UC Irvine

Recent advances in the Internet of Things (IoT) technologies have enabled the use of wear- ables for remote patient monitoring. Wearable sensors capture the patient’s vital signs, and provide alerts or diagnosis based on the collected data. Unfortunately, wearables typically have limited energy and bandwidth, making their use challenging for healthcare applications where monitoring must continue uninterrupted long time, without the need to charge or change the battery. Edge computing can alleviate these problems by offloading computa- tionally intensive tasks from the sensor layer to edge servers, thereby not only meeting the sensors’ limited computational capacity but also enabling the use of local closed-loop energy and bandwidth optimization algorithms to increase the battery life. By incorporating the patient’s contextual information, a desired quality of experience can be achieved by creat- ing a dynamic balance between energy-efficiency and measurement accuracy. In addition, transmitting representation of a signal instead of the raw signal can minimize the bandwidth consumption of the sensor. We first present a run-time distributed control-based solution to find the most energy-efficient system state for a given context while keeping the accuracy of decision making process over a certain threshold. We propose two approaches, myopic and Markov Decision Processes (MDP) to consider both energy constraints and risk factor requirements for achieving a two-fold goal: energy savings while satisfying accuracy require- ments of abnormality detection in a patient’s vital signs. Vital signs, including heart rate,respiration rate and oxygen saturation (SpO2), are extracted from a Photoplethysmogram (PPG) signal and errors of extracted features are compared to a ground truth that is modeled as a Gaussian distribution. We control the sensor’s sensing energy to minimize the power consumption while meeting a desired level of satisfactory detection performance. We present experimental results on realistic case studies using a reconfigurable PPG sensor in an IoT system, and show that compared to non-adaptive methods, myopic reduces an average of 16.9% in sensing energy consumption with the maximum probability of abnormality mis- detection on the order of 0.17 in a 24-hour health monitoring system. In addition, during four weeks of monitoring, we demonstrate that our MDP policy can extend the battery life on average of more than 2x while fulfilling the same average probability of misdetection com- paring to myopic method. We illustrate results comparing, myopic, MDP and non-adaptive methods to monitor 14 subjects during one month. Implementation of myopic, and Markov Decision Processes scheme are based upon implementation of optimization schemes using Matlab coding. We then, propose a framework to dynamically optimize the resolution of an electrocardiogram (ECG) signal transferred to an edge server to detect heart diseases in real-time. Our objective is two fold: maximizing the detection probability of abnormal ECG cycles while satisfying requirements on wireless channel usage. Based on sequential hypoth- esis testing and hierarchical wavelet representation, the framework establishes a control loop between the sensor and the edge server to iteratively determine the subset of wavelet coef- ficients of the ECG heart cycle based on the signal itself. Numerical results show that the sequential hypothesis testing selects different subsets for normal and abnormal ECG cycles. In addition, we show that the signal representation should be personalized, as the system selects different sets of coefficients for different patients. Results over 14 subjects indicate that average channel usage is reduced an average of 43% over non-adaptive optimization to achieve the same accuracy of classification. This work is implemented using Convolutional Neural Network using TensorFlow library and Python programming to achieve the goal.