SWear: Sensing Using WEARables. Generalized Human Crowdsensing on Smartwatches

Boukhechba, Mehdi; Barnes, Laura E.

doi:10.1007/978-3-030-51828-8_67

Cited by 25 publications

(17 citation statements)

References 7 publications

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“…that is equipped with the "SWear" app (Fig. 3 -A), an in-house app designed for collecting long-term sensor data from smartwatches [103]. SWear is available on Android store [104] and is designed to smooth the process of data collection on smartwatches by adding the ability to control each sensor's data collection frequency to the desired sensing regime.…”

Section: ) Physical Sensorsmentioning

confidence: 99%

HARMONY: A Human-Centered Multimodal Driving Study in the Wild

et al. 2021

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Effective shared autonomy requires a clear understanding of driver's behavior, which is governed by multiple psychophysiological and environmental variables. Disentangling this intricate web of interactions requires understanding the driver's state and behaviors in different real-world scenarios, longitudinally. Naturalistic Driving Studies (NDS) have shown to be an effective approach to understanding the driver's state and behavior in real-world scenarios. However, due to the lack of technological and computing capabilities, former NDS only focused on vision-based approaches, ignoring important psychophysiological factors such as cognition and emotion. The main objective of this paper is to introduce HARMONY, a human-centered multimodal naturalistic driving study, where driver's behaviors and states are monitored through (1) in-cabin and outside video streams (2) physiological signals including driver's heart rate and hand acceleration (IMU data), (3) ambient noise, light, and the vehicle's GPS location, and (4) music logs, including song features such as tempo. HARMONY is the first study that collects long-term naturalistic facial, physiological, and environmental data simultaneously. This paper summarizes HARMONY's goals, framework design, data collection and analysis, and the on-going and future research efforts. Through a presented case study, we first demonstrate the importance of longitudinal driver state sensing through using Kernel Density Estimation Methods. Second, we leverage the application of Bayesian Change Point detection methods to demonstrate how we can identify driver behaviors and responses to the environmental conditions by fusing psychophysiological information with features extracted from video streams.INDEX TERMS Naturalistic driving study, physiological sensing,driver state detection, shared-autonomy, contextual awareness, human-in-the-loop systems

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Section: ) Physical Sensorsmentioning

confidence: 99%

HARMONY: A Human-Centered Multimodal Driving Study in the Wild

et al. 2021

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“…Real-time stress was captured during the walk using an android smart watch (Huawei Watch2) capturing heart rate and walking speed. We used an in-house built app to collect 100 HZ Photoplethysmogram (PPG), 60 Hz accelerometer, 1 HZ sound amplitude, and 1/60 HZ GPS data (41). The PPG signal used to estimate HRV was processed using bandpass filters to reduce motion artifacts.…”

Section: Physiological Measuresmentioning

confidence: 99%

The Urban Built Environment, Walking and Mental Health Outcomes Among Older Adults: A Pilot Study

Roe

Mondschein

Neale

et al. 2020

Front. Public Health

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“…To date, Sensus has been used in multiple studies with more than 2,000 diverse participants for periods ranging from few days to few months. Similar generalized crowdsensing system has been developed by Boukhechba and Barnes (2020) to collect multimodal sensor data, such as heart rate and audio signals, from smartwatches.…”

Section: Generalized Architecture For Personalized Mental Health Intementioning

confidence: 99%

Leveraging Mobile Sensing and Machine Learning for Personalized Mental Health Care

Boukhechba¹,

Baglione²,

Barnes³

2020

Ergonomics in Design: The Quarterly of Human Factors Applicatio

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Mental illness is widespread in our society, yet remains difficult to treat due to challenges such as stigma and overburdened health care systems. New paradigms are needed for treating mental illness outside the practitioner’s office. We propose a framework to guide the design of mobile sensing systems for personalized mental health interventions. This framework guides researchers in constructing interventions from the ground up through four phases: sensor data collection, digital biomarker extraction, health state detection, and intervention deployment. We highlight how this framework advances research in personalized mHealth and address remaining challenges, such as ground truth fidelity and missing data.

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SWear: Sensing Using WEARables. Generalized Human Crowdsensing on Smartwatches

Cited by 25 publications

References 7 publications

HARMONY: A Human-Centered Multimodal Driving Study in the Wild

HARMONY: A Human-Centered Multimodal Driving Study in the Wild

The Urban Built Environment, Walking and Mental Health Outcomes Among Older Adults: A Pilot Study

Leveraging Mobile Sensing and Machine Learning for Personalized Mental Health Care

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