Mental health conditions remain under-diagnosed even in countries with common access to advanced medical care. The ability to accurately and efficiently predict mood from easily collectible data has several important implications towards the early detection and intervention of mental health disorders. One promising data source to help monitor human behavior is from daily smartphone usage. However, care must be taken to summarize behaviors without identifying the user through personal (e.g., personally identifiable information) or protected attributes (e.g., race, gender). In this paper, we study behavioral markers or daily mood using a recent dataset of mobile behaviors from high-risk adolescent populations. Using computational models, we find that multimodal modeling of both text and app usage features is highly predictive of daily mood over each modality alone. Furthermore, we evaluate approaches that reliably obfuscate user identity while remaining predictive of daily mood. By combining multimodal representations with privacy-preserving learning, we are able to push forward the performance-privacy frontier as compared to unimodal approaches.
Carnegie Mellon University（卡内基梅隆大学）；University of Oregon（俄勒冈大学）；Columbia University（哥伦比亚大学）；University of Pittsburgh（匹兹堡大学）
Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. The ability to accurately and efficiently predict mood from easily collectible data has several important implications for the early detection, intervention, and treatment of mental health disorders. One promising data source to help monitor human behavior is daily smartphone usage. However, care must be taken to summarize behaviors without identifying the user through personal (e.g., personally identifiable information) or protected (e.g., race, gender) attributes. In this paper, we s…
FedMood: Federated Learning on Mobile Health Data for Mood Detection
School of Cyber Science and Technology, Beihang University（北京航空航天大学网络空间科学与技术学院）；Beijing Advanced Innovation Center for Big Data and Brain Computing（北京大数据与脑计算高精尖创新中心）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Fordham University（福特汉姆大学）；Lehigh University（理海大学）
University of Tokyo（东京大学）；University of Oregon（俄勒冈大学）；Columbia University（哥伦比亚大学）；University of Pittsburgh（匹兹堡大学）；Carnegie Mellon University（卡内基梅隆大学）
Federated learning is a method of training models on private data distributed over multiple devices. To keep device data private, the global model is trained by only communicating parameters and updates which poses scalability challenges for large models. To this end, we propose a new federated learning algorithm that jointly learns compact local representations on each device and a global model across all devices. As a result, the global model can be smaller since it only operates on local representations, reducing the number of communicated parameters. Theoretically, we provide a generalizat…
National University of Defense Technology（国防科技大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Tsinghua University（清华大学）
Recent years have witnessed an explosive growth of mobile devices. Mobile devices are permeating every aspect of our daily lives. With the increasing usage of mobile devices and intelligent applications, there is a soaring demand for mobile applications with machine learning services. Inspired by the tremendous success achieved by deep learning in many machine learning tasks, it becomes a natural trend to push deep learning towards mobile applications. However, there exist many challenges to realize deep learning in mobile applications, including the contradiction between the miniature nature …
Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study
King's College London（伦敦国王学院）；University College London（伦敦大学学院）；South London and Maudsley National Health Services Foundation Trust（南伦敦和莫兹利国家医疗服务基金会信托）；Amsterdam University Medical Centre（阿姆斯特丹大学医学中心）；Vrije Universiteit（自由大学）；Parc Sanitari Sant Joan de Déu（圣琼德杜保健公园）；Universitat de Barcelona（巴塞罗那大学）；Katholieke Universiteit Leuven（鲁汶大学）；LAB University of Applied Sciences（LAB应用科学大学）；Northwestern University（西北大学）；Janssen Research and Development LLC（杨森研发有限责任公司）；H. Lundbeck A/S
Beijing Univerisity of Posts and Telecommunications（北京邮电大学）；Ping An Technology (Shenzhen)（平安科技（深圳））
With the booming development and popularity of mobile applications, different verticals accumulate abundant data of user information and social behavior, which are spontaneous, genuine and diversified. However, each platform describes user's portraits in only certain aspect, resulting in difficult combination of those internet footprints together. In our research, we proposed a modeling approach to analyze user's online behavior across different social media platforms. Structured and unstructured data of same users shared by NetEase Music and Sina Weibo have been collected for cross-platform a…
University of Oxford（牛津大学）；The Alan Turing Institute（艾伦·图灵研究所）；University of Warwick（华威大学）；University College London（伦敦大学学院）
There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbeing. In this paper, we investigate the effectiveness of neural network models for predicting users' level of stress by using the location information collected by smartphones. We characterize the mobility patterns of individuals using the GPS metrics presented in the literature an…
INESC TEC（INESC TEC（葡萄牙系统与计算机工程研究所））；Faculty of Engineering, University of Porto（波尔图大学工程学院）；University of São Paulo（圣保罗大学）
Human activity recognition (HAR) is a classification task that aims to classify human activities or predict human behavior by means of features extracted from sensors data. Typical HAR systems use wearable sensors and/or handheld and mobile devices with built-in sensing capabilities. Due to the widespread use of smartphones and to the inclusion of various sensors in all contemporary smartphones (e.g., accelerometers and gyroscopes), they are commonly used for extracting and collecting data from sensors and even for implementing HAR systems. When using mobile devices, e.g., smartphones, HAR sys…
With the growing popularity of wearable devices, the ability to utilize physiological data collected from these devices to predict the wearer's mental state such as mood and stress suggests great clinical applications, yet such a task is extremely challenging. In this paper, we present a general platform for personalized predictive modeling of behavioural states like students' level of stress. Through the use of Auto-encoders and Multitask learning we extend the prediction of stress to both sequences of passive sensor data and high-level covariates. Our model outperforms the state-of-the-art i…
The Role of Embodiment in Intuitive Whole-Body Teleoperation for Mobile Manipulation
Computer Science Department, TU Darmstadt（图恩-达姆施塔特大学计算机科学系）；University of São Paulo（圣保罗大学）；Centre for Cognitive Science, TU Darmstadt（图恩-达姆施塔特大学认知科学中心）；Systems AI for Robot Learning, German Research Center for AI (DFKI)（德国人工智能研究中心（DFKI）机器人学习系统AI）
With the development of wearable technologies, a new kind of healthcare data has become valuable as medical information. These data provide meaningful information regarding an individual's physiological and psychological states, such as activity level, mood, stress, and cognitive health. These biomarkers are named digital since they are collected from digital devices integrated with various sensors. In this study, we explore digital biomarkers related to stress modality by examining data collected from mobile phones and smartwatches. We utilize machine learning techniques on the Tesserae datas…
