Predicting Subjective Sleep Quality Using Objective Measurements in Older Adults

Humans spend almost a third of their lives asleep. Sleep has a pivotal effect on job performance, memory, fatigue recovery, and both mental and physical health. Sleep quality (SQ) is a subjective experience and reported via patients’ self-reports. Predicting subjective SQ based on objective measurements can enhance diagnosis and treatment of SQ defects, especially in older adults who are subject to poor SQ. In this dissertation, we assessed enhancement of subjective SQ prediction using an easy-to-use E4 wearable device, machine learning techniques and identifying disease-specific risk factors of abnormal SQ in older adults. First, we designed a clinical decision support system to estimate SQ and feeling refreshed after sleep using data extracted from an E4 wearable device. Specifically, we processed four raw physiological signals of heart rate variability (HRV), electrodermal activity, body movement, and skin temperature using distinct signal processing methodologies. Following this, we extracted signal-specific features and selected a subset of the features using recursive feature elimination cross validation strategy to maximize the accuracy of SQ classifiers in predicting the SQ of older caregivers. Second, we investigated discovering more effective features in SQ prediction using HRV features which are not only effortlessly measurable but also can reflect sleep stage transitions and some sleep disorders. Evaluation of two interpretable machine learning methodologies and a convolutional neural network (CNN) methodology demonstrated the CNN outperforms by an accuracy of 0.6 in predicting light, medium, and deep SQ. This outcome verified the capability of using HRV features measurable by easy-to-use wearable devices, in predicting SQ. Finally, we scrutinized daytime sleepiness risk factors as a sign of abnormal SQ from four perspectives: sleep fragmented, sleep propensity, sleep resilience, and non-restorative sleep. The analysis demonstrates distinguishability of the main risk factors of excessive daytime sleepiness (EDS) between patients suffering from fragmented sleep (e.g. apnea) and sleep propensity. We identified the average area under oxygen desaturation curve corresponds to apnea/hypopnea event as a disease-specific risk factor of abnormal SQ. Our further daytime sleepiness prediction demonstrated the significant role of the founded disease-specific risk factor as well

Socialoscope: Sensing User Loneliness and Its Interactions with Personality Traits

Loneliness and social isolation can have a serious impact on one’s mental health, leading to increased stress, lower self-esteem, panic attacks, and drug or alcohol addictions. Older adults and international students are disproportionately affected by loneliness. This thesis investigates Socialoscope, a smartphone app that passively detects loneliness in smartphone users based on the user’s day-to-day social interactions, communication and smartphone activity sensed by the smartphone’s built-in sensors. Statistical analysis is used to determine smartphone features most correlated with loneliness. A previously established relationship between loneliness and personality type is explored. The most correlated features are used to synthesize machine learning classifiers that infer loneliness levels from smartphone sensor features with an accuracy of 90%. These classifiers can be used to make the Socialoscope an intelligent loneliness sensing Android app. The results show that, of the five Big-Five Personality Traits, emotional stability and extraversion personality traits are strongly correlated with the sensor features such as number of messages, number of outgoing calls, number of late night browser searches, number of long incoming or outgoing calls and number of auto-joined trusted Wi-Fi SSIDs. Moreover, the classifier accuracy while classifying loneliness levels is significantly improved to 98% by taking these personality traits into consideration. Socialoscope can be integrated into the healthcare system as an early warning indicator of patients requiring intervention or utilized for personal self-reflection

Utilizing new technologies to measure therapy effectiveness for mental and physical health

Mental health is quickly becoming a major policy concern, with recent data reporting increasing and disproportionately worse mental health outcomes, including anxiety, depression, increased substance abuse, and elevated suicidal ideation. One specific population that is especially high risk for these issues is the military community because military conflict, deployment stressors, and combat exposure contribute to the risk of mental health problems. Although several pharmacological approaches have been employed to combat this epidemic, their efficacy is mixed at best, which has led to novel nonpharmacological approaches. One such approach is Operation Surf, a nonprofit that provides nature-based programs advocating the restorative power of the ocean and surfing. Although the limited research in this area has shown a positive impact on the health of veterans, these results were based on self-reported survey instruments that suffer from a series of well-known biases. Fortunately, the recent introduction of wearable technology (e.g., Whoop bands) that unobtrusively gather physiological data such as heart rate variability (HRV), resting heart rate (RHR), and both rapid eye movement (REM) and deep sleep, offers an opportunity to validate or invalidate traditional survey assessment data. This study used survey data to measure changes in depression, anxiety, and posttraumatic stress disorder (PTSD), together with data generated from Whoop bands, and qualitative data, producing a more robust set of programmatic efficacy inferences for military veterans who participated in Operation Surf between 2021–2022. Paired samples t tests were used to analyze the data gathered before the intervention, immediately after, and 1 month later. Survey scores before the therapy, as measured by the psychometrically sound PHQ-8 (depression), PCL-5 (PTSD), and GAD-7 (anxiety), were significantly higher than both time points after therapy, revealing statistically significant and clinically significant decreases in anxiety, depression, and PTSD symptoms. Physiological data indicated varying degrees of statistically significant change in HRV, RHR, REM sleep, and deep sleep, while the qualitative data provided supported the quantitative findings. Taken together, the introduction of physiological data gathered from wearable technology can hopefully further understanding toward a low-cost, scalable treatment modality while eliminating stigmas and barriers to care for military veterans and informing public policy care decisions

Passive Biometric Authentication via Head Mounted Display using Ballistocardiography

We demonstrate that by monitoring readings from the accelerometer and gyroscope of a head- mounted display, we are able to construct a waveform that is closely tied with the cardiac cycle of the wearer. Furthermore, we show that, from this waveform, we can then extract features that are not only consistent over time in the wearer, but also reasonably unique between different individuals. By then constructing an ensemble of random forest classifiers, we show that such a model can be used to determine if a new set of features does or does not belong to wearer. In this way, such a system can be used in an authentication context with a high degree of accuracy

Classification of lapses in smokers attempting to stop: A supervised machine learning approach using data from a popular smoking cessation smartphone app

Introduction Smoking lapses after the quit date often lead to full relapse. To inform the development of real-time, tailored lapse prevention support, we used observational data from a popular smoking cessation app to develop supervised machine learning algorithms to distinguish lapse from non-lapse reports. Methods We used data from app users with ≥20 unprompted data entries, which included information about craving severity, mood, activity, social context, and lapse incidence. A series of group-level supervised machine learning algorithms (e.g., Random Forest, XGBoost) were trained and tested. Their ability to classify lapses for out-of-sample i) observations and ii) individuals were evaluated. Next, a series of individual-level and hybrid algorithms were trained and tested. Results Participants (N=791) provided 37,002 data entries (7.6% lapses). The best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.969 (95% CI= 0.961-0.978). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUC=0.482-1.000). Individual-level algorithms could be constructed for 39/791 participants with sufficient data, with a median AUC of 0.938 (range: 0.518-1.000). Hybrid algorithms could be constructed for 184/791 participants and had a median AUC of 0.825 (range: 0.375-1.000). Discussion Using unprompted app data appeared feasible for constructing a high-performing group-level lapse classification algorithm but its performance was variable when applied to unseen individuals. Algorithms trained on each individual’s dataset, in addition to hybrid algorithms trained on the group plus a proportion of each individual’s data, had improved performance but could only be constructed for a minority of participants. Implications This study used routinely collected data from a popular smartphone app to train and test a series of supervised machine learning algorithms to distinguish lapse from non-lapse events. Although a high-performing group-level algorithm was developed, it had variable performance when applied to new, unseen individuals. Individual-level and hybrid algorithms had somewhat greater performance but could not be constructed for all participants due to lack of variability in the outcome measure. Triangulation of results with those from a prompted study design is recommended prior to intervention development, with real-world lapse prediction likely requiring a balance between unprompted and prompted app data

Data Driven Approach to Thermal Comfort Model Design

Apart from the dominant environmental factors such as relative humidity, radiant, and ambient temperatures, studies have confirmed that thermal comfort significantly depends on internal personal parameters such as metabolic rate, age, and health status. This study reviews the sensitivity of the Predicted Mean Vote (PMV) thermal comfort model relative to its environmental and personal parameters of a group of people in a space. PMV model equations adapted in ASHRAE Standard 55–Thermal Environmental Conditions for Human Occupancy, are used in this investigation to conduct a parametric study by generating and analyzing multi-dimensional comfort zone plots. It has been found that personal parameters such as metabolic rate and clothing have the highest impact. Current and newly emerging advancements in state of the art wearable technology have made it possible to continuously acquired biometric information. This work proposes to access and exploit this data to build a new innovative thermal comfort model. Relying on various supervised machine-learning methods, a thermal comfort model has been produced and compared to a general model to show its superior performance. Finally, the study represents an architecture to employ new thermal comfort model in inexpensive, responsive and extensible smart home service. Advisor: Fadi Alsalee

Hierarchical approach to classify food scenes in egocentric photo-streams.

Recent studies have shown that the environment where people eat can affect their nutritional behavior. In this paper, we provide automatic tools for personalized analysis of a person's health habits by the examination of daily recorded egocentric photo-streams. Specifically, we propose a new automatic approach for the classification of food-related environments, which is able to classify up to 15 such scenes. In this way, people can monitor the context around their food intake in order to get an objective insight into their daily eating routine. We propose a model that classifies food-related scenes organized in a semantic hierarchy. Additionally, we present and make available a new egocentric dataset composed of more than 33,000 images recorded by a wearable camera, over which our proposed model has been tested. Our approach obtains an accuracy and F-score of 56% and 65%, respectively, clearly outperforming the baseline methods