Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data

Campana, Mattia Giovanni; Chatzopoulos, Dimitris; Delmastro, Franca; Hui, Pan

doi:10.1145/3267305.3274178

Cited by 5 publications

(10 citation statements)

References 41 publications

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“…The main goal of COMPASS is to find similarities in the mobile sensors data to recognize the situation in which a mobile user is involved. In order to evaluate its performance in the reference scenario, in this section we perform a set of experiments by using two real-world datasets: ContextLabeler, presented in our previous work (Campana et al, 2018), and ExtraSensory, proposed in (Vaizman et al, 2018a). Both datasets have been collected from real mobile devices and in the same experimental setup: data has been collected from users that were engaged in their regular natural behavior (i.e., in the wild).…”

Section: Real-world Context Datasetsmentioning

confidence: 99%

“…As a first step we are planning to test and evaluate the proposed solution in real mobile environments. To this aim, we are currently integrating COMPASS in ContextKit, a prototype mobile framework we developed to provide contextaware features to third-party mobile applications, presented in our previous work (Campana et al, 2018). Currently, ContextKit implements a sensing layer that is able to collect data generated by a heterogeneous set of smartphone sensors, including both physical and virtual sensors.…”

Section: Time Performances Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

COMPASS: Unsupervised and online clustering of complex human activities from smartphone sensors

Campana

Delmastro²

2021

Expert Systems with Applications

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Section: Real-world Context Datasetsmentioning

confidence: 99%

Section: Time Performances Evaluationmentioning

confidence: 99%

COMPASS: Unsupervised and online clustering of complex human activities from smartphone sensors

Campana

Delmastro²

2021

Expert Systems with Applications

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“…The crossover with affective computing, to recognise and interpret human emotion, further highlights the complexity and challenges in creating context-aware systems [37]. However, improvements in machine learning [39], increased availability of relevant data [9], an enhanced battery and network performance mean that efficacious context models are increasingly practicable [11]. Although research has attempted to encourage behaviour change through recommendations [18,20,21,38,42,49], machine learning models [28,41] and timely interventions [30,32,39], CoCo is the first system, that we know of, which combines alcohol, caffeine and cortisol sensors with a functional context model in order to encourage specific user behaviours.…”

Section: Background and Related Workmentioning

confidence: 99%

Context-Aware Wearables

Snooks

Lindley

Richards

et al. 2021

Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

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We present Connected Companion (CoCo), a health tracking wearable that provides users with timely, context-relevant notifications aimed at improving wellness. Traditionally, self-tracking wearables report basic health data such as resting heart rate; these data are visualised and positive behaviours (e.g. exercising often) are encouraged with rudimentary gamification (e.g. award badges) and notification systems. CoCo is the first wearable to combine caffeine, alcohol and cortisol sensors, a context network (which predicts user context), and a wellness model (which establishes per-user wellness measures). Working in tandem these provide users with notifications that encourage discrete behaviours intended to optimise user-wellness per very specific biological and social contexts. The paper describes the (sometimes unexpected) results of a user-study intended to evaluate CoCo's efficacy and we conclude with a discussion about the power and responsibility that comes with attempts to build context-aware computing systems. CCS CONCEPTS • Human-centered computing à User interface design; Information visualization; • Applied computing à Health informatics; • Computer systems organization à Sensors and actuators; • General and reference à Design.

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“…Context-awareness represents the key feature of such applications. Specifically, the great variety of sensors embedded in modern mobile devices (e.g., smartphones and wearables) provides essential information to recognize different aspects of the user's daily life, including, for example, movements and body postures [12] and daily life situations [13]. According to Rawassizadeh et al [3], processing user's data directly on the local device provides several advantages.…”

Section: Introductionmentioning

confidence: 99%

“…In this case social context and location information are based on data derived from smartphone-embedded sensors and from the user interaction with her personal mobile device. As shown in Figure 1, we envision the implementation of the proposed models as part of a pre-existing middleware architecture [13], which includes all the necessary components to perform the context-recognition task directly on the user's device: a Sensing Manager (SM) layer that unobtrusively collects context data from both physical and virtual sensors available on mobile devices, a Context Modeling (CM) layer aimed at processing raw sensor data to extract meaningful features to characterize the user's context; and a Context Reasoning (CR) layer that relies on such features to recognize the user's context by using machine learning models, including both unsupervised clustering solutions [24,25] and pre-trained supervised classifiers (e.g., Random Forest and Artificial Neural Networks [26,27]).…”

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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Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data

Cited by 5 publications

References 41 publications

COMPASS: Unsupervised and online clustering of complex human activities from smartphone sensors

COMPASS: Unsupervised and online clustering of complex human activities from smartphone sensors

Context-Aware Wearables

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

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