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Debus, Maike Elisabeth

Nom
Debus, Maike Elisabeth
Affiliation principale
Fonction
Professeur.e assistant.e
Email
maike.debus@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/31815
_
0000-0003-1040-540X

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  • Publication
    Restriction temporaire
    The Role of Model Personalization for Sleep Stage and Sleep Quality Recognition Using Wearables
    (2022)
    Shkurta Gashi
    ;
    Lidia Alecci
    ;
    Elena Di Lascio
    ;
    Debus, Maike Elisabeth 
    ;
    Francesca Gasparini
    ;
    Silvia Santini
    Personal informatics systems can help people promote their health and well-being. Recent studies have shown that such systems can be used to infer relevant health indicators such as, e.g., stress, anxiety, and sleeping habits. While automatic detection of sleep has been studied extensively, there is a lack of studies exploring how population and personalized models influence the performance of sleep detection. In this article, we address this challenge by investigating the recognition of sleep/wake stages and high/low sleep quality with a focus on the impact of personalized models. To evaluate our approach, we collect a dataset of physiological signals and self-reports about sleep/wake times and sleep quality score. The dataset contains 6557 hours of sensor data collected using wristbands from 16 participants over one month. Our results show that personalized models perform significantly better than population models for sleep quality recognition, and are comparably good for sleep stage detection. The balanced accuracy for sleep/wake and high/low sleep quality are 92.2% and 61.51%, which are significantly higher than baseline classifiers.
  • Publication
    Restriction temporaire
    Handling Missing Data For Sleep Monitoring Systems
    (2022)
    Shkurta Gashi
    ;
    Lidia Alecci
    ;
    Martin Gjoreski
    ;
    Elena Di Lascio
    ;
    Abhinav Mehrotra
    ;
    Mirco Musolesi
    ;
    Debus, Maike Elisabeth 
    ;
    Francesca Gasparini
    ;
    Silvia Santini
    Sensor-based sleep monitoring systems can be used to track sleep behavior on a daily basis and provide feedback to their users to promote health and well-being. Such systems can provide data visualizations to enable self-reflection on sleep habits or a sleep coaching service to improve sleep quality. To provide useful feedback, sleep monitoring systems must be able to recognize whether an individual is sleeping or awake. Existing approaches to infer sleep-wake phases, however, typically assume continuous streams of data to be available at inference time. In real-world settings, though, data streams or data samples may be missing, causing severe performance degradation of models trained on complete data streams. In this paper, we investigate the impact of missing data to recognize sleep and wake, and use regression- and interpolation-based imputation strategies to mitigate the errors that might be caused by incomplete data. To evaluate our approach, we use a data set that includes physiological traces – collected using wristbands –, behavioral data – gathered using smartphones – and self-reports from 16 participants over 30 days. Our results show that the presence of missing sensor data degrades the balanced accuracy of the classifier on average by 10-35 percentage points for detecting sleep and wake depending on the missing data rate. The imputation strategies explored in this work increase the performance of the classifier by 4-30 percentage points. These results open up new opportunities to improve the robustness of sleep monitoring systems against missing data.
  • Publication
    Restriction temporaire
    Automatic Recognition of Flow During Work Activities Using Context and Physiological Signals
    (2021)
    Elena Di Lascio
    ;
    Shkurta Gashi
    ;
    Debus, Maike Elisabeth 
    ;
    Silvia Santini
    Flow is a positive affective state occurring when individuals are fully immersed into an activity. Being in flow during work activities can lead to higher performance and productivity. Despite the importance of flow at work, few approaches have been proposed for its automatic recognition using sensor data and most existing studies are conducted in laboratory settings with simulated work activities. In this paper, we investigate the use of physiological data, collected using wrist-worn devices, combined with context information, obtained through self-reports, to automatically distinguish between low and high levels of flow. We investigate the role of the context for flow perceptions and in its automatic recognition. Further, we compare the performance of several sensor fusion strategies based on shallow and deep learning. To evaluate our approach we use a data set of 390 activities collected during actual work days. Our results show that using raw blood volume pulse, electrodermal activity and the type of activity as input to a sensor-based late fusion approach, implemented using convolutional neural networks, allows to reach a balanced accuracy of 70.93%.
  • Publication
    Restriction temporaire
    A Multi-Sensor Approach to Automatically Recognize Breaks and Work Activities of Knowledge Workers in Academia
    (2020)
    Elena Di Lascio
    ;
    Shkurta Gashi
    ;
    Juan Sebastian Hidalgo
    ;
    Beatrice Nale
    ;
    Debus, Maike Elisabeth 
    ;
    Silvia Santini
    Personal informatics systems for the work environment can help improving workers' well-being and productivity. Using both self-reported data logged manually by the users and information automatically inferred from sensor measurements, such systems may track users' activities at work and help them reflect on their work habits through insightful data visualizations. They can further support interventions like, e.g., blocking distractions during work activities or suggest the user to take a break. The ability to automatically recognize when the user is engaged in a work activity or taking a break is thus a fundamental primitive such systems need to implement. In this paper, we explore the use of data collected from personal devices -- smartwatches, laptops, and smartphones -- to automatically recognize when users are working or taking breaks. We collect a data set of of continuous streams of sensor data captured from personal devices along with labels indicating whether a user is working or taking a break. We use multiple instruments to facilitate the collection of users' self-reported labels and discuss our experience with this approach. We analyse the available data -- 449 labelled activities of nine knowledge workers collected during a typical work week -- using machine learning techniques and show that user-independent models can achieve a (F1 score) of 94% for the identification of work activities and of 69% for breaks, outperforming baseline methods by 5-10 and 12-54 percentage points, respectively.