WellBeat: A Framework for Tracking Daily Well-Being Using Smartwatches

Park, Sungkyu; Constantinides, Marios; Aiello, Luca Maria; Quercia, Daniele; Gent, Paul van

doi:10.1109/mic.2020.3017867

Cited by 22 publications

(14 citation statements)

References 15 publications

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“…Once dietary, sedentary, and physical activity patterns are profiled based on accurate physiological signs, mobile apps can draw inferences and provide users with a set of timely recommendations to improve their wellness, fitness, and health conditions. Especially considering that the happiness, alertness, and relaxation of humans are linked with physiological signs [3], mobile apps can even make suggestions for mental and emotional well-being. Occupational Health & Safety (OHS): According to the International Labor Organization, occupational accidents and work-related diseases annually cause 374 million non-fatal injuries and 2.78 million deaths, which accounts for around 4% of global gross domestic product [https://www.ilo.org/global/ topics/safety-and-health-at-work].…”

Section: Iob Opportunities: a Taxonomy Of Iob Devices And Applicationsmentioning

confidence: 99%

The Internet of Bodies: The Human Body as an Efficient and Secure Wireless Channel

Çelik¹,

Eltawil²

2021

Preprint

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<div>Taking a cue from the Internet of Things, the Internet of Bodies (IoB) can be defined as a network of smart objects placed in, on, and around the human body, allowing for intra- and inter-body communications. This position paper aims to provide a glimpse into the opportunities created by implantable, injectable, ingestible, and wearable IoB devices. The paper starts with a thorough discussion of application-specific design goals, technical challenges, and enabling of communication standards. We discuss the reason that the highly radiative nature of radio frequency (RF) systems results in inefficient systems due to over-extended coverage that causes interference and becomes susceptible to eavesdropping. Body channel communication (BCC) presents an attractive, alternative wireless technology by inherently coupling signals to the human body, resulting in highly secure and efficient communications. The conductive nature of body tissues yields a better channel quality, while the BCC's operational frequency range (1-100 kHz) eliminates the need for radio front-ends. State-of-the-art BCC transceivers can reach several tens of Mbps data rates at pJ/b energy efficiency levels that support IoB devices and applications. Furthermore, as the cyber and biological worlds meet, security risks and privacy concerns take center stage, leading to a discussion of the multi-faceted legal, societal, ethical, and political issues related to technology governance.</div>

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Section: Iob Opportunities: a Taxonomy Of Iob Devices And Applicationsmentioning

confidence: 99%

The Internet of Bodies: The Human Body as an Efficient and Secure Wireless Channel

Çelik¹,

Eltawil²

2021

Preprint

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“…The descriptive data recorded includes the means, standard deviations, minimums, and maximums of each dimension of each sensor, namely the accelerometer, gyroscope, and heart-rate sensor. Additionally, using Well-Beat's data processing pipeline [27], we extracted Heart Rate Variability (HRV) parameters in 5-minute window data sets. In particular, we made use of a widely used HRV parameter, the RMSSD (i.e., the root mean square differences of successive RR intervals).…”

Section: Data Set 51 Data Processing Pipelinementioning

confidence: 99%

“…, where n is the total number of RR intervals. We refer the reader to [27] for additional reading in conducting HRV analysis.…”

Section: Data Set 51 Data Processing Pipelinementioning

confidence: 99%

“…The protocol suggests a 10-minute anticipation period, followed by a 10-minute presentation period and, finally, by a 5-minute arithmetic task. To obtain participants' behavior traces, we used an application for Samsung watches [27]. The application continuously records motion and physiological readings through the device's sensors at 10Hz sampling rate.…”

Section: Materials and Apparatusmentioning

confidence: 99%

“…It is defined as the root mean square of the successive differences of R-R intervals, and it is computed as 2 , where n is the total number of RR intervals. We refer the reader to [27] for additional reading in conducting HRV analysis. Normalization.We normalized all features using a z-score transformation as ( 𝑥−𝜇 𝑏1 𝜎 𝑏1 ), where 𝑥 is the value of each data point (Table 1).…”

Section: Data Set 51 Data Processing Pipelinementioning

confidence: 99%

See 2 more Smart Citations

Anticipatory Detection of Compulsive Body-focused Repetitive Behaviors with Wearables

Searle

Spathis

Constantinides

et al. 2021

Proceedings of the 23rd International Conference on Mobile Human-Computer Interaction

Self Cite

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Body-focused repetitive behaviors (BFRBs), like face-touching or skin-picking, are hand-driven behaviors which can damage one's appearance, if not identified early and treated. Technology for automatic detection is still under-explored, with few previous works being limited to wearables with single modalities (e.g., motion). Here, we propose a multi-sensory approach combining motion, orientation, and heart rate sensors to detect BFRBs. We conducted a feasibility study in which participants (N=10) were exposed to BFRBs-inducing tasks, and analyzed 380 mins of signals 1 under an extensive evaluation of sensing modalities, cross-validation methods, and observation windows. Our models achieved an AUC > 0.90 in distinguishing BFRBs, which were more evident in observation windows 5 mins prior to the behavior as opposed to 1-min ones. In a follow-up qualitative survey, we found that not only the timing of detection matters but also models need to be context-aware, when designing just-in-time interventions to prevent BFRBs. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing; Ubiquitous and mobile computing design and evaluation methods.

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Measurement Procedure for Daily Motion and Sleep Classification in Aging People Using Smartwatch Data

Casaccia

Morresi

Revel

et al. 2022

Social Innovation in Long-Term Care Through Digitalization

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WellBeat: A Framework for Tracking Daily Well-Being Using Smartwatches

Cited by 22 publications

References 15 publications

The Internet of Bodies: The Human Body as an Efficient and Secure Wireless Channel

The Internet of Bodies: The Human Body as an Efficient and Secure Wireless Channel

Anticipatory Detection of Compulsive Body-focused Repetitive Behaviors with Wearables

Measurement Procedure for Daily Motion and Sleep Classification in Aging People Using Smartwatch Data

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