7,963 Hits in 7.8 sec

The use of ambulatory assessment in smoking cessation

Christine Vinci, Aaron Haslam, Cho Y. Lam, Santosh Kumar, David W. Wetter
2018 Addictive Behaviours  
The second half of the paper focuses on the use of wearable wireless sensors to collect data during the smoking cessation process.  ...  Finally, a discussion of some of the current challenges and limitations related to using sensor-based tools for smoking cessation are presented, along with suggestions for future research in this area.  ...  Prior to wearable sensors, researchers had to rely on participant self-report of smoking.  ... 
doi:10.1016/j.addbeh.2018.01.018 pmid:29398067 pmcid:PMC5964000 fatcat:q4ki2p6fb5g2hn3bjuxtia6yya

Intelligent Monitoring of Affective Factors Underlying Sport Performance by Means of Wearable and Mobile Technology

Carlos Bailon, Miguel Damas, Hector Pomares, Daniel Sanabria, Pandelis Perakakis, Carmen Goicoechea, Oresti Banos
2018 Proceedings (MDPI)  
However, traditional procedures to assess the affective state are limited to self-reported questionnaires within controlled settings, thus removing the impact of the context.  ...  This work proposes a multimodal, context-aware platform that combines the data acquired through smartphones and wearable sensors to assess the affective state of the athlete.  ...  Furthermore, the assessment of affective states have been almost exclusively based on athletes' self-reports through questionnaires.  ... 
doi:10.3390/proceedings2191202 fatcat:l7fint6kwbgrjbtzfxd7lrlqby

Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being [article]

Pablo Robles-Granda, Suwen Lin, Xian Wu, Sidney D'Mello, Gonzalo J. Martinez, Koustuv Saha, Kari Nies, Gloria Mark, Andrew T. Campbell, Munmun De Choudhury, Anind D. Dey, Julie Gregg (+6 others)
2020 arXiv   pre-print
Existing techniques use data extracted from questionnaires, sensors (wearable, computer, etc.), or other traits, to assess well-being and cognitive attributes of individuals.  ...  We design data mining techniques as benchmark and uses real noisy and incomplete data derived from wearable sensors to predict 19 constructs based on 12 standardized well-validated tests.  ...  We report predictions for individuals with at least 2 weeks of data.  ... 
arXiv:2006.08364v1 fatcat:ibk7rfug2bhipmkcae7jpzbgfy

Wearables to Fight COVID-19: From Symptom Tracking to Contact Tracing

Oliver Amft, Luis Lopera, Paul Lukowicz, Sizhen Bian, Paul Burggraf, Oliver Amft, Kristof Van Laerhoven
2020 IEEE pervasive computing  
Thryve & THE NEW CORONAVIRUS pandemic has promoted the new development of mobile and wearable computing in unprecedented ways.  ...  We discuss how on-body devices can help to fight the pandemic and may stay as a toolset to effectively deal with infectious diseases in the future.  ...  Manual symptom reporting, either structured through questionnaires or unstructured through social network reporting, has been used as a data source for more than a decade. 8, 9, 15 The wearable sensors  ... 
doi:10.1109/mprv.2020.3021321 fatcat:kwdohm5djbflrmnkwluupep4ha

Harnessing consumer smartphone and wearable sensors for clinical cancer research

Carissa A. Low
2020 npj Digital Medicine  
As smartphones and consumer wearable devices become more ubiquitous, there is a growing opportunity to capture rich mobile sensor data continuously, passively, and in real-world settings with minimal burden  ...  Leveraging digital technology has the potential to enrich scientific understanding of how cancer and its treatment affect patient lives, to use this understanding to offer more timely and personalized  ...  Compared with self-report assessments of physical activity, wearable devices offer more objective and fine-grained activity data 14 .  ... 
doi:10.1038/s41746-020-00351-x pmid:33134557 pmcid:PMC7591557 fatcat:i6hlpfezsbamtbfjd4spel6gau

Self-Reported and Performance-Based Outcome Measures Estimation Using Wearables After Unilateral Total Knee Arthroplasty

Ik-Hyun Youn, Todd Leutzinger, Jong-Hoon Youn, Joseph A. Zeni, Brian A. Knarr
2020 Frontiers in Sports and Active Living  
The findings demonstrate that wearable sensor-based gait analysis may be able to help predict clinical measures in individuals after unilateral knee treatment.  ...  The purpose of this study was to determine if inertial gait variables from wearable sensors effectively estimate the questionnaire, performance (6-min walk test, timed up and go, and 30-s chair stand test  ...  , and Na and Buchanan (2020) validated the use of wearable technology against self-reported measures for stability in individuals with knee OA.  ... 
doi:10.3389/fspor.2020.569932 pmid:33345128 pmcid:PMC7739603 fatcat:jbn65iy6pvhv3dvzyzujw3fmvu

Home‐based monitoring of falls using wearable sensors in Parkinson's disease

Ana Lígia Silva de Lima, Tine Smits, Sirwan K. L. Darweesh, Giulio Valenti, Mladen Milosevic, Marten Pijl, Heribert Baldus, Nienke M Vries, Marjan J. Meinders, Bastiaan R. Bloem
2019 Movement Disorders  
We matched all 2063 elderly individuals with self-reported PD to 2063 elderly individuals without PD based on age, gender, comorbidity, and living conditions.  ...  We analyzed fall events collected at home via a wearable sensor.  ...  Some promising examples of the use of wearable sensors to quantify fall events in controlled settings and free-living environments were reported in PD. 17, 18 Moreover, sensors can be used together with  ... 
doi:10.1002/mds.27830 pmid:31449705 fatcat:na6du5c37fe4dlf7hqhhnvznkq

Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data

Allison Shapiro, Nicole Marinsek, Ieuan Clay, Ben Bradshaw, Ernesto Ramirez, Jae Min, Andrew Trister, Yuedong Wang, Tim Althoff, Luca Foschini
2020 Patterns  
Compared with self-reported diagnosed flu cases from the same time frame (n = 426, 85 with wearable data) or pre-pandemic (n = 6,270, 1,265 with wearable data), COVID-19 patients reported a distinct symptom  ...  We examined person-generated health data (PGHD), consisting of survey and commercial wearable data from individuals' everyday lives, for 230 people who reported a COVID-19 diagnosis between March 30, 2020  ...  Differences in self-reported symptoms are supported by data from wearable sensors.  ... 
doi:10.1016/j.patter.2020.100188 pmid:33506230 pmcid:PMC7815963 fatcat:umbuhhddefbongluasfthke2li

A Survey of Passive Sensing in the Workplace [article]

Subigya Nepal, Gonzalo J. Martinez, Shayan Mirjafari, Koustuv Saha, Vedant Das Swain, Xuhai Xu, Pino G. Audia, Munmun De Choudhury, Anind K. Dey, Aaron Striegel, Andrew T. Campbell
2022 arXiv   pre-print
New passive sensing technology is emerging capable of assessing human behavior with the goal of promoting better cognitive and physical capabilities at work.  ...  Many of the papers discussed are still in an exploratory phase, where datadriven research is advancing what sensor streams and models can best capture dimensions of the workplace and workforce.  ...  The ground truth for awakeness and focus along with stress were obtained as self reports from the participants.  ... 
arXiv:2201.03074v1 fatcat:6xupuqnd7rgezbiopvxjg4dhey

Poster: DyMand -- An Open-Source Mobile and Wearable System for Assessing Couples' Dyadic Management of Chronic Diseases

George Boateng, Prabhakaran Santhanam, Janina Lüscher, Urte Scholz, Tobias Kowatsch
2019 The 25th Annual International Conference on Mobile Computing and Networking - MobiCom '19  
Married adults share illness management with spouses and it involves social support and common dyadic coping (CDC).  ...  In this ongoing work, we describe the development of DyMand, a novel open-source mobile and wearable system for ambulatory assessment of couples' dyadic management of chronic diseases.  ...  At the end of the testing, we had collected 900+ five-minute sensor data along with corresponding triggered self-reports.  ... 
doi:10.1145/3300061.3343399 dblp:conf/mobicom/BoatengSLSK19 fatcat:y2w6m2bwnfbrle47q5v3muozj4

Pulse Rate Measurement During Sleep Using Wearable Sensors, and its Correlation with the Menstrual Cycle Phases, A Prospective Observational Study

Mohaned Shilaih, Valérie de Clerck, Lisa Falco, Florian Kübler, Brigitte Leeners
2017 Scientific Reports  
In addition, wearable PPG technology has been established to be an accurate estimation of PR in healthy individuals during rest and sleep 6 .  ...  Participants measured PR during sleep using wrist-worn bracelets with photoplethysmographic sensors. Ovulation day was estimated with "Clearblue Digital-Ovulation-urine test".  ...  The inclusion criteria were an age between 18-42 and a self-reported regular cycle in the six months preceding the study.  ... 
doi:10.1038/s41598-017-01433-9 pmid:28465583 pmcid:PMC5431053 fatcat:w4frlmmep5cfxi2ula762y2324

Estimating wearable motion sensor performance from personal biomechanical models and sensor data synthesis

Adrian Derungs, Oliver Amft
2020 Scientific Reports  
Our analysis shows that running speed affects gait marker estimation performance.  ...  Our methodology enables wearable designers and algorithm developers to rapidly analyse the design options and create personalised systems where needed, e.g. for patients with movement disorders.  ...  Especially for those individual movement compensation, personalised wearable systems with tailored sensor positions are of interest.Figure 7shows nRMSE maps for simulated sensor positions at the affected  ... 
doi:10.1038/s41598-020-68225-6 pmid:32651412 fatcat:azefho52gzfqln4gtsh6k5ftwi

Wearable bluetooth sensors for capturing relational variables and temporal variability in relationships: A construct validation study

James G. Matusik, Ralph Heidl, John R. Hollenbeck, Andrew Yu, Hun Whee Lee, Michael Howe
2018 Journal of Applied Psychology  
self-reports of time spent with different groups where there is a "ground truth" criterion.  ...  respect to traditional self-reports of relational data, (b) display discriminant validity with respect to qualitative differences in the nature of alternative relationships (i.e., advice versus friendship  ...  For the most part, research on relational variables has relied on cross-sectional or before/after approaches captured with self-reported data or direct observations from others, both of which are affected  ... 
doi:10.1037/apl0000334 pmid:30070543 fatcat:jit4e2fuzrbjxjd77giq57lzna

Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones

Akane Sano, Andrew J. Phillips, Amy Z. Yu, Andrew W. McHill, Sara Taylor, Natasha Jaques, Charles A. Czeisler, Elizabeth B. Klerman, Rosalind W. Picard
2015 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)  
What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition?  ...  We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS  ...  Researchers have used wearable sensors and/or mobile phone data to understand factors such as personality type [1] , mood [2, 3] , sleep [3, 4, 5] and self-reported stress [6, 7] .  ... 
doi:10.1109/bsn.2015.7299420 pmid:28516162 pmcid:PMC5431072 fatcat:znx7fwqwonbdzlier6srbuzgwq

Smartphone-Based Tracking of Sleep in Depression, Anxiety, and Psychotic Disorders

Talayeh Aledavood, John Torous, Ana Maria Triana Hoyos, John A. Naslund, Jukka-Pekka Onnela, Matcheri Keshavan
2019 Current Psychiatry Reports  
We review uses of smartphones and wearable devices for sleep research in patients with these conditions.  ...  Human and Animal Rights and Informed Consent This article does not contain any studies with human or animal subjects performed by any of the authors.  ...  Compliance with Ethical Standards Conflict of Interest Talayeh Aledavood, Ana Maria Triana Hoyos, John A. Naslund, and Matcheri Keshavan each declare no potential conflict of interest.  ... 
doi:10.1007/s11920-019-1043-y pmid:31161412 pmcid:PMC6546650 fatcat:xfmoo76slzandbfndyrme3odua
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