Towards Continuous Mobile Sensing for Remote COPD Monitoring

Chronic Obstructive Pulmonary Disease (COPD) is a debilitating and life-threatening disease. In 2016 there were an estimated 251 million cases of COPD globally and the World Health Organization predicts that by 2030 COPD will be the third leading cause of death worldwide. Technologies that help people with COPD manage their condition could have significant impact on their lives. The work presented in this thesis outlines a system that uses wearable and mobile devices to passively sense and monitor patients with COPD. Mobile and wearable devices contain a myriad of sensors and have been used in applications ranging from earthquake detection to flight control for drones. To make these devices relevant for COPD monitoring, this thesis focuses on two signals that can be extracted from wearable sensors, respiratory rate and coughing. To detect respiratory rate, we propose WearBreathing -- our system for respiratory rate detection using the accelerometer and gyroscope sensors found in smartwatches. While respiratory rate from a smartwatch has been done in previous works, existing methods are only accurate in in-lab settings and while participants are stationary, making them unsuitable for remote monitoring. Therefore, WearBreathing is designed specifically to operate in the wild and we show that it is indeed more accurate in the wild than existing methods. Similar to respiratory rate, we found that existing cough detection solutions do not perform well in the wild. Using an in-the-wild dataset that we collect from COPD patients, we first characterize the sounds captured by a smartwatch microphone in a wild setting. Using our dataset, we build a state of the art cough detector, which we call CoughWatch that works on in-the-wild data and is more accurate than existing cough detectors. Finally, because mobile devices are resource constrained devices designed for intermittent use, battery life becomes a significant concern when attempting to continuously monitor sensor data. End users, such as patients with COPD, are unlikely to use a device that provides only a few hours of battery life per charge. Therefore, we propose Sidewinder, a developer friendly hardware architecture for energy efficient continuous sensing on mobile devices.Ph.D

Feasibility of a wearable self-management application for patients with COPD at home: a pilot study

Abstract Background Among people with COPD, smartphone and wearable technology may provide an effective method to improve care at home by supporting, encouraging, and sustaining self-management. The current study was conducted to determine if patients with COPD will use a dedicated smartphone and smartwatch app to help manage their COPD and to determine the effects on their self-management. Methods We developed a COPD self-management application for smartphones and smartwatches. Participants were provided with the app on a smartphone and a smartwatch, as well as a cellular data plan and followed for 6 months. We measured usage of the different smartphone app functions. For the primary outcome, we examined the change in self-management from baseline to the end of follow up. Secondary outcomes include changes in self-efficacy, quality of life, and COPD disease control. Results Thirty-four patients were enrolled and followed. Mean age was 69.8 years, and half of the participants were women. The most used functions were recording steps through the smartwatch, entering a daily symptom questionnaire, checking oxygen saturation, and performing breathing exercises. There was no significant difference in the primary outcome of change in self-management after use of the app or in overall total scores of health-related quality of life, disease control or self-efficacy. Conclusion We found older patients with COPD would engage with a COPD smartphone and smartwatch application, but this did not result in improved self-management. More research is needed to determine if a smartphone and smartwatch application can improve self-management in people with COPD. Trial registration ClinicalTrials.Gov NCT03857061, First Posted February 27, 2019

Talk2Me: Automated linguistic data collection for personal assessment.

Language is one the earliest capacities affected by cognitive change. To monitor that change longitudinally, we have developed a web portal for remote linguistic data acquisition, called Talk2Me, consisting of a variety of tasks. In order to facilitate research in different aspects of language, we provide baselines including the relations between different scoring functions within and across tasks. These data can be used to augment studies that require a normative model; for example, we provide baseline classification results in identifying dementia. These data are released publicly along with a comprehensive open-source package for extracting approximately two thousand lexico-syntactic, acoustic, and semantic features. This package can be applied arbitrarily to studies that include linguistic data. To our knowledge, this is the most comprehensive publicly available software for extracting linguistic features. The software includes scoring functions for different tasks

Additional file 1 of Feasibility of a wearable self-management application for patients with COPD at home: a pilot study

Supplementary Material 1