Machine Learning for Fatigue Detection using Fitbit Fitness Trackers

Husom, Erik Johannes; Dautov, Rustem; Nedisan, Adela-Aniela; Gonidis, Fotis; Papatzelos, Spyridon; Malamas, Nikolaos

doi:10.5220/0011527500003321

Cited by 5 publications

(2 citation statements)

References 36 publications

(40 reference statements)

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“…The wide adoption of DHTs by both consumers and programs like the AoURP make DHTs a great source of data for researchers. Researchers can use various analytical, statistical, and machine learning approaches to further develop DHT data into digital biomarkers [60][61][62][63]. Like with any technology, there are inherent limitations and sources of error that stakeholders (eg, researchers using DHT data in their analyses) should be aware of.…”

Section: Discussionmentioning

confidence: 99%

The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program

Lederer,

Breton,

Jeong

et al. 2023

JMIR Mhealth Uhealth

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Wearable digital health technologies (DHTs) have become increasingly popular in recent years, enabling more capabilities to assess behaviors and physiology in free-living conditions. The All of Us Research Program (AoURP), a National Institutes of Health initiative that collects health-related information from participants in the United States, has expanded its data collection to include DHT data from Fitbit devices. This offers researchers an unprecedented opportunity to examine a large cohort of DHT data alongside biospecimens and electronic health records. However, there are existing challenges and sources of error that need to be considered before using Fitbit device data from the AoURP. In this viewpoint, we examine the reliability of and potential error sources associated with the Fitbit device data available through the AoURP Researcher Workbench and outline actionable strategies to mitigate data missingness and noise. We begin by discussing sources of noise, including (1) inherent measurement inaccuracies, (2) skin tone–related challenges, and (3) movement and motion artifacts, and proceed to discuss potential sources of data missingness in Fitbit device data. We then outline methods to mitigate such missingness and noise in the data. We end by considering how future enhancements to the AoURP’s Fitbit device data collection methods and the inclusion of new Fitbit data types would impact the usability of the data. Although the reliability considerations and suggested literature are tailored toward Fitbit device data in the AoURP, the considerations and recommendations are broadly applicable to data from wearable DHTs in free-living conditions.

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Section: Discussionmentioning

confidence: 99%

The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program

Lederer,

Breton,

Jeong

et al. 2023

JMIR Mhealth Uhealth

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“…This enables near-realtime inference, reduces latency, saves bandwidth, enhances privacy, and enables offline functionality even in the absence of the Internet connection. All these features are especially important to the healthcare domain where physiological data collected by wearable or portable medical devices are processed either directly on those devices or on a smartphone acting as a wireless gateway [6], [7]. Similarly, the data privacy and network bandwidth constraints are usually critical aspects in various image and video recognition scenarios involving CCTV cameras [8], [9].…”

Section: B Edge Artificial Intelligencementioning

confidence: 99%

Towards Community-Driven Generative AI

Dautov,

Husom,

Sen

et al. 2023

Annals of Computer Science and Information Systems

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While the emerging market of Generative Artificial Intelligence (AI) is increasingly dominated and controlled by the Tech Giants, there is also a growing interest in open-source AI code and models from smaller companies, research organisations and individual users. They often have valuable data that could be used for training, but their computing resources are limited, while data privacy concerns prevent them from sharing this data for public training. A possible solution to overcome these two issues is to utilise the crowd-souring principles and apply federated learning techniques to build a distributed privacypreserving architecture for training Generative AI. This paper discusses how these two key enablers, together with some other emerging technologies, can be effectively combined to build a community-driven Generative AI ecosystem, allowing even small actors to participate in the training of Generative AI models by securely contributing their training data. The paper also discusses related non-technical issues, such as the role of the community and intellectual property rights, and outlines further research directions associated with AI moderation.

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