Energy harvesting for wearable applications

Energy harvesting, the process of collecting low level ambient energy and converting it into electrical energy, is a promising approach to power wearable devices. By converting the energy of the human body by using piezoelectric and thermoelectric principles, the need for batteries and charging can be avoided, and the autonomy of wearable devices can be significantly increased. Due to the inherent random nature of human motion, however, the energy harvesting devices need to be specifically designed in order to ensure their optimal operation and sufficient power generation. Using several combined approaches, a new class of autonomous devices, suitable for telemedicine, patient monitoring or IoT applications, can be developed

Masking Kernel for Learning Energy-Efficient Representations for Speaker Recognition and Mobile Health

Modern smartphones possess hardware for audio acquisition and to perform speech processing tasks such as speaker recognition and health assessment. However, energy consumption remains a concern, especially for resource-intensive DNNs. Prior work has improved the DNN energy efficiency by utilizing a compact model or reducing the dimensions of speech features. Both approaches reduced energy consumption during DNN inference but not during speech acquisition. This paper proposes using a masking kernel integrated into gradient descent during DNN training to learn the most energy-efficient speech length and sampling rate for windowing, a common step for sample construction. To determine the most energy-optimal parameters, a masking function with non-zero derivatives was combined with a low-pass filter. The proposed approach minimizes the energy consumption of both data collection and inference by 57%, and is competitive with speaker recognition and traumatic brain injury detection baselines

High-Performance Accelerometer Based On Asymmetric Gapped Cantilevers For Physiological Acoustic Sensing

Continuous or mobile monitoring of physiological sounds is expected to play important role in the emerging mobile healthcare field. Because of the miniature size, low cost, and easy installation, accelerometer is an excellent choice for continuous physiological acoustic signal monitoring. However, in order to capture the detailed information in the physiological signals for clinical diagnostic purpose, there are more demanding requirements on the sensitivity/noise performance of accelerometers. In this thesis, a unique piezoelectric accelerometer based on the asymmetric gapped cantilever which exhibits significantly improved sensitivity is extensively studied. A meso-scale prototype is developed for capturing the high quality cardio and respiratory sounds on healthy people as well as on heart failure patients. A cascaded gapped cantilever based accelerometer is also explored for low frequency vibration sensing applications such as ballistocardiogram monitoring. Finally, to address the power issues of wireless sensors such as wireless wearable health monitors, a wide band vibration energy harvester based on a folded gapped cantilever is developed and demonstrated on a ceiling air condition unit

Long-term monitoring of respiratory metrics using wearable devices

Recently, there has been an increased interest in monitoring health using wearable sensors technologies however, few have focused on breathing. The utility of constant monitoring of breathing is currently not well understood, both for general health as well as respiratory conditions such as asthma and chronic obstructive pulmonary disease (COPD) that have significant prevalence in society. Having a wearable device that could measure respiratory metrics continuously and non-invasively with high adherence would allow us to investigate the significance of ambulatory breathing monitoring in health and disease management. The purpose of this thesis was to determine if it was feasible to continuously monitor respiratory metrics. To do this, we identified pulse oximetry to provide the best balance between use of mature signal processing methods, commercial availability, power efficiency, monitoring site and perceived wearability. Through a survey, it was found users would monitor their breathing, irrespective of their health status using a smart watch. Then it was found that reducing the duty cycle and power consumption adversely affected the reliability to capture accurate respiratory rate measurements through pulse oximetry. To account for the decreased accuracy of PPG derived respiratory rate at higher rates, a long short-term memory (LSTM) network and a U-Net were proposed, characterised and implemented. In addition to respiratory rate, inspiration time, expiration time, inter-breath intervals and the Inspiration:Expiration ratio were also predicted. Finally, the accuracy of these predictions was validated using pilot data from 11 healthy participants and 11 asthma participants. While percentage bias was low, the 95\% limits of agreement was high. While there is likely going to be enthusiastic uptake in wearable device use, it remains unseen whether clinical utility can be achieved, in particular the ability to forecast respiratory status. Further, the issues of sensor noise and algorithm performance during activity was not calculated. However, this body of work has investigated and developed the use of pulse oximetry, classical signal processing and machine learning methodologies to extract respiratory metrics to lay a foundation for both the hardware and software requirements in future clinical research

Ergonomic interventions: comparisons between footrest and anti-fatigue mat in reducing lower leg muscle discomforts during prolonged standing

Ergonomics is a science focusing on the study of human fit, decreasing human fatigue and discomfort through the design of new product. Prevention related to workers injury and illness such as muscle discomfort is part of the main goals in ergonomics interventions. Thus, this present study investigates the effectiveness of ergonomic interventions such as footrest and floor conditions in reducing workers lower leg muscle discomforts during prolonged standing. The main objective of this study was to determine and compare the lower leg muscles discomfort (exertion percentage (%)) of Gastrocnemius and Tibialis Anterior among the respondents using the two ergonomic interventions (footrest and anti-fatigue mat). About 60 healthy subjects were recruited to stand for 2 hours (120 minutes) while using the two interventions in different session with one week interval between each test session. During standing, lower leg muscle discomfort of Gastrocnemius and Tibialis Anterior muscles were continuously monitored. Changes in lower leg muscle discomforts over standing time were measured using the surface Electromyography (sEMG). In this study, the sEMG readings showed that the percentage of exertion (%) were increasing with time (within 120 minutes) on muscles for both legs with the usage of the interventions (footrest and anti-fatigue mat). However, the percentage of exertion (%) from the sEMG readings were lower compared to previous studies. The independent t-test was used to find the mean changes on exertion percentage (%) between each muscles of both legs for the two interventions. Results found that there were significant exertion percentage at certain time with 15 minutes time period within the 120 minutes standing. This study showed that the ergonomic interventions (anti-fatigue mat and footrest) gives a low number of exertion percentage (%), showing a reduced muscle discomfort to the lower leg muscles compared to previous studies and interventions. In comparisons with footrest, this study showed that anti-fatigue mat is more applicable for the assembly workers in the industrial factory. The data produced by the comparisons between the two interventions can be useful especially to the Department of Occupational Safety and Health Malaysia (DOSH) in enhancing the safety and wellbeing of industrial workers in Malaysia

Nutritional Habits and Interventions in Childhood

The objective of this book is to present nutritional and educational interventions for children and their families. The creation of healthy preferences is a key determinant of food choices and therefore diet quality. Food choices have important implications for health, particularly for food-related diseases, such as feeding difficulties and the development of non-communicable diseases. The first years of a child's life are fundamental for the creation of tastes, eating habits and the relationship with food. Preferences for certain foods are neither innate nor unchangeable. Eating behaviour is the result of experience and learning, and, through the repeated offering of food by parents, especially those less accepted, it is possible to promote good nutrition. Behaviour depends on the interaction of environmental factors, genetics, sex, and age. The environment in which the child is immersed, and which influences them, includes family, other children, society, media and the supply of food. Achieving an adequate intake of macro and micro-nutrients is an important objective for all ages of life and, particularly, for those of pediatric age, since it is crucial for cognitive development. Nutrition has also a therapeutic effect. Nutritional interventions tailored to specific pathologies are needed to prevent nutritional deficiencies and maintain an adequate nutritional status, since children and adolescents with chronic or inflammatory diseases are particularly vulnerable and at major risk of developing malnutrition

Personalised Environmental Monitoring of Building Occupants: Integration of Scalable Technologies

Urbanised societies spend most of their time indoor. These are places to conduct habitual activities that impact across the life course and are generating discussions on the built environment and its interplay with health and wellbeing. To understand the effect buildings and their enclosed spaces have on people/occupants, there is a need to monitor Indoor Environmental Quality (IEQ) and occupant responses. State-of-the-art monitoring approaches exist, but they have limited utility outside of bespoke scenarios due to their limited pragmatism and large cost. Other emergent technologies exist but questions remain relating to e.g., validity. Other routine/traditional subjective approaches for evaluating building IEQ often negate to account for the experiences of individual occupants, adding to complications. This thesis explores current monitoring IEQ trends, uncovering the needs to make the individual the unit of analysis. Research undertaken explores contemporary needs and shifting trends to pragmatic approaches, localised sensors to provide richer data that could enable a better understanding of environmental and occupant changes. Quantitative measurement of the environmental conditions local to individuals are explored to understand whether spatial density in monitoring can 1) reinforce data pertaining to how building occupants experience indoor conditions and 2) provide additional context to current approaches for data capture, which traditionally focus on qualitative approaches. Through a series of original research this thesis broadly presents the design and development of a multi-modal IEQ monitoring device and a supporting methodological process for monitoring individuals. It identifies that low-cost multi-modal monitoring deployed longitudinally can add significant context to traditional qualitative approaches, with the individual as the unit of analysis. Findings from the thesis present a paradigm shift that could have practical implications for researchers and practitioners, changing the way building performance is assessed and the way its impact on health and wellbeing could be evaluated

HAPPEN: The Health and Attainment of Pupils in a Primary Education Network

A complex relationship exists between health and education, with evidence demonstrating the importance of childhood health and wellbeing on academic outcomes. However, prioritising health and wellbeing within the school setting has been a challenge due to curriculum pressures and a lack of collaboration. To address these shortfalls, a primary school network, HAPPEN (Health and Attainment of Pupils in a Primary Education Network) was established. The overarching aim of this thesis is to develop HAPPEN, a network combining multidisciplinary expertise through a unified system of education, health and research specialists, using an action research model. This thesis examines whether HAPPEN can act as a platform to evaluate interventions in the school setting and disseminate evidence-based learning. This is presented through published research in Study 1; a qualitative analysis of curriculum-based outdoor learning and Study 2; a mixed-methods evaluation of The Daily Mile. This thesis also examines if HAPPEN can be used for observational epidemiology by identifying the factors associated with educational attainment. Study 3 presents the association between social, lifestyle and epidemiological factors with attainment at age 10-11 using linked health, educational and survey data. The final chapter presents a critical reflection of the development, scalability and sustainability of HAPPEN. Following an annual process of observation, reflection, planning and implementation, HAPPEN has expanded to a national primary school network and knowledge exchange infrastructure for schools and health professionals in Wales. The research through HAPPEN has demonstrated local, national and international impact and demonstrates the important contribution this thesis provides to the understanding of health and education. In conclusion, HAPPEN fills an important gap in the provision of a synergistic health and education tool for primary schools

Robust Audio and WiFi Sensing via Domain Adaptation and Knowledge Sharing From External Domains

Recent advancements in machine learning have initiated a revolution in embedded sensing and inference systems. Acoustic and WiFi-based sensing and inference systems have enabled a wide variety of applications ranging from home activity detection to health vitals monitoring. While many existing solutions paved the way for acoustic event recognition and WiFi-based activity detection, the diverse characteristics in sensors, systems, and environments used for data capture cause a shift in the distribution of data and thus results in sub-optimal classification performance when the sensor and environment discrepancy occurs between training and inference stage. Moreover, large-scale acoustic and WiFi data collection is non-trivial and cumbersome. Therefore, current acoustic and WiFi-based sensing systems suffer when there is a lack of labeled samples as they only rely on the provided training data. In this thesis, we aim to address the performance loss of machine learning-based classifiers for acoustic and WiFi-based sensing systems due to sensor and environment heterogeneity and lack of labeled examples. We show that discovering latent domains (sensor type, environment, etc.) and removing domain bias from machine learning classifiers make acoustic and WiFi-based sensing robust and generalized. We also propose a few-shot domain adaptation method that requires only one labeled sample for a new domain that relieves the users and developers from the painstaking task of data collection at each new domain. Furthermore, to address the lack of labeled examples, we propose to exploit the information or learned knowledge from sources where available data already exists in volumes, such as textual descriptions and visual domain. We implemented our algorithms in mobile and embedded platforms and collected data from participants to evaluate our proposed algorithms and frameworks in an extensive manner.Doctor of Philosoph