parameters were worse than expected but still sufficient for effective operation: EEG amplifier CMRR ≥ 82 dB, EEG cross-talk = -17.4 dB, and IMU sampling rate = 10 Hz. The contact impedance of the dry electrodes, measured to be several hundred kilohms, was too high to obtain clean EEG. Three small-scale experiments were done to test the performance of the device in operation on people. The first two demonstrated that the pupil localization algorithm produces PERCLOS values close to those from a manually-rated gold standard and is robust to changes in ambient light levels, iris colour, and the presence of glasses. The final experiment demonstrated that the system is capable of capturing all three physiological signals, transmitting them to the remote computer in real time, extracting features from each signal, and classifying simulated microsleeps from the extracted features. However, this test was successful only when using conventional wet EEG electrodes instead of the dry electrodes built into the device; it will be necessary to find replacement dry electrodes for the device to be useful. The device and associated software form a platform which other researchers can use to develop algorithms for lapse detection. This platform provides data capture hardware and abstracts away the low-level software details so that other researchers are free to focus solely on developing signal processing techniques. In this way, we hope to enable progress towards a practical real-time, real-world lapse detection system.

Residual gait abnormalities are common following lower limb injury and/or stroke and can have several negative impacts on an individual’s life. Without continuous treatment and follow up, individuals can be prone to chronic pain as abnormal gait may lead to non-physiological loading of the musculoskeletal system. The current industry gold standard for diagnosing abnormal gait requires specialty equipment that is generally only available at designated gait facilities. Due to the inaccessibility and high cost associated with these facilities, a wearable SmartSole device to continuously detect abnormal gait was proposed. A previous iteration of the SmartSole was unable to properly detect abnormal gait and also experienced fracturing throughout the 3D printed body. In this present study, sensor placement and material selection were reconsidered to address these limitations. The objective of this study was to determine if a redesigned SmartSole could identify events of abnormal gait through validation and verification testing against the industry standard force plates. In total, 14 participants were selected for gait studies, 7 with pronounced gait abnormalities (e.g. limps), and 7 with physiological gait. Parameters of interest included stance time, gait cycle time, and the ratio of the force magnitudes recorded during heel strike and toe off. Results indicated that the SmartSole was effective at determining overall event timings within the gait cycle, as both stance and cycle time had strong, positive correlations (left stance: r = 0.761, right stance: r = 0.560, left cycle: r = 0.688) with the force plates, with the exception of right foot cycle time. The sole was not effective at measuring actual values of events during gait as there were weak correlations with the force plates. Furthermore, when comparing parameters of interest between the injured and non-injured sides for test participants with gait abnormalities, neither the SmartSole nor the force plates were able to detect significant differences. The inability of the sole to accurately collect force magnitudes or to detect abnormal gait leads to the conclusion that additional sensors may need to be implemented. Future iterations may consider placement of additional sensors to allow for a “fuller picture” and the inclusion of other types of sensors for improved, continuous tracking of gait abnormalities.

Human-computer interaction (HCI) is an important issue in the area of medicine, for example, the operation of surgical simulators, virtual rehabilitation systems, telemedicine treatments, and so on. In this thesis, the human-computer interaction of a self-operated mobile telemedicine device is designed. The mobile telemedicine device (i.e. intelligent Medication Box or iMedBox) is used for remotely monitoring patient health and activity information such as ECG (electrocardiogram) signals, home medication, patient movements, etc., through a wearable bio-patch and a touch screen on the device, thus creating interaction between patient and doctor via the internet. The telemedicine device also has a reminder function for the time of medication. Two aspects have mainly been addressed in designing the HCI. The first one is about the user interface of the telemedicine device and the second one is about the interaction between patients and the wearable device. Scenario design, user participation and interview were applied as the design methodology of this work. Literature study of relevant background information and interviews with experts were also used for scenario design. After the first version of the prototype was developed, interviews were conducted with some typical users, whose feedback and user data were collected and analysed. The thesis includes envisionment and evaluation as two parts. The study revealed that HCI is an important issue for telemedicine, particularly when it is used for elderly-care. A simple and user-friendly interface, proper physical size of devices, better and bio-compatible materials for the bio-patch etc. are to be considered important for a better HCI for telemedicine devices.

abstract: Global industrialization and urbanization have led to increased levels of air pollution. The costs to society have come in the form of environmental damage, healthcare expenses, lost productivity, and premature mortality. Measuring pollutants is an important task for identifying its sources, warning individuals about dangerous exposure levels, and providing epidemiologists with data to link pollutants with diseases. Current methods for monitoring air pollution are inadequate though. They rely on expensive, complex instrumentation at limited fixed monitoring sites that do not capture the true spatial and temporal variation. Furthermore, the fixed outdoor monitoring sites cannot warn individuals about indoor air quality or exposure to chemicals at worksites. Recent advances in manufacturing and computing technology have allowed new classes of low-cost miniature gas sensor to emerge as possible alternatives. For these to be successful however, there must be innovations in the sensors themselves that improve reliability, operation, and their stability and selectivity in real environments. Three novel gas sensor solutions are presented. The first is the development of a wearable personal exposure monitor using all commercially available components, including two metal oxide semiconductor gas sensors. The device monitors known asthma triggers: ozone, total volatile organic compounds, temperature, humidity, and activity level. Primary focus is placed on the ozone sensor, which requires special circuits, heating algorithm, and calibration to remove temperature and humidity interferences. Eight devices are tested in multiple field tests. The second is the creation of a new compact optoelectronic gas sensing platform using colorimetric microdroplets printed on the surface of a complementary-metal-oxide-semiconductor (CMOS) imager. The nonvolatile liquid microdroplets provide a homogeneous, uniform environment that is ideal for colorimetric reactions and lensless optical measurements. To demonstrate one type of possible indicating system gaseous ammonia is detected by complexation with Cu(II). The third project continues work on the CMOS imager optoelectronic platform and develops a more robust sensing system utilizing hydrophobic aerogel particles. Ammonia is detected colorimetrically by its reaction with a molecular dye, with additives and surface treatments enhancing uniformity of the printed films. Future work presented at the end describes a new biological particle sensing system using the CMOS imager. / Dissertation/Thesis / Doctoral Dissertation Materials Science and Engineering 2020

abstract: Technological advances in low power wearable electronics and energy optimization techniques make motion energy harvesting a viable energy source. However, it has not been widely adopted due to bulky energy harvester designs that are uncomfortable to wear. This work addresses this problem by analyzing the feasibility of powering low wearable power devices using piezoelectric energy generated at the human knee. We start with a novel mathematical model for estimating the power generated from human knee joint movements. This thesis’s major contribution is to analyze the feasibility of human motion energy harvesting and validating this analytical model using a commercially available piezoelectric module. To this end, we implemented an experimental setup that replicates a human knee. Then, we performed experiments at different excitation frequencies and amplitudes with two commercially available Macro Fiber Composite (MFC) modules. These experimental results are used to validate the analytical model and predict the energy harvested as a function of the number of steps taken in a day. The model estimates that 13μWcan be generated on an average while walking with a 4.8% modeling error. The obtained results show that piezoelectricity is indeed a viable approach for powering low-power wearable devices. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020

Indiana University-Purdue University Indianapolis (IUPUI) / Gait analysis plays a major role in treatment of osteoarthritis, knee or hip replacements, and musculoskeletal diseases. It is extensively used for injury rehabilitation and physical therapy for issues like Hemiplegia and Diplegia. It also provides us with the information to detect various improper gaits such as Parkinson's disease, Hemiplegic and diplegic gaits. Though there are many wearable and non-wearable methods to detect the improper gate performance, they are usually not user friendly and have restrictions. Most existing devices and systems can detect the gait but are very limited with regards of diagnosing them. The proposed method uses two A201 Force sensing resistors, accelerometer, and gyroscope to detect the gait and send diagnosed information of the possibility of the specified improper gaits via Bluetooth wireless communication system to the user's hand-held device or the desktop. The data received from the sensors was analyzed by the custom made micro-controller and is sent to the desktop or mobile device via Bluetooth module. The peak pressure values during a gait cycle were recorded and were used to indicate if the walk cycle of a person is normal or it has any abnormality. Future work: A magnetometer can be added to get more accurate results. More improper gaits can be detected by using two PCBs, one under each foot. Data can be sent to cloud and saved for future comparisons.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24608号 / 工博第5114号 / 新制||工||1978(附属図書館) / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 松野 文俊, 教授 小森 雅晴, 教授 森本 淳 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

A tool allowing the prediction of the risk of bleeding in patients with hemophilia would be relevant for patients, stakeholders, and policymakers. We performed a systematic review of the literature searching for available risk assessment models to predict the risk of bleeding in people living with hemophilia, and to determine the key risk factors that the ideal model should include. We also systematically review the literature to determine the acceptability and accuracy of wrist-wearable devices to measure physical activity in the general population. Finally, we validated the performance of a risk assessment model for the prediction of the risk for bleeding in people living with hemophilia. We identified the following risk factors for bleeding in people living with hemophilia: plasma factor levels, history of bleeds, physical activity, antithrombotic treatment, and obesity. The FitBit Charge and FitBit Charge HR are the most accurate devices for measuring steps, and the Apple Watch is the most accurate for measuring heart rate. No device proved to be accurate in measuring energy expenditure. The predictive accuracy of the risk assessment model that we validated does not endorse its use to drive decision making on treatment strategies based on the predicted number of bleeds. This might in part be explained by the methods used in the derivation phase. The need for an accurate risk assessment model to predict the risk of bleeding in people living with hemophilia is still unmet. This should be done by including the relevant risk factors identified through our work, with data on physical activity possibly collected using an accurate wrist-wearable device, and through the application of rigorous methods in the derivation and validation phases. / Thesis / Doctor of Philosophy (PhD) / People living with hemophilia lack a coagulation factor and tend to experience spontaneous bleeds, with frequency and intensity that vary between individuals. Predicting who will experience more bleeds would allow for changing the treatment strategies and directing the best resources to the persons that can benefit more. Through this project, we identified the variables that should be considered to estimate the risk for bleeding in people living with hemophilia, namely the blood levels of the lacking coagulation factor, the bleeding history, the physical activity levels, the concomitant treatment with blood thinners, and the presence of obesity. We determined that Fitbit Charge and Charge HR are the most accurate devices for measuring steps and Apple Watch for heart rate. Lastly, we found that an existing tool for predicting the risk of bleeding is not accurate enough to be used in this setting, and a new model should be produced.

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