Daniyal Liaqat

Sidewinder: An Energy Efficient and Developer Friendly Heterogeneous Architecture for Continuous Mobile Sensing

Applications that perform continuous sensing on mobile phones have the potential to revolutionize everyday life. Examples range from medical and health monitoring applications, such as pedometers and fall detectors, to participatory sensing applications, such as noise pollution, traffic and seismic activity monitoring. Unfortunately, current mobile devices are a poor match for continuous sensing applications as they require the device to remain awake for extended periods of time, resulting in poor battery life. This paper presents Sidewinder, a new approach towards offloading sensor data processing to a low-power processor and waking up the main processor when events of interest occur. This approach differs from other heterogeneous architectures in that developers are presented with a programming interface that lets them construct application specific wake-up conditions by linking together and parameterizing predefined sensor data processing algorithms. Our experiments indicate performance that is comparable to approaches that provide fully programmable offloading, but do so with a much simpler programming interface that facilitates deployment and portability.

Poster: WearCOPD - Monitoring COPD Patients Remotely using Smartwatches

Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung disease that is characterized by airway obstruction, coughing, shortness of breath and increased sputum production. An acute exacerbation of COPD is a sudden worsening of the disease. Acute exacerbations result in more frequent and severe coughing and increased difficulty breathing. If not treated quickly, hospitalization may be required which is expensive and decreases patients quality of life. If untreated, an acute exacerbation can lead to death. We present WearCOPD, an application that uses a smartwatch and smartphone to continuously monitor physiological signs from patients with the goal of predicting exacerbations before they happen.

Feasibility of Using a Smartwatch to Intensively Monitor Patients With Chronic Obstructive Pulmonary Disease: Prospective Cohort Study

Background Acute exacerbations of chronic obstructive pulmonary disease (COPD) are associated with accelerated decline in lung function, diminished quality of life, and higher mortality. Proactively monitoring patients for early signs of an exacerbation and treating them early could prevent these outcomes. The emergence of affordable wearable technology allows for nearly continuous monitoring of heart rate and physical activity as well as recording of audio which can detect features such as coughing. These signals may be able to be used with predictive analytics to detect early exacerbations. Prior to full development, however, it is important to determine the feasibility of using wearable devices such as smartwatches to intensively monitor patients with COPD. Objective We conducted a feasibility study to determine if patients with COPD would wear and maintain a smartwatch consistently and whether they would reliably collect and transmit sensor data. Methods Patients with COPD were recruited from 3 hospitals and were provided with a smartwatch that recorded audio, heart rate, and accelerations. They were asked to wear and charge it daily for 90 days. They were also asked to complete a daily symptom diary. At the end of the study period, participants were asked what would motivate them to regularly use a wearable for monitoring of their COPD. Results Of 28 patients enrolled, 16 participants completed the full 90 days. The average age of participants was 68.5 years, and 36% (10/28) were women. Survey, heart rate, and activity data were available for an average of 64.5, 65.1, and 60.2 days respectively. Technical issues caused heart rate and activity data to be unavailable for approximately 13 and 17 days, respectively. Feedback provided by participants indicated that they wanted to actively engage with the smartwatch and receive feedback about their activity, heart rate, and how to better manage their COPD. Conclusions Some patients with COPD will wear and maintain smartwatches that passively monitor audio, heart rate, and physical activity, and wearables were able to reliably capture near-continuous patient data. Further work is necessary to increase acceptability and improve the patient experience.

SIDEWINDER: Efficient and Easy-to-Use Continuous Sensing

Applications that perform continuous sensing on mobile phones have the potential to revolutionize everyday life. Examples range from medical and health monitoring applications, such as pedometers and fall detectors, to participatory sensing applications, such as noise pollution, traffic and seismic activity monitoring. Unfortunately, current mobile devices are a poor match for continuous sensing applications as they require the device to remain awake for extended periods of time, resulting in poor battery life. We present Sidewinder, a new approach toward offloading sensor data processing to a lowpower processor and waking up the main processor when events of interest occur. Sidewinder differs from other heterogeneous architectures in that developers are presented with a programming interface that lets them construct custom wake-up conditions by linking together and parameterizing predefined sensor data processing algorithms. Sidewinders wake-up conditions achieve energy efficiency matching fully programmable offloading, but do so with a much simpler programming interface that facilitates deployment and portability.

Using Mobile Sensing to Predict Episodes of Medical Conditions

Certain medical conditions either manifest themselves periodically or are associated with episodes where the condition has a greater impact. Examples of such conditions include Chronic Obstructive Pulmonary Disease (COPD), Seasonal Affective Disorder (SAD) and Bipolar Disorder (BD). Currently, the care of such conditions is reactive and generally relies on patients identifying episodes and visiting their doctor for a proper diagnosis. However, relying on patients to identify episodes may not be reliable or timely. For example, with the onset of depression in SAD, patient selfefficacy is reduced, which hinders their ability to recognize the episode. Despite expecting a depressive episode at the start of winter, SAD patients often do not identify the depression until late winter. Since SAD negatively affects quality of life [5], and treatments have shown to help [4], timely detection and treatment is crucial. Similarly, an acute exacerbation of COPD is a sudden worsening, often caused by a bacterial infection. If caught early, it can be treated with antibiotics. If left untreated, acute exacerbations require hospitalization, which is poor for patient quality of life and expensive on the health care system. Acute exacerbations of COPD may even lead to death. It is estimated that by 2020, COPD will be the 3rd leading cause of death [2]. Again, we see that early detection of episodes is critical. Wearable technology and ubiquitous computing provide a new opportunity to continuously collect and process objective sensor data over extended periods of time. Where detection of episodes previously relied on patient’s ability to self-detect, ubiquitous computing could allow detection based on objective, quantitative data and transform what used to be reactive care into proactive care. The goal of my research is to use wearable technology to collect and process sensor data from patients with certain medical conditions to detect or, ideally, predict episodes related to those conditions. Currently we are collecting data from patients with COPD. We have implemented a data collection framework using Android Wear Smartwatches and Android Smartphones. The