BodyBeat

Rahman, Tauhidur; Adams, Alexander T.; Mi, Zhang; Cherry, E. Colin; Zhou, Bo; Peng, Huaishu; Choudhury, Tanzeem

doi:10.1145/2594368.2594386

Cited by 134 publications

(15 citation statements)

References 21 publications

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“…Eating and drinking may also produce idiosyncratic sounds through chewing and swallowing. A microphone attached at the neck can classify sounds produced by eating and drinking with reasonable accuracy (Kalantarian et al 2015, Rahman et al 2014, Yatani & Truong 2012).…”

Section: Review Of Personal Sensing Researchmentioning

confidence: 99%

Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning

Mohr

Zhang

Schueller

2017

Annu. Rev. Clin. Psychol.

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616

411

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Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions.

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Section: Review Of Personal Sensing Researchmentioning

confidence: 99%

Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning

Mohr

Zhang

Schueller

2017

Annu. Rev. Clin. Psychol.

Self Cite

616

411

View full text Add to dashboard Cite

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“…In [10], Rahman et al present BodyBeat: a robust system for detecting human sounds. A similar work is presented by Yatani et al in [11].…”

Section: Related Workmentioning

confidence: 99%

A comparison of piezoelectric-based inertial sensing and audio-based detection of swallows

et al. 2016

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Prior research has shown a correlation between poor dietary habits and countless negative health outcomes such as heart disease, diabetes, and certain cancers. Automatic monitoring of food intake in an unobtrusive, wearable form-factor can encourage healthy dietary choices by enabling individuals to regulate their eating habits. This paper presents an objective comparison of two methods for digital dietary intake monitoring: piezoelectric swallow sensing by means of a smart necklace which monitors vibrations in the neck, and audio-based detection using a throat microphone. Data was collected from twenty subjects with ages ranging from 22 to 40 as they consumed a variety of foods using both devices. In experiment 1, we distinguished sandwich, chips, and water. In experiment 2, we distinguished nuts, chocolate, and a meat patty. F-Measures for the audio based approach were 91.3% and 88.5%, respectively. In the piezo-based approach, F-measures were significantly lower, at 75.3% and 79.4%, , respectively, though computational overhead and power dissipation were significantly less due to lower sampling rates associated with the piezoelectric approach.

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“…First, we filter the audio stream using an exponentially weighted moving average filter and scale the resulting data to the unit norm (L 2 normalization). To remove the background noise, we perform loudness normalization, which eliminates bias due to the variation of perceived loudness across different sound frames in each audio window [27]. These procedures remove most of the background noises.…”

Section: A-vowel Detection Modelmentioning

confidence: 99%

Lung Function Estimation from a Monosyllabic Voice Segment Captured Using Smartphones

Saleheen

Ahmed

Rahman

et al. 2020

22nd International Conference on Human-Computer Interaction With Mobile Devices and Services

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Chronic respiratory diseases refer to a group of lung diseases that affect the airways and cause difficulty in breathing. Respiratory diseases are one of the leading causes of death and negatively impact the patients' quality of life. Early detection and regular monitoring of lung functions might reduce the risk of death; however, lung function assessment requires the active supervision of a medical professional in a clinical setting. To make lung function tests more accessible and ubiquitous, researchers started leveraging mobile devices, which still require active supervision and demand extraneous effort from the user. In this work, we propose a convenient mobile-based approach that uses a monosyllabic voice segment called 'A-vowel' sound or 'Aaaa...' sound to estimate lung function. We conducted two studies (a lab study and an in-clinic study) with 201 participants to develop a detection model detecting 'Avowel' sound from other acoustic events and a prediction model to estimate the lung function using the detected A-vowel sound. Our study shows that A-vowel sounds can be detected with 93% accuracy, and A-vowel sounds can estimate lung functions with 7.4-11.35% mean absolute error. We also conducted a validation study * Pursued this work while interning with Samsung Research America.

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BodyBeat

Cited by 134 publications

References 21 publications

Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning

Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning

A comparison of piezoelectric-based inertial sensing and audio-based detection of swallows

Lung Function Estimation from a Monosyllabic Voice Segment Captured Using Smartphones

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