Outliers in Smartphone Sensor Data Reveal Outliers in Daily Happiness

Buda, Teodora Sandra; Khwaja, Mohammed; Matic, Aleksandar

doi:10.1145/3448095

Cited by 12 publications

(11 citation statements)

References 25 publications

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“…The majority of articles (59%) used smartphone sensing to infer mental health conditions. While six articles examined overall mental health [ 13 , 24 , 25 , 26 , 27 , 68 ], other studies examined specific factors such as mood [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ] and stress [ 76 , 77 , 78 ]. Additionally, studies also examined specific mental health conditions such as depression [ 7 , 8 , 67 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 ], schizophrenia [ 90 , 91 , 92 , 93 ], and bipolar disorder [ 94 , 95 ].…”

Section: Resultsmentioning

confidence: 99%

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Kulkarni

Kirkham

McNaney

2022

Sensors

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Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.

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Section: Resultsmentioning

confidence: 99%

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Kulkarni

Kirkham

McNaney

2022

Sensors

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“…As previously mentioned, the majority of prior work in behavioral modeling has focused on classification tasks with hand-crafted features. While neural minutelevel models may achieve superior performance than simple classifiers trained on these features, there is nonetheless a large body of work supporting the utility of handcrafted features in sensing [7,17,31,35,40,43,48,66,70]. For this pretraining task, we ask the model to perform a multiple regression to predict the daily features in Table 2 on the final day of the seven day window (Figure 1C).…”

Section: Domain Inspired Featuresmentioning

confidence: 99%

“…Since boosted trees expectedly do not scale well to the thousands of observations in our raw time series data, we compute a set of commonly used features for each day in the window, and then concatenate these features for a final input. While neural models have surpassed non-neural classifiers in most CV and NLP applications, XGBoost is still commonly used in many contemporary sensing studies (e.g., [7,17,35,40,43,48,70]). A list of all features is available in Table 2.…”

Section: Experiments 1: Realistic Single Domain Prediction Tasksmentioning

confidence: 99%

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Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets

Merrill¹,

Althoff²

2022

Preprint

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Detailed mobile sensing data from phones, watches, and fitness trackers offer an unparalleled opportunity to quantify and act upon previously unmeasurable behavioral changes in order to improve individual health and accelerate responses to emerging diseases. Unlike in natural language processing and computer vision, deep representation learning has yet to broadly impact this domain, in which the vast majority of research and clinical applications still rely on manually defined features and boosted tree models or even forgo predictive modeling altogether due to insufficient accuracy. This is due to unique challenges in the behavioral health domain, including very small datasets (∼10 1 participants), which frequently contain missing data, consist of long time series with critical longrange dependencies (length>10 4 ), and extreme class imbalances (>10 3 :1).Here, we introduce a neural architecture for multivariate time series classification designed to address these unique domain challenges. Our proposed behavioral representation learning approach combines novel tasks for self-supervised pretraining and transfer learning to address data scarcity, and captures long-range dependencies across long-history time series through transformer self-attention following convolutional neural network-based dimensionality reduction. We propose an evaluation framework aimed at reflecting expected real-world performance in plausible deployment scenarios. Concretely, we demonstrate (1) performance improvements over baselines of up to 0.15 ROC AUC across five prediction tasks, (2) transfer learning-induced performance improvements of 16% PR AUC in small data scenarios, and (3) the potential of transfer learning in novel disease scenarios through an exploratory case study of zero-shot COVID-19 prediction in an independent data set. Finally, we discuss potential implications for medical surveillance testing.1 Because the full self-attention mechanism of transformers has computational and memory requirements that are quadratic with the input sequence length [4]

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“…This new paradigm creates additional opportunities for the development of novel pervasive and context-aware mobile applications which can benefit from low-latency device-to-device communications and sensing capabilities of modern mobile devices. Examples of these applications include networking services, like data dissemination algorithms [4] and forwarding protocols [5], but also applicationoriented services like mobile health systems [6,7], intelligent transportation systems [8], augmented reality applications [9], and pervasive recommender systems [10,11].…”

Section: Introductionmentioning

confidence: 99%

On-device modeling of user's social context and familiar places from smartphone-embedded sensor data

Campana¹,

Delmastro²

2022

Preprint

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Context modeling and recognition represent complex tasks that allow mobile and ubiquitous computing applications to adapt to the user's situation. The real advantage of context-awareness in mobile environments mainly relies on the prompt system's and applications' reaction to context changes. Current solutions mainly focus on limited context information generally processed on centralized architectures, potentially exposing users' personal data to privacy leakage, and missing personalization features. For these reasons on-device context modeling and recognition represent the current research trend in this area. Among the different information characterizing the user's context in mobile environments, social interactions and visited locations remarkably contribute to the characterization of daily life scenarios. In this paper we propose a novel, unsupervised and lightweight approach to model the user's social context and her locations based on ego networks directly on the user mobile device. Relying on this model, the system is able to extract high-level and semantic-rich context features from smartphone-embedded sensors data. Specifically, for the social context it exploits data related to both physical and cyber social interactions among users and their devices. As far as location context is concerned, we assume that it is more relevant to model the familiarity degree of a specific location for the user's context than the raw location data, both in terms of GPS coordinates and proximity devices. We demonstrate the effectiveness of the proposed approach with 3 different sets of experiments by using 5 real-world datasets collected from a total of 956 personal mobile devices. Specifically, we assess the structure of the social and location ego networks, we provide a semantic evaluation of the proposed models and a complexity evaluation in terms of mobile computing performance. Finally, we demonstrate the relevance of the extracted features by showing the performance of 3 different machine learning algorithms to recognize daily-life situations, obtaining an improvement of 3% of AUROC, 9% of Precision, and 5% in terms of Recall with respect to use only features related to physical context.

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Outliers in Smartphone Sensor Data Reveal Outliers in Daily Happiness

Cited by 12 publications

References 25 publications

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets

On-device modeling of user's social context and familiar places from smartphone-embedded sensor data

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