R-3 Repository :: Browsing by Author "Sano, Akane"

Browsing by Author "Sano, Akane"

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A Two-Step Machine Learning Framework for Wearable Sensing Systems in Personal Healthcare
(2020-07-20) Wan, Cheng; Sano, Akane
Wearable sensing systems can support a wide range of real-world applications. In the past years, a lot of research in this field explored how to design machine learning models using wearable sensor data for personal healthcare usage. There are two challenges in dealing with wearable sensor data for personal healthcare: 1) how to incorporate data sampled at different sampling rates into one model, e.g., daily sampled data and frequently sampled data, and 2) how to deal with the data that contain long-missing patterns. For overcoming these two challenges, we propose a two-step machine learning framework, where the first step extracts features from the data before a predefined time point, while the second step combines this summary with the rest part of data for the machine learning task. For investigating the first problem, we implement our framework for predicting dim light melatonin onset (DLMO) that uses daily sampled sleep parameters and frequently sampled sensor data (light exposure, skin temperature, and physical activity). For the second problem, we predict momentary stress state using sensor data that contains some long-missing segments. The experiment shows that the two-step framework has better performance on both tasks than traditional one-step models, which suggests that this framework is applicable to addressing the above two challenges.
Adaptation of sleep to daylight saving time is slower in people consuming a high-fat diet
(Elsevier, 2024) McHill, Andrew W.; Sano, Akane; Barger, Laura K.; Phillips, Andrew J. K.; Czeisler, Charles A.; Klerman, Elizabeth B.
Adaptation of the circadian clock to the environment is essential for optimal health, well-being, and performance. Animal models demonstrate that a high-fat diet impairs circadian adaptation to advances of the light-dark cycle; it is unknown whether this occurs in humans. Utilizing a natural experiment that occurs when humans must advance their behaviors to an earlier hour for daylight saving time (DST), we measured the influence of diet on sleep/wake timing relative to dim-light melatonin onset time. Students with a lower-fat diet rapidly altered their sleep-wake timing to match the imposed time change, whereas those with a high-fat diet were slower to adapt to the time change. Moreover, a faster shift in timing after DST was associated with higher general health, lower body mass index, and higher grade point average. These data suggest that diet may influence the speed of sleep and circadian adaptation, which could have implications for health and performance.
Deep Learning for Healthcare: Empirical Studies and Methodological Advances in Physiological Time Series Data
(2024-04-19) Guo, Peikun; Sano, Akane
Human physiological time series data, including electrocardiograms (ECGs) and wearable sensors data, provide essential information for healthcare applications. The inherent complexity and variability of these signals introduce both challenges and opportunities for deep learning research. This thesis systematically addresses several critical aspects of physiological time series analysis: (1) We begin by empirically studying the behavior of the "Mixup" family of data augmentation methods, traditionally employed in computer vision, in the context of physiological time series. Through extensive experiments, we verify the unique properties of Mixup in this domain, extending known techniques to new applications. (2) We introduce a novel two-stage curriculum learning approach for seizure forecasting, a critical task in epilepsy treatment. By employing an autoencoder-based scoring stage followed by a subsequent fine-tuning stage involving a pre-trained encoder and a ranked sample pool, we address the challenge of class imbalance in forecasting anomaly events. This scheduled sampling scheme demonstrates superior performance and faster model convergence in seizure prediction. (3) We present a Transformer-based model specifically designed for ECG data analysis. Drawing inspiration from the inherent physiological components of ECG signals, the model adopts a representation learning approach, segmenting ECG signals into distinct fragments or "tokens." These tokens, characterized by physiological significance such as P/QRS/T components in the waveforms, are processed by the Transformer. Experiments demonstrated competitive performance in the arrhythmia classification task. This approach ensures that the model's learning process aligns closely with clinical insights, enhancing its interpretability. We evaluate the proposed methods on various datasets, including standard 12-lead ECG signals for project 1 and 3, multiple biomedical time series datasets for project 1, and wristband-recorded physiological signals for project 2. The evaluations show promising results and significant improvements compared to baseline models. %Together, Collectively, the projects form a cohesive narrative that reflects a progression from empirical exploration to the design of deep learning models and schemes, all centered around the unique challenges and opportunities of physiological time series modeling.
Direct Learning for Time-to-Collision Judgments of Approaching Objects: The Role of Fractal 1/f Noise in Exploration
(2020-04-14) Braly, Adam M; DeLucia, Patricia R; Kortum, Philip; Lane, David M; Sano, Akane
The purpose of this dissertation was to determine whether direct learning can improve time-to-collision (TTC) judgments of approaching objects. Accurate information for judgments of TTC is available in the optic array, but research has shown that observers do not always use this accurate information. Even though this information exists in the optic array, observers may not be attuned to use such information. According to direct learning theory, observers must be able to flexibly combine exploration and feedback to calibrate their judgments. This could explain why prior studies found that observers relied on other less reliably accurate sources of information. Research has also shown that fractal fluctuations in exploration involve fluctuations at all time scales, which ostensibly allows perceptual systems the flexibility to detect information. Therefore, fractal fluctuations in exploration may reflect coordination among detection, calibration, and attunement of information for perception or action. This dissertation tested whether judgments of TTC were significantly better when participants were permitted to make exploratory movements with feedback compared to when they were restricted and not given feedback. In a virtual environment, participants viewed scenes of an object that approached them. After a designated time, the object disappeared and participants judged when the object would have reached them, had it continued to move. Exploration and feedback were factorially crossed to create four between-subjects conditions of Exploration- Feedback, Exploration- No Feedback, No Exploration- Feedback, and No Exploration- No Feedback. Results showed that participants in the Exploration- Feedback learned to used more accurate information for their judgments and this improvement was retained in the absence of further feedback. Participants in the No Exploration- Feedback appeared to learn how to use accurate information, but in the absence of further feedback their performance degraded, suggesting a strategy based on feedback rather than learning. Results of fractal analyses revealed that exploratory movements were fractal, and that trail-by-trial fluctuations in the fractal scaling exponent predicted perceptual error. The findings have implications for theories of TTC perception and practical implications are discussed.
Domain Adaptation, Stress, and Burnout Prediction in Shift Workers Using Wearable Data
(2023-04-21) Choto Segovia, Alicia; Sano, Akane
Healthcare workers are part of the shift worker population, and due to their occupational demands, they are at higher risk of developing mental health problems. With mental health disorders on the rise and healthcare workers’ shortages, it is imperative that workers have the tools to manage stress and prevent burnout. There has been a lot of progress in the applications of wearable sensors and machine learning to predict well-being, including stress, anxiety, and mood forecasting. However, wearable sensors collect time series data with high variability between domains with a small number of labels, making it challenging to maintain good predictive performance across domains. A domain is composed of an input space, an output space, and an associated probability distribution. The challenge of domain adaption is to train a model on a source domain and achieve a small error when tested on the target domain. The source and target domains share the same input and output space, but they have different distributions. Machine learning models need to adapt to new test users with data distribution different than the training data and leverage the unlabeled data to improve their predictions. In this work (1) we designed a personalization framework to adapt stress classifier models to new users. We found the closest points in the training set to the testing set and use them to personalize user-independent models. Our results showed that we can use the test users’ unlabeled data to tailor the training set to new users and improve performance from the user-independent model. (2) We proposed a pipeline to study the domain divergence between two datasets, and subsets within the datasets, to identify the best groups to perform domain adaptation. We measured domain divergence using Proxy-A-distance and used domain adversarial neural networks to extract domain invariant representations for stress prediction. We concluded that even if we extract invariant representations, it does not guarantee good performance on the target domain if the domain distributions are too different. (3) We implemented machine learning models to directly predict if a shift worker is at high risk of burnout by analyzing physiological and rhythm features. We analyzed the data of workers with low and high risks of burnout to understand the differences between their features and find markers indicative of burnout. The analysis showed that users with a low risk of burnout have better sleep regularity and lower anxiety. We also found that heart rate-related features and rhythm features are the most important for models to predict burnout. For all our models, we provided interpretability in relation to the input data that contributes to the prediction of well-being by analyzing the model’s feature importance and the correlation of the features with the activations of a convolutional neural network.
Effect of an Internet–Delivered Cognitive Behavioral Therapy–Based Sleep Improvement App for Shift Workers at High Risk of Sleep Disorder: Single-Arm, Nonrandomized Trial
(JMIR Publications, 2023) Ito-Masui, Asami; Sakamoto, Ryota; Matsuo, Eri; Kawamoto, Eiji; Motomura, Eishi; Tanii, Hisashi; Yu, Han; Sano, Akane; Imai, Hiroshi; Shimaoka, Motomu
Background: Shift workers are at high risk of developing sleep disorders such as shift worker sleep disorder or chronic insomnia. Cognitive behavioral therapy (CBT) is the first-line treatment for insomnia, and emerging evidence shows that internet-based CBT is highly effective with additional features such as continuous tracking and personalization. However, there are limited studies on internet-based CBT for shift workers with sleep disorders. Objective: This study aimed to evaluate the impact of a 4-week, physician-assisted, internet-delivered CBT program incorporating machine learning–based well-being prediction on the sleep duration of shift workers at high risk of sleep disorders. We evaluated these outcomes using an internet-delivered CBT app and fitness trackers in the intensive care unit. Methods: A convenience sample of 61 shift workers (mean age 32.9, SD 8.3 years) from the intensive care unit or emergency department participated in the study. Eligible participants were on a 3-shift schedule and had a Pittsburgh Sleep Quality Index score ≥5. The study comprised a 1-week baseline period, followed by a 4-week intervention period. Before the study, the participants completed questionnaires regarding the subjective evaluation of sleep, burnout syndrome, and mental health. Participants were asked to wear a commercial fitness tracker to track their daily activities, heart rate, and sleep for 5 weeks. The internet-delivered CBT program included well-being prediction, activity and sleep chart, and sleep advice. A job-based multitask and multilabel convolutional neural network–based model was used for well-being prediction. Participant-specific sleep advice was provided by sleep physicians based on daily surveys and fitness tracker data. The primary end point of this study was sleep duration. For continuous measurements (sleep duration, steps, etc), the mean baseline and week-4 intervention data were compared. The 2-tailed paired t test or Wilcoxon signed rank test was performed depending on the distribution of the data. Results: In the fourth week of intervention, the mean daily sleep duration for 7 days (6.06, SD 1.30 hours) showed a statistically significant increase compared with the baseline (5.54, SD 1.36 hours; P=.02). Subjective sleep quality, as measured by the Pittsburgh Sleep Quality Index, also showed statistically significant improvement from baseline (9.10) to after the intervention (7.84; P=.001). However, no significant improvement was found in the subjective well-being scores (all P>.05). Feature importance analysis for all 45 variables in the prediction model showed that sleep duration had the highest importance. Conclusions: The physician-assisted internet-delivered CBT program targeting shift workers with a high risk of sleep disorders showed a statistically significant increase in sleep duration as measured by wearable sensors along with subjective sleep quality. This study shows that sleep improvement programs using an app and wearable sensors are feasible and may play an important role in preventing shift work–related sleep disorders.
Exploiting social graph networks for emotion prediction
(Springer Nature, 2023) Khalid, Maryam; Sano, Akane; Computational Wellbeing Group
Emotion prediction plays an essential role in mental healthcare and emotion-aware computing. The complex nature of emotion resulting from its dependency on a person’s physiological health, mental state, and his surroundings makes its prediction a challenging task. In this work, we utilize mobile sensing data to predict self-reported happiness and stress levels. In addition to a person’s physiology, we also incorporate the environment’s impact through weather and social network. To this end, we leverage phone data to construct social networks and develop a machine learning architecture that aggregates information from multiple users of the graph network and integrates it with the temporal dynamics of data to predict emotion for all users. The construction of social networks does not incur additional costs in terms of ecological momentary assessments or data collection from users and does not raise privacy concerns. We propose an architecture that automates the integration of the user’s social network in affect prediction and is capable of dealing with the dynamic distribution of real-life social networks, making it scalable to large-scale networks. The extensive evaluation highlights the prediction performance improvement provided by the integration of social networks. We further investigate the impact of graph topology on the model’s performance.
Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress
(2020-08-20) Li, Boning; Sano, Akane; Veeraraghavan, Ashok
High-resolution wearable sensor data contain physiological and behavioral information that can be utilized to predict and eventually improve human health and wellbeing. We propose a semi-supervised deep neural network framework to automatically learn features from passively collected multi-modal sensor data. This process can be personalized by finetuning the general features with participant-specific data. Then, using the learned features, we performed personalized prediction of subjective wellbeing scores with high precision. We also provide visual explanation and statistical interpretation of the automatically learned features and the prediction models. In this study, we explored multiple implementations of our framework including locally connected linear network, convolutional neural network, recurrent neural network, and visual attention network. The framework was evaluated using wearable sensor data and wellbeing labels collected from college students (total 6391 days from N=239). Sensor data include skin temperature, skin conductance, and acceleration; wellbeing scores include self-reported mood, health and stress ranged from 0 to 100. Compared to the prediction performance based on hand-crafted features, the proposed framework achieved higher precision with a smaller number of features. Our results show promising potentials of predicting self-reported mood, health, and stress accurately using an interpretable deep learning framework, ultimately for developing real-time health and wellbeing monitoring and intervention systems that can benefit various populations.
Further Improvements in Human Emotion and Wellbeing Prediction: Personalization, Modality Fusion, and Semi-Supervised Learning
(2022-06-07) Yu, Han; Sano, Akane
Human physiological and behavioral data have been continuously collected and studied, empowered by modern wearable devices. Among plenty of research areas, emotion and wellbeing estimation has become a rising topic. Researchers and developers found that there is potential to decode human emotion and wellbeing with physiological and behavioral data. Several outstanding studies have shown promising results and provide incentives for the future development of this area. Nevertheless, there are challenges that remain, such as modality missing, heterogeneity among subjects, and label sparseness. In this thesis, we addressed obstacles in modeling physiological and behavioral data. (1) We proposed a job-role based multi-task and multi-label learning to build models for different groups of populations with correlated labels; (2) We proposed a modality fusion network to adaptively fit parameters and infer emotion prediction even with missing data modalities; (3) We proposed a personalized attention model to learn the heterogeneity in sensor data and labels among individuals; (4) We designed a semi-supervised learning framework to learn representations from the massive unlabeled physiological sequences. We evaluated the proposed methods on data sets collected in the wild as well as using public data sets. The evaluations showed significant improvements in our approaches compared to the baseline models. Further, we conducted the model interpretability analysis to identify the critical part of the input signal that contributes to the proposed deep learning models.
Internet-Based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder Empowered by Well-Being Prediction: Protocol for a Pilot Study
(JMIR, 2021) Ito-Masui, Asami; Kawamoto, Eiji; Sakamoto, Ryota; Yu, Han; Sano, Akane; Motomura, Eishi; Tanii, Hisashi; Sakano, Shoko; Esumi, Ryo; Imai, Hiroshi; Shimaoka, Motomu
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 health care workers with irregular sleeping and working habits using wearable sensors that can continuously monitor biometric data under real-life settings. In addition, internet-based cognitive behavioral therapy for psychiatric disorders has been shown to be effective. Application of wearable sensors and machine learning may potentially enhance the beneficial effects of internet-based cognitive behavioral therapy. Objective: In this study, we aim to develop and evaluate the effect of a new internet-based cognitive behavioral therapy for SWSD (iCBTS). This system includes current methods such as medical sleep advice, as well as machine learning well-being prediction to improve the sleep durations of shift workers and prevent declines in their well-being. Methods: This study consists of two phases: (1) preliminary data collection and machine learning for well-being prediction; (2) intervention and evaluation of iCBTS for SWSD. Shift workers in the intensive care unit at Mie University Hospital will wear a wearable sensor that collects biometric data and answer daily questionnaires regarding their well-being. They will subsequently be provided with an iCBTS app for 4 weeks. Sleep and well-being measurements between baseline and the intervention period will be compared. Results: Recruitment for phase 1 ended in October 2019. Recruitment for phase 2 has started in October 2020. Preliminary results are expected to be available by summer 2021. Conclusions: iCBTS empowered with well-being prediction is expected to improve the sleep durations of shift workers, thereby enhancing their overall well-being. Findings of this study will reveal the potential of this system for improving sleep disorders among shift workers. Clinical Trial: UMIN Clinical Trials Registry UMIN000036122 (phase 1), UMIN000040547 (phase 2); https://tinyurl.com/dkfmmmje, https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046284
Leveraging Graph Networks for Health and Wellbeing Prediction
(2024-12-05) Khalid, Maryam; Sano, Akane
Health and well-being prediction plays an essential role in mental healthcare and well-being-aware computing. The complex nature of well-being, resulting from its dependency on a person’s physiological health, mental state, and surroundings, makes its prediction a challenging task. In this work, we utilize mobile sensing data to predict self-reported well-being metrics. In addition to a person’s physiology, we incorporate the environment’s impact through weather and social network data. To this end, we leverage phone data to construct social networks and develop a machine learning architecture that aggregates information from multiple users within the graph network and integrates it with the temporal dynamics of data to predict well-being outcomes for all users. To address the dynamic nature of social networks, we introduce GEDD (Graph Extraction for Dynamic Distribution), an approach that automatically adapts to fluctuating network sizes. GEDD utilizes graph properties, including connectivity and components, to transform variable-sized graphs into a standardized format, ensuring no user data is discarded. The proposed architecture supports online learning, making it feasible to scale to large networks without adding ecological momentary assessments (EMAs) or additional data collection burdens, thus preserving user privacy. Through extensive evaluations, we show that social network incorporation improves prediction accuracy, although node influence, especially in users with high eigenvector centrality, can amplify noise. To address this, we propose a robust system that leverages attention and social contagion in well-being behaviors through graph networks and integrates it with physiological and phone data from ubiquitous mobile and wearable devices. This system is designed to predict well-being outcomes, such as sleep duration and other health metrics while mitigating the challenges posed by noisy and incomplete data. Finally, we further leverage the graph structure to reduce the user burden associated with collecting health and well-being metrics, which are often captured at a much lower resolution than sensing data through surveys and EMAs. To this end, we introduce a benchmark framework to evaluate existing state-of-the-art graph-based active learning (AL) strategies in dynamic sensing environments. Our framework assesses AL strategies in terms of adaptability to real-time, user-centric data by evaluating performance over time in a stream-based setting. We also introduce new metrics, including sampling entropy, coverage ratio, and time-gap analysis, to quantify user burden, sampling diversity, and generalization performance. These metrics provide a holistic view of the AL strategies’ effectiveness, helping to identify those that best balance predictive accuracy and user engagement. This comprehensive evaluation framework supports scalable and efficient health prediction systems, facilitating practical, large-scale deployment.
Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint
(JMIR, 2022) Clay, Ieuan; Cormack, Francesca; Fedor, Szymon; Foschini, Luca; Gentile, Giovanni; Hoof, Chris van; Kumar, Priya; Lipsmeier, Florian; Sano, Akane; Smarr, Benjamin; Vandendriessche, Benjamin; Luca, Valeria De
The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.
New Data-driven Insights into Adult and Pediatric Type-2 Diabetes
(2023-12-01) Abbasi, Mahsan; Sabharwal, Ashutosh; Aazhang, Behnam; Sano, Akane
The currently recommended strategies for managing diabetes rely primarily on broad, population-level data and average treatment effects observed in clinical trials. There is a critical need for an approach to personalizing type 2 diabetes (T2D) intervention, thereby refining treatment efficacy. In this thesis, we utilize unsupervised clustering techniques to investigate the various factors contributing to the prevalence and progression of T2D in individuals with different lifestyles and characteristics. We demonstrated our approach on two datasets from two projects. In the first project, we developed a data-driven framework to find physical activity-related phenotypes in an underserved T2D population. Moreover, we examine the association between physical activity measures and participants’ diabetes progression in the exclusive subgroups. In the second case, we classified pediatric patients into five distinct diabetes subtypes using K-Prototypes cluster analysis. Additionally, our findings provide new insights on how the treatment strategies and risk stratifications can differ even at the time of diabetes diagnosis based on a more precise characterization of pediatric T2D. In conclusion, the proposed data-driven approaches could bring us one step closer to precision therapies and individualized recommendations to become a routine part of diabetes management.
Objective Sociability and Impulsivity Measures for Dimensional Assessment of Mental Health
(2024-04-16) Lamichhane, Bishal; Sabharwal, Ashutosh; Sano, Akane; Patel, Ankit; Beier, Margaret
Mental health disorders have a high prevalence and are on the rise globally. Subjective self-reports used for mental health assessment, with their recall and reporting biases, pose a scalability challenge for accurate and frequent assessment. Additionally, the existing nosology of disorders has rampant comorbidity and heterogeneity, creating challenges in the diagnosis and treatment. Instead of the dichotomous diagnostic labeling of mental health based on subjective self-reports, as is currently practiced, assessing mental health along the objectively measured bio-behavioral dimensions of functioning could provide a foundation for scalable, robust, and accurate mental healthcare. Towards the goal of improved mental health assessment, this thesis investigated objective measures of sociability and impulsivity, the bio-behavioral dimensions of functioning implicated in several mental health disorders. Sociability represents an individual’s tendency to interact and socialize with others. Sociability deficits and altered sociability patterns are commonly implicated across several mental health disorders. In this thesis, we propose using free-living audio to obtain sociability measures objectively. We developed a deep learning-based audio processing pipeline, ECoNet, that addresses the challenges of free-living audio processing to infer sociability measures accurately. The inferred sociability measures were significantly correlated with dimensional mental health measures in a clinical study comprising participants with diverse mental health conditions. Free-living audio also complemented the conventional mobile sensing modalities for dimensional mental health score prediction. Our results establish the potential of free-living audio-based objective sociability measures to represent mental health state. Impulsivity represents an individual’s tendency to act on urges without sufficient forethought about one’s action. Several mental health disorders commonly implicate heightened impulsivity. In this thesis, we propose using multimodal behavioral, physiological, and neurobiological measurements to model impulsivity objectively. We conducted a clinical study with mood disorder participants and discovered the complementarity of objective modalities to model impulsivity and predict the clinical outcome of suicidality with high accuracy. The complementarity of modalities was additionally validated with a large open-source dataset. To exploit the richness of neurobiological measurements for predicting impulsivity, we developed ImpulsNet. ImpulsNet is a lightweight deep learning model based on multi-task learning that outperforms existing models in the literature to predict impulsivity. Our results establish the feasibility of objectively modeling impulsivity for mental health applications. This thesis provides a foundation for dimensional mental health assessment using objective sociability and impulsivity measures. Further validation in larger studies should be pursued in future work. Other objectively assessed dimensions of functioning should also be explored to better represent mental health in the vastness of bio-behavioral space.
Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis
(JMIR, 2022) Zhou, Joanne; Lamichhane, Bishal; Ben-Zeev, Dror; Campbell, Andrew; Sano, Akane
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 continuous multimodal mobile sensing data for relapse prediction tasks. The identified clusters can represent different routine behavioral trends related to daily living of patients and atypical behavioral trends associated with impending relapse. Methods: We used the mobile sensing data obtained from the CrossCheck project for our analysis. Continuous data from six different mobile sensing-based modalities (ambient light, sound, conversation, acceleration, etc) obtained from 63 patients with schizophrenia, each monitored for up to a year, were used for the clustering models and relapse prediction evaluation. Two clustering models, Gaussian mixture model (GMM) and partition around medoids (PAM), were used to obtain behavioral representations from the mobile sensing data. These models have different notions of similarity between behaviors as represented by the mobile sensing data, and thus, provide different behavioral characterizations. The features obtained from the clustering models were used to train and evaluate a personalized relapse prediction model using balanced random forest. The personalization was performed by identifying optimal features for a given patient based on a personalization subset consisting of other patients of similar age. Results: The clusters identified using the GMM and PAM models were found to represent different behavioral patterns (such as clusters representing sedentary days, active days but with low communication, etc). Although GMM-based models better characterized routine behaviors by discovering dense clusters with low cluster spread, some other identified clusters had a larger cluster spread, likely indicating heterogeneous behavioral characterizations. On the other hand, PAM model-based clusters had lower variability of cluster spread, indicating more homogeneous behavioral characterization in the obtained clusters. Significant changes near the relapse periods were observed in the obtained behavioral representation features from the clustering models. The clustering model-based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0.23 for the relapse prediction task in a leave-one-patient-out evaluation setting. The obtained F2 score was significantly higher than that of a random classification baseline with an average F2 score of 0.042. Conclusions: Mobile sensing can capture behavioral trends using different sensing modalities. Clustering of the daily mobile sensing data may help discover routine and atypical behavioral trends. In this study, we used GMM-based and PAM-based cluster models to obtain behavioral trends in patients with schizophrenia. The features derived from the cluster models were found to be predictive for detecting an oncoming psychotic relapse. Such relapse prediction models can be helpful in enabling timely interventions.
Turning data into better mental health: Past, present, and future
(Frontiers Media S.A., 2022) Moukaddam, Nidal; Sano, Akane; Salas, Ramiro; Hammal, Zakia; Sabharwal, Ashutosh
In this mini-review, we discuss the fundamentals of using technology in mental health diagnosis and tracking. We highlight those principles using two clinical concepts: (1) cravings and relapse in the context of addictive disorders and (2) anhedonia in the context of depression. This manuscript is useful for both clinicians wanting to understand the scope of technology use in psychiatry and for computer scientists and engineers wishing to assess psychiatric frameworks useful for diagnosis and treatment. The increase in smartphone ownership and internet connectivity, as well as the accelerated development of wearable devices, have made the observation and analysis of human behavior patterns possible. This has, in turn, paved the way to understand mental health conditions better. These technologies have immense potential in facilitating the diagnosis and tracking of mental health conditions; they also allow the implementation of existing behavioral treatments in new contexts (e.g., remotely, online, and in rural/underserved areas), and the possibility to develop new treatments based on new understanding of behavior patterns. The path to understand how to best use technology in mental health includes the need to match interdisciplinary frameworks from engineering/computer sciences and psychiatry. Thus, we start our review by introducing bio-behavioral sensing, the types of information available, and what behavioral patterns they may reflect and be related to in psychiatric diagnostic frameworks. This information is linked to the use of functional imaging, highlighting how imaging modalities can be considered “ground truth” for mental health/psychiatric dimensions, given the heterogeneity of clinical presentations, and the difficulty of determining what symptom corresponds to what disease. We then discuss how mental health/psychiatric dimensions overlap, yet differ from, psychiatric diagnoses. Using two clinical examples, we highlight the potential agreement areas in assessment/management of anhedonia and cravings. These two dimensions were chosen because of their link to two very prevalent diseases worldwide: depression and addiction. Anhedonia is a core symptom of depression, which is one of the leading causes of disability worldwide. Cravings, the urge to use a substance or perform an action (e.g., shopping, internet), is the leading step before relapse. Lastly, through the manuscript, we discuss potential mental health dimensions.
Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia
(Springer Nature, 2020) Tseng, Vincent W.-S.; Sano, Akane; Ben-Zeev, Dror; Brian, Rachel; Campbell, Andrew T.; Hauser, Marta; Kane, John M.; Scherer, Emily A.; Wang, Rui; Wang, Weichen; Wen, Hongyi; Choudhury, Tanzeem
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 is to predict fine-grained symptom changes with interpretable models. We computed rhythm-based features from 61 participants with 6,132 days of data and used multi-task learning to predict their ecological momentary assessment scores for 10 different symptom items. By taking into account both the similarities and differences between different participants and symptoms, our multi-task learning models perform statistically significantly better than the models trained with single-task learning for predicting patients’ individual symptom trajectories, such as feeling depressed, social, and calm and hearing voices. We also found different subtypes for each of the symptoms by applying unsupervised clustering to the feature weights in the models. Taken together, compared to the features used in the previous studies, our rhythm features not only improved models’ prediction accuracy but also provided better interpretability for how patients’ behavioral rhythms and the rhythms of their environments influence their symptom conditions. This will enable both the patients and clinicians to monitor how these factors affect a patient’s condition and how to mitigate the influence of these factors. As such, we envision that our solution allows early detection and early intervention before a patient’s condition starts deteriorating without requiring extra effort from patients and clinicians.
Validation studies of the FLASH-TV system to passively measure children’s TV viewing
(Springer Nature, 2024) Vadathya, Anil Kumar; Garza, Tatyana; Alam, Uzair; Ho, Alex; Musaad, Salma M. A.; Beltran, Alicia; Moreno, Jennette P.; Baranowski, Tom; Haidar, Nimah; Hughes, Sheryl O.; Mendoza, Jason A.; Veeraraghavan, Ashok; Young, Joseph; Sano, Akane; O’Connor, Teresia M.
TV viewing is associated with health risks, but existing measures of TV viewing are imprecise due to relying on self-report. We developed the Family Level Assessment of Screen use in the Home (FLASH)-TV, a machine learning pipeline with state-of-the-art computer vision methods to measure children’s TV viewing. In three studies, lab pilot (n = 10), lab validation (n = 30), and home validation (n = 20), we tested the validity of FLASH-TV 3.0 in task-based protocols which included video observations of children for 60 min. To establish a gold-standard to compare FLASH-TV output, the videos were labeled by trained staff at 5-second epochs for whenever the child watched TV. For the combined sample with valid data (n = 59), FLASH-TV 3.0 provided a mean 85% (SD 8%) accuracy, 80% (SD 17%) sensitivity, 86% (SD 8%) specificity, and 0.71 (SD 0.15) kappa, compared to gold-standard. The mean intra-class correlation (ICC) of child’s TV viewing durations of FLASH-TV 3.0 to gold-standard was 0.86. Overall, FLASH-TV 3.0 correlated well with the gold standard across a diverse sample of children, but with higher variability among Black children than others. FLASH-TV provides a tool to estimate children’s TV viewing and increase the precision of research on TV viewing’s impact on children’s health.

Browsing by Author "Sano, Akane"

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