Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia

Kikhia, Basel; Stavropoulos, Thanos G.; Andreadis, Stelios; Karvonen, Niklas; Kompatsiaris, Ioannis; Sävenstedt, Stefan; Pijl, Marten; Melander, Catharina

doi:10.3390/s16121989

Cited by 78 publications

(49 citation statements)

References 30 publications

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“…Specifically, we collected EEG, eye-tracking, facial expressions and wristband data from 32 students, while they were answering an adaptive self-assessment test. Next, we extracted features from the data sources (eg, number of fixations, blink presence, BVP) that have been commonly used in literature Huang et al, 2007;Kikhia et al, 2016;Reichle et al, 2009), to add ground truth contextual knowledge (Di Mitri et al, 2018), that would be necessary for the interpretation of the findings later on. After the feature extraction step, we employed two feature selection algorithms (PCA, RF) to find the set of a few important variables contributing to the learning outcomes, ie, that strongly correlate with effort and performance.…”

Section: Discussionmentioning

confidence: 99%

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Sharma¹,

Papamitsiou²,

Giannakos³

2019

Brit J Educational Tech

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Students' on‐task engagement during adaptive learning activities has a significant effect on their performance, and at the same time, how these activities influence students' behavior is reflected in their effort exertion. Capturing and explaining effortful (or effortless) behavior and aligning it with learning performance within contemporary adaptive learning environments, holds the promise to timely provide proactive and actionable feedback to students. Using sophisticated machine learning (ML) algorithms and rich learner data, facilitates inference‐making about several behavioral aspects (including effortful behavior) and about predicting learning performance, in any learning context. Researchers have been using ML methods in a “black‐box” approach, ie, as a tool where the input data is the learner data and the output is a given class from the chosen construct. This work proposes a methodological shift from the “black‐box” approach to a “grey‐box” approach that bridges the hypothesis/literature‐driven (feature extraction) “white‐box” approach with the computation/data‐driven (feature fusion) “black‐box” approach. This will allow us to utilize data features that are educationally and contextually meaningful. This paper aims to extend current methodological paradigms, and puts into practice the proposed approach in an adaptive self‐assessment case study taking advantage of new, cutting‐edge, interdisciplinary work on building pipelines for educational data, using innovative tools and techniques. What is already known about this topic Capturing and measuring learners' engagement and behavior using physiological data has been explored during the last years and exhibits great potential. Effortless behavioral patterns commonly exhibited by learners, such as “cheating,” “guessing” or “gaming the system” counterfeit the learning outcome. Multimodal data can accurately predict learning engagement, performance and processes. What this paper adds Generalizes a methodology for building machine learning pipelines for multimodal educational data, using a modularized approach, namely the “grey‐box” approach. Showcases that fusion of eye‐tracking, facial expressions and arousal data provide the best prediction of effort and performance in adaptive learning settings. Highlights the importance of fusing data from different channels to obtain the most suited combinations from the different multimodal data streams, to predict and explain effort and performance in terms of pervasiveness, mobility and ubiquity. Implications for practice and/or policy Learning analytics researchers shall be able to use an innovative methodological approach, namely the “grey‐box,” to build machine learning pipelines from multimodal data, taking advantage of artificial intelligence capabilities in any educational context. Learning design professionals shall have the opportunity to fuse specific features of the multimodal data to drive the interpretation of learning outcomes in terms of physiological learner states. The constraints from th...

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Section: Discussionmentioning

confidence: 99%

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Sharma¹,

Papamitsiou²,

Giannakos³

2019

Brit J Educational Tech

View full text Add to dashboard Cite

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“…Another pilot study evaluated the utility of a wrist sensor (Philips DTI-2) and digital dashboard to track agitation and stress in six nursing home patients with dementia over 2 months (total recorded time was 142 h across 37 days). 21 Wrist sensor data (galvanic skin conductance, accelerometry, skin and environment temperature, and ambient light) were extracted weekly and compared with 24 h observations made by nursing staff study across four parameters-sleep, aggression, stress, and normal. These data allowed the authors to develop objective thresholds with sensor data for defining "stress" and "agitation" in AD patients and develop a dashboard that allows a clinician to run a stress analysis for a given patient over a given time period.…”

Section: Mobile Assessment Of Function (Activities Of Daily Living) Amentioning

confidence: 99%

Mobile and pervasive computing technologies and the future of Alzheimer’s clinical trials

2018

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The rapid growth of mobile phones, automated speech recognizing personal assistants, and internet access among the elderly provides new opportunities for incorporating such technologies into clinical research and personalized medical care. Alzheimer's disease is a good test case given the need for early detection, the high rate of clinical trial failures, the need to more efficiently recruit patients for trials, and the need for sensitive and ecologically valid trial outcomes.

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“…Studies on indoor locations [14] continuously monitor users' locations to identify activity patterns and prevent them from moving into dangerous areas. Studies on collecting the activity or biometric data of a user with a wearable device [15,16] aim to monitor and manage the user's health in real-time by measuring walking, gesture, and posture information. Several studies [17][18][19] provide appropriate services to users by analyzing and predicting activities based on measured data in the residential environment.…”

Section: Smart Home Researchmentioning

confidence: 99%

Edge Computing-Based Self-Organized Device Network for Awareness Activities of Daily Living in the Home

2020

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Activities of daily living (ADL) are important indicators for awareness of brain health in the elderly, and hospitals use ADL as a standard test for diagnosing chronic brain diseases such as dementia. However, since it is difficult to judge real-life ADL in hospitals, doctors typically predict ADL ability through interviews with patients or accompanying caregivers. Recently, many studies have attempted to diagnose accurate brain health by collecting and analyzing the real-life ADL of patients in their living environments. However, most of these were conducted by constructing and implementing expensive smart homes with the concept of centralized computing, and ADL data were collected from simple data about patients’ home appliance usage and the surrounding environment. Despite the high cost of building a smart home, the collected ADL data are inadequate for predicting accurate brain health. In this study, we developed and used three types of portable devices (wearable, tag, and stationary) that can be easily installed and operated in typical existing houses. We propose a self-organized device network structure based on edge computing that can perform user perception, location perception, and behavioral perception simultaneously. This approach enables us to collect user activity data, analyze ADL in real-time to determine if the user’s behavior was successful or abnormal, and record the physical ability of the user to move between fixed spaces. The characteristics of this proposed system enable us to distinguish patients from other family members and provide real-time notifications after a forgetful or mistaken action. We implemented devices that constitute the edge network of the smart home scenario and evaluated the performance of this system to verify its usefulness.

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Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia

Cited by 78 publications

References 30 publications

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Mobile and pervasive computing technologies and the future of Alzheimer’s clinical trials

Edge Computing-Based Self-Organized Device Network for Awareness Activities of Daily Living in the Home

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