The ability to point using a mouse or a touch screen is an indispensible part of modern humancomputer interactions. However, people with motor impairment need assistive technologies. Existing technologies based on voice command, eye tracker, head tracker, tongue tracking, muscletension tracking tend to be complex or expensive. We propose a new system that uses head position

to control the cursor position using a very simple, low-complexity infrared-based mechanism. It

uses two infrared (IR) LEDs to measure the distance between the chin and the sensor deck. Simple

algorithms convert measured data to X and Y coordinate on the screen. The Device sends either

raw or processed data to host system through Bluetooth Low Energy (BLE) technology. Experimental results show the proposed head mouse to be easy and intuitive to use while consuming low

power and is easy to build

The Internet of Things (IoT) has been receiving growing attention in recent years as the next wave of computing revolution made possible by all types of networks of things (NoTs), where devices powered with low-cost, miniature low-power systems-on-chip (SoC) with computing and communication capabilities, and are bridged to the Internet with the assistance of gateways. More and more NoT device are designed to provide more than one functionalities to fulfill different requirements from the application domains. We believe that the true power of IoT is that functionalities of devices can work across application domains. In order to reveal the potential of IoT, the description of a device's capability needs to represent the functionalities that the device can provide. We discover the previous solutions on describing a device's capability focus mainly on hiding the vendor-specific interfaces made by different manufacturers, but they do not reflect different functionalities that a device provides.

In this thesis, the concept of device capability profile is proposed. Different from the previous solutions, the device capability profile specified in the firmware of a device allows the device to work across different application domains. Together with device capability profile, a centralized IoT middleware framework, called rimware, is proposed. Rimware tracks every device's capability and state in a centralized manner and provides different ways for application domains to query against the device's functionalities. In addition, rimware utilizes the device capability profile to carry out the enforcement of the security and privacy throughout the communication with the devices. Moreover, tasks can be scheduled through the rimware which enables functionalities from multiple devices to work together to fulfill the requirements from application domains. Optimization is applied on cases that one device working for multiple task simultaneously. An implementation of rimware that is specifically designed for BLE devices, called BlueRim, which takes advantages of BLE's very long battery life on the device side and the cloud functionality on the centralized side is provided. The fundamental features of rimware have been validated in several real-world applications from different different domains while incurring minimal code size and communication overhead on BLE devices. We believe that our approach represents an important technology in taking IoT closer to realizing the full potentials.

Medical sensing system collect and analyze the patients’ physiological data for monitoring, aid or diagnostic purposes. System designers are faced with stringent requirements on not only correctness and safety but also power. Reference designs and multi-purpose platforms help to significantly shorten the development cycle.

This work takes a cross-layer, system-level, platform-based approach to addressing the problem of saving power in a class of portable medical system. We propose a low-power medical sensing system that can be used to monitor Electrocardiography (ECG), Photoplethysmogram (PPG), and muscle tension. It also includes a hand gesture recognition system to aid mobility-impaired patients.

We explore the theory and application of a compressive sensing framework to medical signal processing. A novel compressive sensing-based ECG compression algorithm and a dominant frequency extraction-based PPG heart-rate calculation algorithm are proposed to reduce the system power. The unique combination of hardware structure and software signal-processing algorithms makes low-power design possible. The system test results show that the proposed system is superior to existing works in terms of power consumption and system size.

Wearable embedded systems with sensing, communication, and computing capabilities have given rise to innovations in e-health and telemedicine in general. The scope of such systems ranges from devices and mobile apps to cloud backend and analysis algorithms, all of which must be well integrated. To manage the development, operation, and evolution of such complex systems, a framework systematic framework is needed. This dissertation makes contributions in two parts. First is a framework for defining the structure of a wide range of wearable medical applications with modern cloud support. The second part includes several algorithms that can be plugged into this framework for making these systems more efficient in terms of processing performance and data size. We propose a novel QT analysis algorithm that can take advantage of GPU as well as in a server-client environment, and we show competitive results in terms of both performance and energy consumption with or without parallelization. We also propose ECG compression techniques using trained overcomplete dictionary. After constructing the dictionary through learning process with a given dataset, the signal can be compressed by sparse estimation using the trained dictionary. We propose reconstructing ECG signal from undersampled data based on compressive sensing framework that can reconstruct the ECG signals precisely from fewer samples so long as the signal is sparse or compressible. Together, these algorithms operating in the context of our proposed framework validate the effectiveness of our structured approach to the framework for wearable medical applications.

The ubiquitous IoT devices have promoted the growth of applications that require target positioning. While precise positioning remains a challenge for IoT devices, existing methods mainly focus on optimizing the positioning algorithms or expanding the sensing modalities. One opportunity area of improvement is to leverage their wireless communication capability to exchange sensing data of different modalities with nearby devices. This work proposes collaborative sensing frameworks to enhance the accuracy of position inference. Our approach to collaborative sensing is divided into selection of reliable devices, the data-exchange mechanism, and the data-fusion algorithm. We demonstrate our approach on two different position-sensing systems: one for chest compression-depth estimation and one for indoor pedestrian localization. The key components used in the frameworks include quality assessment of sensor data using mutual information, data-fusion algorithms based on the chest-compression model and the pedestrian-encounter model, and the data-exchange mechanism using Bluetooth Low Energy (BLE). A real-time position-estimation method based on our chest-compression model is proposed to remove the noise and handle the cumulative error. A collaborative conditional random field algorithm is developed to reduce the convergence distance in the localization estimation. Experimental results show our collaborative-sensing approach achieves higher rate of convergence than the existing solutions in terms of real-time estimated position. This dissertation proposes an alternative approach to optimizing the position-sensing systems through collaborative sensing. Our work represents the first step towards the universal positioning on IoT devices and provides research opportunities for further improvement and exploration.

This thesis presents a family of computational storage drives (CSD) and demonstrates their performance and power improvements due to in-storage processing (ISP) when running big-data analytics applications. CSDs are an emerging class of solid-state drives (SSD) that are capable of running user code while minimizing data-transfer time and energy. Applications that can benefit from in-situ processing include distributed training, distributed inferencing, and databases. To achieve the full advantage of the proposed ISP architecture, we propose software solutions for workload balancing before and at runtime for training and inferencing applications. Other applications such as sharding-based databases can readily take advantage of our ISP structure without additional tooling. Experimental results on different capacity and form factors of CSDs show up to 3.1x speedup in processing while reducing the energy consumption and data transfer by up to 67% and 68%, respectively, compared to regular enterprise SSDs.

This thesis presents a new open-source framework for plotting and managing streams of sensor data on iOS devices. The framework, called the Sensor Plot Kit (SPK), aims to help developers build apps for viewing sensor data that can be streamed in real-time or pre-recorded. It can handle multiple streams at relatively high data rates with short latencies, making it suitable for advanced medical applications such as electrocardiograms (ECG). This framework also supports filtering, post processing, and saving the data in non-volatile storage. A novel feature of our design is that we adopt the Model View View-Model (MVVM) design pattern that reduces complexity and maximizes code reusability compared to the standard Model View Controller (MVC) pattern in iOS while remaining compatible. Experimental results using a real-world ECG, on-board sensors, and pre-recorded results over actual and simulated iOS devices confirm that SPK enables developers to build feature-rich, robust apps that embed real-time, responsive plotting capability while significantly shortening development time. This work has the potential to make a crosscutting impact on science, engineering, medicine, the financial market, and many other fields that increasingly rely on smart phones and tablets as the primary device for viewing and interacting live and historical streams of data.

While Internet-Of-Things (IoT) has drawn more attention to researchers and the public, to build a complete system from the edge sensing units to the cloud services requires massive amount of efforts. Researchers with strong interests in collected information are often lost in various technologies, including distributed sensing embedded systems, bridge devices between Internet and local network, and data backend services.

This work takes a cross-system, script-based, and semantic-enhanced approach to address the problem of lacking suitable runtime supports. We proposed a threaded code runtime support for edge sensing systems, a script based wrapper on Physical-to-Cyber bridges, and scalable middleware into the backend services.

With proposed runtime supports, we are able to apply distributed sensing systems into real world applications quickly and explorer insights from collected information. As a result, a building structure monitoring system is installed and allow civil researchers to develop algorithms to prevent disaster events. Body area sensing systems such as ECG monitoring, CO2 detection, and body movement are developed. This enables baby screening and detect potential heart problems. The results have shown that with proposed runtime supports applications can be realized quickly and scalable.