Akane Sano

Akane Sano
Rice University · Electrical & Computer Engineering

Doctor of Philosophy

About

94
Publications
15,133
Reads
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2,514
Citations
Citations since 2016
70 Research Items
2398 Citations
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20162017201820192020202120220100200300400500
20162017201820192020202120220100200300400500
Introduction
Akane Sano currently works at Rice University. She completed her PhD at Media Lab, Massachusetts Institute of Technology. Akane does research in Computation + Psychiatry, Machine Learning for Health, Affective Computing, Biomedical Health Informatics, Ubiquitous Computing, Neurology and Sleep Medicine.

Publications

Publications (94)
Preprint
Due to individual heterogeneity, performance gaps are observed between generic (one-size-fits-all) models and person-specific models in data-driven health applications. However, in real-world applications, generic models are usually more favorable due to new-user-adaptation issues and system complexities, etc. To improve the performance of the gene...
Preprint
Contrastive learning, a self-supervised learning method that can learn representations from unlabeled data, has been developed promisingly. Many methods of contrastive learning depend on data augmentation techniques, which generate different views from the original signal. However, tuning policies and hyper-parameters for more effective data augmen...
Preprint
Deep learning has performed remarkably well on many tasks recently. However, the superior performance of deep models relies heavily on the availability of a large number of training data, which limits the wide adaptation of deep models on various clinical and affective computing tasks, as the labeled data are usually very limited. As an effective t...
Preprint
There has been an increase in research in developing machine learning models for mental health detection or prediction in recent years due to increased mental health issues in society. Effective use of mental health prediction or detection models can help mental health practitioners re-define mental illnesses more objectively than currently done, a...
Conference Paper
Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focused on how to effectively fuse information from multiple sensors. Fusing multiple sensors is a common choice in many applications, but may not always be feasible in real-wo...
Preprint
Mobile sensing-based modeling of behavioral changes could predict an oncoming psychotic relapse in schizophrenia patients for timely interventions. Deep learning models could complement existing non-deep learning models for relapse prediction by modeling latent behavioral features relevant to the prediction. However, given the inter-individual beha...
Preprint
Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focused on how to effectively fuse information from multiple sensors. Fusing multiple sensors is a common scenario in many applications, but may not always be feasible in real-...
Article
Full-text available
A potential contributor to insufficient sleep among college students is their daily schedule, with sleep sacrificed for other waking activities. We investigated how daily schedules predict day-to-day sleep-wake timing in college students. 223 undergraduate college students (M±SD = 19.2±1.4 years, 37% females) attending a Massachusetts university in...
Preprint
Multimodal wearable physiological data in daily life settings have been used to estimate self-reported stress labels.However, missing data modalities in data collection make it challenging to leverage all the collected samples. Besides, heterogeneous sensor data and labels among individuals add challenges in building robust stress detection models....
Article
Circadian rhythms influence multiple essential biological activities, including sleep, performance, and mood. The dim light melatonin onset (DLMO) is the gold standard for measuring human circadian phase (i.e., timing). The collection of DLMO is expensive and time consuming since multiple saliva or blood samples are required overnight in special co...
Preprint
A schizophrenia relapse has severe consequences for a patient's health, work, and sometimes even life safety. If an oncoming relapse can be predicted on time, for example by detecting early behavioral changes in patients, then interventions could be provided to prevent the relapse. In this work, we investigated a machine learning based schizophreni...
Preprint
Shift workers who are essential contributors to our society, face high risks of poor health and wellbeing. To help with their problems, we collected and analyzed physiological and behavioral wearable sensor data from shift working nurses and doctors, as well as their behavioral questionnaire data and their self-reported daily health and wellbeing l...
Preprint
BACKGROUND Behavioral representations obtained from mobile sensing data can be helpful for the prediction of an oncoming psychotic relapse in patients with schizophrenia and the delivery of timely interventions to mitigate such relapse. OBJECTIVE In this study, we aim to develop clustering models to obtain behavioral representations from continuou...
Article
Background: Behavioral representations obtained from mobile sensing data can be helpful for the prediction of an oncoming psychotic relapse in patients with schizophrenia and the delivery of timely interventions to mitigate such relapse. Objective: In this study, we aim to develop clustering models to obtain behavioral representations from conti...
Article
The time of dim light melatonin onset (DLMO) is the gold standard for circadian phase assessment in humans, but collection of samples for DLMO is time and resource intensive. Numerous studies have attempted to estimate circadian phase from actigraphy data, but most of these studies have involved individuals on controlled and stable sleep-wake sched...
Chapter
A schizophrenia relapse has severe consequences for a patient’s health, work, and sometimes even life safety. If an oncoming relapse can be predicted on time, for example by detecting early behavioral changes in patients, then interventions could be provided to prevent the relapse. In this work, we investigated a machine learning based schizophreni...
Chapter
Shift workers who are essential contributors to our society, face high risks of poor health and wellbeing. To help with their problems, we collected and analyzed physiological and behavioral wearable sensor data from shift working nurses and doctors, as well as their behavioral questionnaire data and their self-reported daily health and wellbeing l...
Article
Appropriate synchronization of the timing of behaviors with the circadian clock and adequate sleep are both important for almost every physiological process. The timing of the circadian clock relative to social (i.e., local) clock time and the timing of sleep can vary greatly between individuals. Whether the timing of these processes is stable with...
Preprint
BACKGROUND Shift work sleep disorders (SWSDs) are associated with the high turnover rates of nurses, and are considered a major medical safety issue. However, initial management can be hampered by insufficient awareness. In recent years, it has become possible to visualize, collect and analyze the work-life balance of healthcare workers with irregu...
Article
Full-text available
Background: Shift work sleep disorders (SWSDs) are associated with the high turnover rates of nurses, and are considered a major medical safety issue. However, initial management can be hampered by insufficient awareness. In recent years, it has become possible to visualize, collect and analyze the work-life balance of healthcare workers with irre...
Article
Full-text available
Schizophrenia is a severe and complex psychiatric disorder with heterogeneous and dynamic multi-dimensional symptoms. Behavioral rhythms, such as sleep rhythm, are usually disrupted in people with schizophrenia. As such, behavioral rhythm sensing with smartphones and machine learning can help better understand and predict their symptoms. Our goal i...
Article
Full-text available
Research interest in personality dynamics over time is rapidly growing. Passive personality assessment via mobile sensors offers an intriguing new approach for measuring a wide variety of personality dynamics. In this paper, we address the possibility of integrating sensor-based assessments to enhance personality dynamics research. We consider a va...
Conference Paper
We examine the problem of forecasting tomorrow morning's three self-reported levels (on scales from 0 to 100) of stressed-calm, sad-happy, and sick-healthy based on physiological data (skin conductance, skin temperature, and acceleration) from a sensor worn on the wrist from 10am-5pm today. We train automated forecasting regression algorithms using...
Conference Paper
Students, office workers, or other computer and mobile device users can suffer from decrements in alertness or productivity, but many intervention methods on these can be too distracting or even affect daily routines. Using heart rate (HR) to determine a fast and slow target frequency at which to oscillate light brightness stimulation on a laptop,...
Conference Paper
Predicting mood, health, and stress can sound an early alarm against mental illness. Multi-modal data from wearable sensors provide rigorous and rich insights into one's internal states. Recently, deep learning-based features on continuous high-resolution sensor data have outperformed statistical features in several ubiquitous and affective computi...
Conference Paper
Predicting one's mood, health, and stress in the future may provide useful feedback before wellbeing related problems become severe. Previously, researchers developed participant-dependent wellbeing prediction models using mobile and wearable sensors, where the models were trained and tested with the same group of people. However, in real-world app...
Article
Continuous wearable sensor data in high resolution contain physiological and behavioral information that can be utilized to predict human health and wellbeing, establishing the foundation for developing early warning systems to eventually improve human health and wellbeing. We propose a deep neural network framework, the Locally Connected Long Shor...
Article
Full-text available
The Interactions website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.
Article
Full-text available
Human biology is deeply rooted in the daily 24-hour temporal period. Our biochemistry varies significantly and idiosyncratically over the course of a day. Staying out of sync with one's circadian rhythm can lead to many complications over time, including a higher likelihood for cardiovascular disease, cancer, obesity, and mental health problems [1]...
Article
2020 IEEE. We examine the problem of forecasting tomorrow morning's three self-reported levels (on scales from 0 to 100) of stressed-calm, sad-happy, and sick-healthy based on physiological data (skin conductance, skin temperature, and acceleration) from a sensor worn on the wrist from 10am-5pm today. We train automated forecasting regression algor...
Article
Study objectives: Sleep regularity, in addition to duration and timing, is predictive of daily variations in well-being. One possible contributor to changes in these sleep dimensions are early morning scheduled events. We applied a composite metric - the Composite Phase Deviation (CPD) - to assess mistiming and irregularity of both sleep and event...
Conference Paper
Mental health issues affect a significant portion of the world's population and can result in debilitating and life-threatening outcomes. To address this increasingly pressing healthcare challenge, there is a need to research novel approaches for early detection and prevention. Toward this, ubiquitous systems can play a central role in revealing an...
Preprint
Circadian rhythms govern most essential biological processes in the human body; they influence multiple biological activities including sleep, performance, mood, skin temperature, hormone production, and nutrient absorption. The dim light melatonin onset (DLMO) is the current gold standard for measuring human circadian phase (or timing). The collec...
Conference Paper
Accurately forecasting well-being may enable people to make desirable behavioral changes that could improve their future well-being. In this paper, we evaluate how well an automated model can forecast the next-day's well-being (specifically focusing on stress, health, and happiness) from static models (support vector machine and logistic regression...
Article
2019 IEEE. Accurately forecasting well-being may enable people to make desirable behavioral changes that could improve their future well-being. In this paper, we evaluate how well an automated model can forecast the next-day's well-being (specifically focusing on stress, health, and happiness) from static models (support vector machine and logistic...
Article
2019 IEEE. Accurately forecasting stress may enable people to make behavioral changes that could improve their future health. For example, accurate stress forecasting might inspire people to make changes to their schedule to get more sleep or exercise, in order to reduce excessive stress tomorrow night. In this paper, we examine how accurately the...
Conference Paper
Mental health issues affect a significant portion of the world's population and can result in debilitating and life-threatening outcomes. To address this increasingly pressing healthcare challenge, there is a need to research novel approaches for early detection and prevention. Toward this, ubiquitous systems can play a central role in revealing an...
Conference Paper
Multimodal signals allow us to gain insights into internal cognitive processes of a person, for example: speech and gesture analysis yields cues about hesitations, knowledgeability, or alertness, eye tracking yields information about a person's focus of attention, task, or cognitive state, EEG yields information about a person's cognitive load or i...
Article
Unobtrusive and accurate ambulatory methods are needed to monitor long-term sleep patterns for improving health. Previously developed ambulatory sleep detection methods rely either in whole or in part on self-reported diary data as ground truth, which is a problem, since people often do not fill them out accurately. This paper presents an algorithm...
Chapter
We designed, developed, and evaluated a novel system, QuantifyMe, for novice self-experimenters to conduct proper-methodology single-case self-experiments in an automated and scientific manner using their smartphones. In this work we evaluate its use with four different kinds of personalized investigations, examining how variables such as sleep dur...
Conference Paper
Introduction We recently developed the Composite Phase Deviation (CPD), a metric that quantifies mistiming of sleep along two dimensions: (i) relative to an individual’s habitual sleep timing, and (ii) relative to their sleep timing on the previous day. Given recent findings that sleep regularity can be predictive of mood, we examined whether CPD i...
Article
Full-text available
Smartphones and wearable sensors have enabled unprecedented data collection, with many products now providing feedback to users about recommended step counts or sleep durations. However, these recommendations do not provide personalized insights that have been shown to be best suited for a specific individual. A scientific way to find individualize...
Book
ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018. We designed, developed, and evaluated a novel system, QuantifyMe, for novice self-experimenters to conduct proper-methodology single-case self-experiments in an automated and scientific manner using their smartphones. In this work we evaluate its use w...
Article
While accurately predicting mood and wellbeing could have a number of important clinical benefits, traditional machine learning (ML) methods frequently yield low performance in this domain. We posit that this is because a one-size-fits-all machine learning model is inherently ill-suited to predicting outcomes like mood and stress, which vary greatl...
Article
Background: Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being. Objective: We developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phone...
Article
2017 IEEE. To accomplish forecasting of mood in real-world situations, affective computing systems need to collect and learn from multimodal data collected over weeks or months of daily use. Such systems are likely to encounter frequent data loss, e.g. when a phone loses location access, or when a sensor is recharging. Lost data can handicap classi...
Article
2017 IEEE. A growing number of studies show links between changes in tongue appearance and human health conditions. This paper studies tongue color changes in the context of stress to explore the feasibility of providing a novel and non-invasive stress measurement method. In a laboratory study, 24 participants were asked to perform a calm and a str...
Article
Full-text available
The association of irregular sleep schedules with circadian timing and academic performance has not been systematically examined. We studied 61 undergraduates for 30 days using sleep diaries, and quantified sleep regularity using a novel metric, the sleep regularity index (SRI). In the most and least regular quintiles, circadian phase and light exp...
Article
Introduction Spontaneous electrodermal responses (EDRs) during sleep, or sleep storms, have been observed since the 1960s. With results counter-intuitive to an emotional arousal interpretation, these studies have found that sleep storms occurred most frequently during slow wave sleep and least frequently during REM sleep. However, little is known a...
Article
Introduction Irregular sleep-wake schedules are commonplace in modern society. Recent studies have indicated the importance of sleep regularity, in addition to sleep duration. We studied in college students how weekly sleep regularity is predictive of daily self-reported happiness, healthiness and calmness, during one week and on the first day foll...
Article
Introduction While polysomnography (PSG) is currently the gold standard for sleep-wake scoring, existing PSG technologies are impractical for long-term home use. Meanwhile, semi-automatic scoring from sleep diaries and actigraphy are commonly used in ambulatory sleep studies, but significant effort is required by users to maintain accurate diaries,...
Article
Introduction Circadian rhythms modulate the timing and quality of sleep. Methods for measuring and predicting the timing of an individual’s circadian rhythms are therefore valuable to clinical assessment of sleep pathology or operational assessment of sleep scheduling. Mathematical models have been developed to predict the effects of light/dark pat...
Article
Introduction Perceived wellbeing, as measured by self-reported health, stress, and happiness, has a number of important clinical health consequences. The ability to model and predict these measures could therefore be immensely beneficial in the treatment and prevention of mental illness. However, predicting self-reported health, stress, and happine...
Article
Introduction College is a critical developmental time period for establishing long-term health behaviors, including appropriate sleep timing and duration and exercise habits. Understanding how these behaviors interact, and also how they may influence subjective mood, is vital to identifying potential modifiable behaviors that may improve health dur...
Article
Inadequate sleep affects health in multiple ways. Unobtrusive ambulatory methods to monitor long-term sleep patterns in large populations could be useful for health and policy decisions. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep episode on/offset. We co...
Chapter
This chapter provides an overview of intelligent mobile, wearable, and ambient device applications for behavioral health care. Several of the latest advancements in these technologies are presented and descriptions of applicable artificial intelligence methods and technologies are provided. Examples of their practical applications in behavioral and...
Conference Paper
Full-text available
In HCI research, attention has focused on understanding external influences on workplace multitasking. We explore instead how multitasking might be influenced by individual factors: personality, stress, and sleep. Forty information workers' online activity was tracked over two work weeks. The median duration of online screen focus was 40 seconds. T...
Conference Paper
Full-text available
While email provides numerous benefits in the workplace, it is unclear how patterns of email use might affect key workplace indicators of productivity and stress. We investigate how three email use patterns: duration, interruption habit, and batching, relate to perceived workplace productivity and stress. We tracked email usage with computer loggin...
Thesis
This thesis carries out a series of studies and develops a methodology and tools to measure and analyze ambulatory physiological, behavioral and social data from wearable sensors and mobile phones with trait data such as personality, for learning about behaviors and traits that impact human health and wellbeing. This thesis also validates the metho...
Conference Paper
In order to model students' happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month each. The data collected include physiological signals, location, smartphone logs, and survey responses to behavioral questions. Each day, participants reported their wellbeing on measures includi...
Article
In order to model students' happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month each. The data collected include physiological signals, location, smartphone logs, and survey responses to behavioral questions. Each day, participants reported their wellbeing on measures includi...
Conference Paper
Full-text available
We collected and analyzed subjective and objective data using surveys and wearable sensors worn day and night from 68 participants for ~30 days each, to address questions related to the relationships among sleep duration, sleep irregularity, self-reported Happy-Sad mood and other daily behavioral factors in college students. We analyzed this behavi...
Conference Paper
Full-text available
Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the de...
Conference Paper
Electrodermal activity (EDA) recording is a powerful, widely used tool for monitoring psychological or physiological arousal. However, analysis of EDA is hampered by its sensitivity to motion artifacts. We propose a method for removing motion artifacts from EDA, measured as skin conductance (SC), using a stationary wavelet transform (SWT). We model...