research-article

Open access

MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech

Authors:

Margaret Danilovich,

David E. Conroy,

Hernisa Kacorri,

Eun Kyoung ChoeAuthors Info & Claims

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Article No.: 416, Pages 1 - 21

https://doi.org/10.1145/3491102.3517457

Published: 29 April 2022 Publication History

All formats PDF

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech

Pages 1 - 21

Abstract
Supplemental Material
References

Abstract

Current activity tracking technologies are largely trained on younger adults’ data, which can lead to solutions that are not well-suited for older adults. To build activity trackers for older adults, it is crucial to collect training data with them. To this end, we examine the feasibility and challenges with older adults in collecting activity labels by leveraging speech. Specifically, we built MyMove, a speech-based smartwatch app to facilitate the in-situ labeling with a low capture burden. We conducted a 7-day deployment study, where 13 older adults collected their activity labels and smartwatch sensor data, while wearing a thigh-worn activity monitor. Participants were highly engaged, capturing 1,224 verbal reports in total. We extracted 1,885 activities with corresponding effort level and timespan, and examined the usefulness of these reports as activity labels. We discuss the implications of our approach and the collected dataset in supporting older adults through personalized activity tracking technologies.

Supplemental Material

MP4 File

Talk Video

Transcript for: Talk Video

MP4 File

Video Figure

Transcript for: Video Figure

ZIP File

Supplemental Materials

Download
24.69 MB

References

[1]

Barbara E. Ainsworth, William L. Haskell, Arthur S. Leon, David R. Jacobs, Henry J. Montoye, James F. Sallis, and Ralph S. Paffenbarger. 1993. Compendium of Physical Activities: Classification of Energy Costs of Human Physical Activities. Medicine & Science in Sports & Exercise 25, 1 (1993), 71–80. https://journals.lww.com/acsm-msse/Fulltext/1993/01000/Compendium_of_Physical_Activities__classification.11.aspx

[2]

American College of Sports Medicine. 2021. ACSM’s Guidelines for Exercise Testing and Prescription (11 ed.). Lippincott Williams & Wilkins, Philadelphia, PA, USA. 548 pages.

[3]

Michael J. Annear, Grant Cushman, and Bob Gidlow. 2009. Leisure time physical activity differences among older adults from diverse socioeconomic neighborhoods. Health & Place 15, 2 (2009), 482–490. https://doi.org/10.1016/j.healthplace.2008.09.005

[4]

Shiri Azenkot and Nicole B. Lee. 2013. Exploring the Use of Speech Input by Blind People on Mobile Devices. In Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility(Bellevue, Washington) (ASSETS ’13). Association for Computing Machinery, New York, NY, USA, Article 11, 8 pages. https://doi.org/10.1145/2513383.2513440

Digital Library

[5]

Maxim Bakaev. 2008. Fitts’ Law for Older Adults: Considering a Factor of Age. In Proceedings of the VIII Brazilian Symposium on Human Factors in Computing Systems (Porto Alegre, RS, Brazil) (IHC ’08). Sociedade Brasileira de Computação, BRA, 260–263.

[6]

Paul B. Baltes and Margret M. Baltes. 1990. Successful Aging: Perspectives from the Behavioral Sciences. Cambridge University Press, Cambridge, United Kingdom. https://doi.org/10.1017/CBO9780511665684

[7]

Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural Language Processing with Python(1st ed.). O’Reilly Media, Inc., Sebastopol, CA, USA.

[8]

Niall Bolger, Angelina Davis, and Eshkol Rafaeli. 2003. Diary Methods: Capturing Life as It Is Lived. Annual Review of Psychology 54, 1 (2003), 579–616. https://doi.org/10.1146/annurev.psych.54.101601.145030

[9]

Marc Bonnefoy, Sylvie Normand, Christiane Pachiaudi, Jean RenÃ© Lacour, Martine Laville, and Tomasz Kostka. 2001. Simultaneous Validation of Ten Physical Activity Questionnaires in Older Men: A Doubly Labeled Water Study. Journal of the American Geriatrics Society 49, 1 (Jan. 2001), 28–35. https://doi.org/10.1046/j.1532-5415.2001.49006.x

[10]

Gunnar Borg. 1990. Psychophysical Scaling with Applications in Physical Work and the Perception of Exertion. Scandinavian Journal of Work, Environment and Health 16, SUPPL. 1(1990), 55–58. https://doi.org/10.5271/sjweh.1815

[11]

Gunnar Borg. 1998. Borg’s Perceived Exertion and Pain Scales. Human Kinetics, USA.

[12]

Gerry R. Boss and J. Edwin Seegmiller. 1981. Age-Related Physiological Changes and Their Clinical Significance. The Western Journal of Medicine 135, 6 (Dec. 1981), 434–40. http://www.ncbi.nlm.nih.gov/ /7336713http://www.central.nih.gov/articlerender.fcgi?artid=PMC1273316

[13]

Lawrence R. Brawley, W. Jack Rejeski, and Abby C. King. 2003. Promoting Physical Activity for Older Adults: the Challenges for Changing Behavior. American Journal of Preventive Medicine 25, 3 Suppl 2 (Oct. 2003), 172–83. https://doi.org/10.1016/s0749-3797(03)00182-x

[14]

Matthew P. Buman, Eric B. Hekler, William L. Haskell, Leslie Pruitt, Terry L. Conway, Kelli L. Cain, James F. Sallis, Brian E. Saelens, Lawrence D. Frank, and Abby C. King. 2010. Objective Light-Intensity Physical Activity Associations With Rated Health in Older Adults. American Journal of Epidemiology 172, 10 (Nov. 2010), 1155–1165. https://doi.org/10.1093/aje/kwq249

[15]

Eli Carmeli, Hagar Patish, and Raymond Coleman. 2003. The Aging Hand. The Journals of Gerontology: Series A 58, 2 (Feb. 2003), M146–M152. https://doi.org/10.1093/gerona/58.2.M146

[16]

Marta E. Cecchinato, Anna L. Cox, and Jon Bird. 2017. Always On(Line)? User Experience of Smartwatches and Their Role within Multi-Device Ecologies. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, 3557–3568. https://doi.org/10.1145/3025453.3025538

Digital Library

[17]

Barbara L. Chalfonte, Robert S. Fish, and Robert E. Kraut. 1991. Expressive Richness: a Comparison of Speech and Text as Media for Revision. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(CHI ’91). Association for Computing Machinery, New York, NY, USA, 21–26. https://doi.org/10.1145/108844.108848

Digital Library

[18]

Ranganathan Chandrasekaran, Vipanchi Katthula, and Evangelos Moustakas. 2020. Patterns of Use and Key Predictors for the Use of Wearable Health Care Devices by US Adults: Insights from a National Survey. Journal of Medical Internet Research 22, 10 (Oct. 2020), e22443. https://doi.org/10.2196/22443

[19]

Liu Chen and Meysam Asgari. 2021. Refining Automatic Speech Recognition System for Older Adults. In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Washington, DC, USA, 7003–7007. https://doi.org/10.1109/ICASSP39728.2021.9414207

[20]

Wojtek J. Chodzko-Zajko, David N. Proctor, Maria A. Fiatarone Singh, Christopher T. Minson, Claudio R. Nigg, George J. Salem, and James S. Skinner. 2009. Exercise and Physical Activity for Older Adults. Medicine & science in sports & exercise 41, 7 (2009), 1510–1530.

[21]

Pak-Kwong Chung, Yanan Zhao, Jing-Dong Liu, and Binh Quach. 2015. A Brief Note on the Validity and Reliability of the Rating of Perceived Exertion Scale in Monitoring Exercise Intensity among Chinese Older Adults in Hong Kong. Perceptual and Motor Skills 121, 3 (Dec. 2015), 805–809. https://doi.org/10.2466/29.PMS.121c24x8

[22]

Leigh Clark, Philip Doyle, Diego Garaialde, Emer Gilmartin, Stephan Schlögl, Jens Edlund, Matthew Aylett, João Cabral, Cosmin Munteanu, Justin Edwards, 2019. The State of Speech in HCI: Trends, Themes and Challenges. Interacting with Computers 31, 4 (2019), 349–371. https://doi.org/10.1093/iwc/iwz016

[23]

Mark G. Davis, Kenneth R. Fox, Melvyn Hillsdon, Debbie J. Sharp, Jo C. Coulson, and Janice L. Thompson. 2011. Objectively Measured Physical Activity in a Diverse Sample of Older Urban UK Adults. Medicine & Science in Sports & Exercise 43, 4 (April 2011), 647–654. https://doi.org/10.1249/MSS.0b013e3181f36196

[24]

Luigi De Russis and Fulvio Corno. 2019. On the impact of dysarthric speech on contemporary ASR cloud platforms. Journal of Reliable Intelligent Environments 5, 3(2019), 163–172. https://doi.org/10.1007/s40860-019-00085-y

[25]

Utkarsh Dwivedi, Jaina Gandhi, Raj Parikh, Merijke Coenraad, Elizabeth Bonsignore, and Hernisa Kacorri. 2021. Exploring Machine Teaching with Children. In 2021 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). IEEE, IEEE, Washington, DC, USA, 11 pages.

[26]

Elin Ekblom-Bak, Björn Ekblom, Max Vikström, Ulf de Faire, and Mai-Lis Hellénius. 2014. The Importance of Non-Exercise Physical Activity for Cardiovascular Health and Longevity. British Journal of Sports Medicine 48, 3 (Feb. 2014), 233–238. https://doi.org/10.1136/bjsports-2012-092038

[27]

Chloe Fan, Jodi Forlizzi, and Anind Dey. 2012. Considerations for Technology that Support Physical Activity by Older Adults. In Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility - ASSETS ’12. ACM Press, New York, New York, USA, 33. https://doi.org/10.1145/2384916.2384923

Digital Library

[28]

Sabahat Fatima. 2021. Activity Recognition in Older Adults with Training Data from Younger Adults: Preliminary Results on in VivoSmartwatch Sensor Data. In Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility(ASSETS ’21). ACM, New York, NY, USA, 26:1–26:8. https://doi.org/10.1145/3441852.3476475

Digital Library

[29]

Mireia Fernández-Ardèvol and Andrea Rosales. 2017. My Interests, My Activities: Learning from an Intergenerational Comparison of Smartwatch Use. In Human Aspects of IT for the Aged Population. Applications, Services and Contexts, Jia Zhou and Gavriel Salvendy (Eds.). Springer International Publishing, Cham, 114–129. https://doi.org/10.1007/978-3-319-58536-9_10

[30]

Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In Proceedings of the 34th International Conference on Machine Learning (ICML ’17)(Proceedings of Machine Learning Research, Vol. 70). PMLR, USA, 1126–1135. https://proceedings.mlr.press/v70/finn17a.html

[31]

F. Foerster, M. Smeja, and J. Fahrenberg. 1999. Detection of Posture and Motion by Accelerometry: a Validation study in Ambulatory Monitoring. Computers in Human Behavior 15, 5 (Sept. 1999), 571–583. https://doi.org/10.1016/S0747-5632(99)00037-0

[32]

David P. French, Ellinor K. Olander, Anna Chisholm, and Jennifer Mc Sharry. 2014. Which Behaviour Change Techniques are Most Effective at Increasing Older Adults’ Self-Efficacy and Physical Activity Behaviour? A Systematic Review. Annals of behavioral medicine 48, 2 (2014), 225–234.

[33]

Paul A. Gardiner, Bronwyn K. Clark, Genevieve N. Healy, Elizabeth G. Eakin, Elisabeth A.H. Winkler, and Neville Owen. 2011. Measuring Older Adults’ Sedentary Time: Reliability, Validity, and Responsiveness. Medicine & Science in Sports & Exercise 43, 11 (Nov. 2011), 2127–2133. https://doi.org/10.1249/MSS.0b013e31821b94f7

[34]

Kathrin Gerling, Mo Ray, Vero Vanden Abeele, and Adam B. Evans. 2020. Critical Reflections on Technology to Support Physical Activity among Older Adults: An Exploration of Leading HCI Venues. ACM Transactions on Accessible Computing 13, 1 (April 2020), 1–23. https://doi.org/10.1145/3374660

Digital Library

[35]

Abraham T. Glasser, Kesavan R. Kushalnagar, and Raja S. Kushalnagar. 2017. Feasibility of Using Automatic Speech Recognition with Voices of Deaf and Hard-of-Hearing Individuals. In Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility (Baltimore, Maryland, USA) (ASSETS ’17). Association for Computing Machinery, New York, NY, USA, 373–374. https://doi.org/10.1145/3132525.3134819

Digital Library

[36]

Google. 2021. Activity Recognition API. Retrieved Sep 09, 2021 from https://developers.google.com/location-context/activity-recognition

[37]

Google. 2021. Protocol Buffers. Retrieved Sep 09, 2021 from https://developers.google.com/protocol-buffers

[38]

Google. 2021. Speech-to-Text: Automatic Speech Recognition | Google Cloud. Retrieved Sep 09, 2021 from https://cloud.google.com/speech-to-text

[39]

E. Gorman, H. M. Hanson, P. H. Yang, K. M. Khan, T. Liu-Ambrose, and M. C. Ashe. 2014. Accelerometry Analysis of Physical Activity and Sedentary Behavior in Older Adults: a Systematic Review and Data Analysis. European Review of Aging and Physical Activity 11, 1 (April 2014), 35–49. https://doi.org/10.1007/s11556-013-0132-x

[40]

Jordan R. Green, Robert L. MacDonald, Pan-Pan Jiang, Julie Cattiau, Rus Heywood, Richard Cave, Katie Seaver, Marilyn A. Ladewig, Jimmy Tobin, Michael P. Brenner, Philip C. Nelson, and Katrin Tomanek. 2021. Automatic Speech Recognition of Disordered Speech: Personalized Models Outperforming Human Listeners on Short Phrases. In Interspeech 2021. ISCA, Winona, MN, USA, 4778–4782. https://doi.org/10.21437/Interspeech.2021-1384

[41]

Mark Hamer, Cesar de Oliveira, and Panayotes Demakakos. 2014. Non-Exercise Physical Activity and Survival: English Longitudinal Study of Ageing. American Journal of Preventive Medicine 47, 4 (Oct. 2014), 452–460. https://doi.org/10.1016/j.amepre.2014.05.044

[42]

Gabriella M. Harari, Nicholas D. Lane, Rui Wang, Benjamin S. Crosier, Andrew T. Campbell, and Samuel D. Gosling. 2016. Using Smartphones to Collect Behavioral Data in Psychological Science. Perspectives on Psychological Science 11, 6 (Nov. 2016), 838–854. https://doi.org/10.1177/1745691616650285 arxiv:15334406

[43]

Juliet Harvey, Sebastien Chastin, and Dawn Skelton. 2013. Prevalence of Sedentary Behavior in Older Adults: A Systematic Review. International Journal of Environmental Research and Public Health 10, 12 (Dec. 2013), 6645–6661. https://doi.org/10.3390/ijerph10126645

[44]

Andrea L. Hergenroeder, Bethany Barone Gibbs, Mary P. Kotlarczyk, Robert J. Kowalsky, Subashan Perera, and Jennifer S. Brach. 2018. Accuracy of Objective Physical Activity Monitors in Measuring Steps in Older Adults. Gerontology and Geriatric Medicine 4 (Jan. 2018), 233372141878112. https://doi.org/10.1177/2333721418781126

[45]

Javier Hernandez, Daniel McDuff, Christian Infante, Pattie Maes, Karen Quigley, and Rosalind Picard. 2016. Wearable ESM: Differences in the Experience Sampling Method across Wearable Devices. In Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, New York, NY, USA, 195–205. https://doi.org/10.1145/2935334.2935340

Digital Library

[46]

Jonggi Hong, Kyungjun Lee, June Xu, and Hernisa Kacorri. 2020. Crowdsourcing the Perception of Machine Teaching. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). ACM, New York, NY, USA, 1–14. https://doi.org/10.1145/3313831.3376428

Digital Library

[47]

Jonggi Hong, Christine Vaing, Hernisa Kacorri, and Leah Findlater. 2020. Reviewing Speech Input with Audio: Differences between Blind and Sighted Users. ACM Trans. Access. Comput. 13, 1, Article 2 (April 2020), 28 pages. https://doi.org/10.1145/3382039

Digital Library

[48]

Enamul Hoque, Robert F. Dickerson, and John A. Stankovic. 2014. Vocal-Diary: A Voice Command based Ground Truth Collection System for Activity Recognition. In Proceedings of the Wireless Health 2014 on National Institutes of Health. ACM, New York, NY, USA, 1–6. https://doi.org/10.1145/2668883.2669587

Digital Library

[49]

Ann L. Horgas, Hans-Ulrich Wilms, and Margret M. Baltes. 1998. Daily Life in Very Old Age: Everyday Activities as Expression of Successful Living. The Gerontologist 38, 5 (Oct. 1998), 556–568. https://doi.org/10.1093/geront/38.5.556

[50]

Xueliang Huo, Hangue Park, and Maysam Ghovanloo. 2012. Dual-Mode Tongue Drive System: Using Speech and Tongue Motion to Improve Computer Access for People with Disabilities. In Proceedings of the Conference on Wireless Health(San Diego, California) (WH ’12). Association for Computing Machinery, New York, NY, USA, Article 6, 8 pages. https://doi.org/10.1145/2448096.2448102

Digital Library

[51]

Stephen Intille, Caitlin Haynes, Dharam Maniar, Aditya Ponnada, and Justin Manjourides. 2016. μEMA: Microinteraction-Based Ecological Momentary Assessment (EMA) Using a Smartwatch. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Heidelberg, Germany) (UbiComp ’16). ACM, New York, NY, USA, 1124–1128. https://doi.org/10.1145/2971648.2971717

Digital Library

[52]

David R. Jacobs, Barbara E. Ainsworth, Terryl J. Hartman, and Arthur S. Leon. 1993. A Simultaneous Evaluation of 10 Commonly Used Physical Activity Questionnaires. Medicine & Science in Sports & Exercise 25, 1 (1993), 81–91. https://journals.lww.com/acsm-msse/Fulltext/1993/01000/A_simultaneous_evaluation_of_10_commonly_used.12.aspx

[53]

JetBrains s.r.o.2021. Kotlin Programming Language. Retrieved Sep 09, 2021 from https://kotlinlang.org/

[54]

Hernisa Kacorri. 2017. Teachable Machines for Accessibility. SIGACCESS Access. Comput. 119 (Nov. 2017), 10–18. https://doi.org/10.1145/3167902.3167904

Digital Library

[55]

Man-Yee Kan, Muzhi Zhou, Daniela Veronica Negraia, Kamila Kolpashnikova, Ekaterina Hertog, Shohei Yoda, and Jiweon Jun. 2021. How do Older Adults Spend Their Time? Gender Gaps and Educational Gradients in Time Use in East Asian and Western Countries. Journal of Population Ageing 14 (2021), 537–562. https://doi.org/10.1007/s12062-021-09345-3

[56]

Sidney Katz, Amasa B. Ford, Roland W. Moskowitz, Beverly A. Jackson, and Marjorie W. Jaffe. 1963. Studies of Illness in the Aged: The Index of ADL: A Standardized Measure of Biological and Psychosocial Function. Journal of the American Medical Association 185, 12 (1963), 914–919. https://doi.org/10.1001/jama.1963.03060120024016

[57]

Martijn G Kersloot, Florentien J P van Putten, Ameen Abu-Hanna, Ronald Cornet, and Derk L Arts. 2020. Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies. Journal of biomedical semantics 11, 1 (11 2020), 14–14. https://doi.org/10.1186/s13326-020-00231-z

[58]

Shreya Khare, Ashish Mittal, Anuj Diwan, Sunita Sarawagi, Preethi Jyothi, and Samarth Bharadwaj. 2021. Low Resource ASR: The Surprising Effectiveness of High Resource Transliteration. In Interspeech 2021. ISCA, Winona, MN, USA, 1529–1533. https://doi.org/10.21437/Interspeech.2021-2062

[59]

Matin Kheirkhahan, Sanjay Nair, Anis Davoudi, Parisa Rashidi, Amal A. Wanigatunga, Duane B. Corbett, Tonatiuh Mendoza, Todd M. Manini, and Sanjay Ranka. 2019. A Smartwatch-Based Framework for Real-Time and Online Assessment and Mobility Monitoring. Journal of Biomedical Informatics 89, November 2018 (2019), 29–40. https://doi.org/10.1016/j.jbi.2018.11.003

[60]

Young-Ho Kim, Bongshin Lee, Arjun Srinivasan, and Eun Kyoung Choe. 2021. Data@Hand: Fostering Visual Exploration of Personal Data On Smartphones Leveraging Speech and Touch Interaction. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). ACM, New York, NY, USA, Article 462, 17 pages. https://doi.org/10.1145/3411764.3445421

Digital Library

[61]

Pascal van Kooten. 2021. Contractions Python Library. Retrieved Sep 09, 2021 from https://github.com/kootenpv/contractions

[62]

Sarah Kozey-Keadle, Amanda Libertine, Kate Lyden, John Staudenmayer, and Patty S. Freedson. 2011. Validation of Wearable Monitors for Assessing Sedentary Behavior. Medicine and Science in Sports and Exercise 43, 8 (2011), 1561–1567. https://doi.org/10.1249/MSS.0b013e31820ce174

[63]

Oscar D. Lara and Miguel A. Labrador. 2013. A Survey on Human Activity Recognition using Wearable Sensors. IEEE Communications Surveys & Tutorials 15, 3 (2013), 1192–1209. https://doi.org/10.1109/SURV.2012.110112.00192

[64]

Reed Larson and Mihaly Csikszentmihalyi. 2014. The Experience Sampling Method. In Flow and the foundations of positive psychology. Springer, Berlin/Heidelberg, Germany, 21–34.

[65]

M. Powell Lawton and Elaine M. Brody. 1969. Assessment of Older People: Self-Maintaining and Instrumental Activities of Daily Living. The Gerontologist 9, 3 Part 1 (Sept. 1969), 179–186. https://doi.org/10.1093/geront/9.3_Part_1.179

[66]

I-Min Lee and Eric J. Shiroma. 2014. Using Accelerometers to Measure Physical Activity in Large- Scale Epidemiologic Studies: Issues and Challenges. British Journal of Sports Medicine 48, 3 (Feb. 2014), 197–201. https://doi.org/10.1136/bjsports-2013-093154

[67]

I-Min Lee, Eric J. Shiroma, Masamitsu Kamada, David R. Bassett, Charles E. Matthews, and Julie E. Buring. 2019. Association of Step Volume and Intensity With All-Cause Mortality in Older Women. JAMA Internal Medicine 179, 8 (Aug. 2019), 1105. https://doi.org/10.1001/jamainternmed.2019.0899

[68]

Kyungjun Lee, Jonggi Hong, Simone Pimento, Ebrima Jarjue, and Hernisa Kacorri. 2019. Revisiting Blind Photography in the Context of Teachable Object Recognizers. In The 21st International ACM SIGACCESS Conference on Computers and Accessibility(Pittsburgh, PA, USA) (ASSETS ’19). ACM, New York, NY, USA, 83–95. https://doi.org/10.1145/3308561.3353799

Digital Library

[69]

Russell V. Lenth, Paul Buerkner, Maxime Herve, Jonathon Love, Hannes Riebl, and Henrik Singmann. 2021. emmeans: Estimated Marginal Means, aka Least-Squares Means. CRAN. https://CRAN.R-project.org/package=emmeans

[70]

Chenglin Li, Di Niu, Bei Jiang, Xiao Zuo, and Jianming Yang. 2021. Meta-HAR: Federated Representation Learning for Human Activity Recognition. In Proceedings of the Web Conference 2021 (Ljubljana, Slovenia) (WWW ’21). ACM, New York, NY, USA, 912–922. https://doi.org/10.1145/3442381.3450006

Digital Library

[71]

Yuhan Luo, Young-Ho Kim, Bongshin Lee, Naeemul Hassan, and Eun Kyoung Choe. 2021. FoodScrap: Promoting Rich Data Capture and Reflective Food Journaling Through Speech Input. In Designing Interactive Systems Conference 2021(Virtual Event, USA) (DIS ’21). ACM, New York, NY, USA, 606–618. https://doi.org/10.1145/3461778.3462074

Digital Library

[72]

Yuhan Luo, Bongshin Lee, and Eun Kyoung Choe. 2020. TandemTrack: Shaping Consistent Exercise Experience by Complementing a Mobile App with a Smart Speaker. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). ACM, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376616

Digital Library

[73]

Kate Lyden, Sarah Keadle, John Staudenmayer, and Patty Freedson. 2012. Validity of Two Wearable Monitors to Estimate Breaks from Sedentary Time. Medicine and science in sports and exercise 44 (05 2012), 2243–52. https://doi.org/10.1249/MSS.0b013e318260c477

[74]

Robert L. MacDonald, Pan-Pan Jiang, Julie Cattiau, Rus Heywood, Richard Cave, Katie Seaver, Marilyn A. Ladewig, Jimmy Tobin, Michael P. Brenner, Philip C. Nelson, Jordan R. Green, and Katrin Tomanek. 2021. Disordered Speech Data Collection: Lessons Learned at 1 Million Utterances from Project Euphonia. In Interspeech 2021. ISCA, Winona, MN, USA, 4833–4837. https://doi.org/10.21437/Interspeech.2021-697

[75]

Soultana Macridis, Nora Johnston, Steven Johnson, and Jeff K Vallance. 2018. Consumer Physical Activity Tracking Device Ownership and Use Among a Population-Based Sample of Adults. PloS one 13, 1 (2018), e0189298.

[76]

Meethu Malu, Pramod Chundury, and Leah Findlater. 2018. Exploring Accessible Smartwatch Interactions for People with Upper Body Motor Impairments. Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3173574.3174062

Digital Library

[77]

Todd Matthew Manini, Tonatiuh Mendoza, Manoj Battula, Anis Davoudi, Matin Kheirkhahan, Mary Ellen Young, Eric Weber, Roger Benton Fillingim, and Parisa Rashidi. 2019. Perception of Older Adults Toward Smartwatch Technology for Assessing Pain and Related Patient-Reported Outcomes: Pilot Study. JMIR mHealth and uHealth 7, 3 (March 2019), e10044. https://doi.org/10.2196/10044

[78]

Justin Mccarthy. 2021. One in Five U.S. Adults Use Health Apps, Wearable Trackers | Gallup. Retrieved Sep 09, 2021 from https://news.gallup.com/poll/269096/one-five-adults-health-apps-wearable-trackers.aspx

[79]

Kryss McKenna, Kieran Broome, and Jacki Liddle. 2007. What Older People Do: Time Use and Exploring the Link Between Role Participation and Life Satisfaction in People Aged 65 Years and Over. Australian Occupational Therapy Journal 54, 4 (March 2007), 273–284. https://doi.org/10.1111/j.1440-1630.2007.00642.x

[80]

Kathryn Mercer, Melissa Li, Lora Giangregorio, Catherine Burns, and Kelly Grindrod. 2016. Behavior change techniques present in wearable activity trackers: a critical analysis. JMIR mHealth and uHealth 4, 2 (2016), e4461.

[81]

Microsoft. 2021. Cognitive Speech Services | Microsoft Azure. Retrieved Sep 09, 2021 from https://azure.microsoft.com/en-us/services/cognitive-services/speech-services/

[82]

Md Abu Sayeed Mondol, Ifat A. Emi, Sirat Samyoun, M. Arif Imtiazur Rahman, and John A. Stankovic. 2018. WaDa: An Android Smart Watch App for Sensor Data Collection. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (Singapore, Singapore) (UbiComp ’18). ACM, New York, NY, USA, 404–407. https://doi.org/10.1145/3267305.3267660

Digital Library

[83]

Miriam S. Moss and M. Powell Lawton. 1982. Time Budgets of Older People: a Window on Four Lifestyles. Journal of Gerontology 37, 1 (Jan. 1982), 115–123. https://doi.org/10.1093/geronj/37.1.115

[84]

Lilian Genaro Motti, Nadine Vigouroux, and Philippe Gorce. 2013. Interaction Techniques for Older Adults using Touchscreen Devices: a Literature Review. In Proceedings of the 25th ICME conference francophone on l’Interaction Homme-Machine - IHM ’13. ACM Press, New York, New York, USA, 125–134. https://doi.org/10.1145/2534903.2534920

Digital Library

[85]

Akram Mustafa and Mostafa Rahimi Azghadi. 2021. Automated Machine Learning for Healthcare and Clinical Notes Analysis. Computers 10, 24 (2021), 31 pages. Issue 2. https://doi.org/10.3390/computers10020024

[86]

Rosemery O. Nelson and Steven C. Hayes. 1981. Theoretical Explanations for Reactivity in Self-Monitoring.Behavior Modification 5, 1 (1981), 3–14. https://doi.org/10.1177/014544558151001

[87]

Bijarne Martens Nes, Imre Janszky, Ulrik Wisløff, Asbjørn Støylen, and Trine Karlsen. 2013. Age-predicted Maximal Heart Rate in Healthy Subjects: The HUNT Fitness Study. Scandinavian Journal of Medicine & Science in Sports 23, 6 (Dec. 2013), 697–704. https://doi.org/10.1111/j.1600-0838.2012.01445.x

[88]

Carley O’Neill and Shilpa Dogra. 2016. Different Types of Sedentary Activities and Their Association with Perceived Health and Wellness among Middle-Aged and Older Adults: A Cross-Sectional Analysis. American Journal of Health Promotion 30, 5 (2016), 314–322. https://doi.org/10.1177/0890117116646334

[89]

PAL Technologies Ltd.2021. activPAL. Retrieved Sep 09, 2021 from https://www.palt.com/

[90]

Sinno Jialin Pan and Qiang Yang. 2010. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering 22, 10(2010), 1345–1359. https://doi.org/10.1109/TKDE.2009.191

Digital Library

[91]

Carolyn Pang, Zhiqin Collin Wang, Joanna McGrenere, Rock Leung, Jiamin Dai, and Karyn Moffatt. 2021. Technology Adoption and Learning Preferences for Older Adults:. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, 1–13. https://doi.org/10.1145/3411764.3445702

Digital Library

[92]

Cassandra Phoenix and Meridith Griffin. 2013. Narratives at work: what can stories of older athletes do?Ageing and Society 33, 2 (2013), 243–266. https://doi.org/10.1017/S0144686X11001103

[93]

Cassandra Phoenix and Noreen Orr. 2014. Pleasure: A forgotten dimension of physical activity in older age. Social Science & Medicine 115 (2014), 94–102. https://doi.org/10.1016/j.socscimed.2014.06.013

[94]

Katrina L. Piercy, Richard P. Troiano, Rachel M. Ballard, Susan A. Carlson, Janet E. Fulton, Deborah A. Galuska, Stephanie M. George, and Richard D. Olson. 2018. The Physical Activity Guidelines for Americans. JAMA 320, 19 (11 2018), 2020–2028. https://doi.org/10.1001/jama.2018.14854

[95]

José Pinheiro and Douglas Bates. 2000. Mixed-Effects Models in S and S-PLUS(1 ed.). Springer-Verlag, New York. 528 pages. https://doi.org/10.1007/b98882

[96]

Stefania Pizza, Barry Brown, Donald McMillan, and Airi Lampinen. 2016. Smartwatch In Vivo. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, 5456–5469. https://doi.org/10.1145/2858036.2858522

Digital Library

[97]

Aditya Ponnada, Caitlin Haynes, Dharam Maniar, Justin Manjourides, and Stephen Intille. 2017. Microinteraction Ecological Momentary Assessment Response Rates: Effect of Microinteractions or the Smartwatch?Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 3, Article 92 (Sept. 2017), 16 pages. https://doi.org/10.1145/3130957

Digital Library

[98]

Alisha Pradhan, Amanda Lazar, and Leah Findlater. 2020. Use of Intelligent Voice Assistants by Older Adults with Low Technology Use. ACM Trans. Comput.-Hum. Interact. 27, 4, Article 31 (Sept. 2020), 27 pages. https://doi.org/10.1145/3373759

Digital Library

[99]

Archiki Prasad and Preethi Jyothi. 2020. How Accents Confound: Probing for Accent Information in End-to-End Speech Recognition Systems. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 3739–3753. https://doi.org/10.18653/v1/2020.acl-main.345

[100]

Emil Protalinski. 2017. Google’s Speech Recognition Technology Now Has a 4.9% Word Error Rate. Retrieved Sep 09, 2021 from https://venturebeat.com/2017/05/17/googles-speech-recognition-technology-now-has-a-4-9-word-error-rate/

[101]

W. Jack Rejeski, Anthony P. Marsh, Peter H. Brubaker, Matthew Buman, Roger A. Fielding, Don Hire, Todd Manini, Alvito Rego, and Michael E. Miller. 2016. Analysis and Interpretation of Accelerometry Data in Older Adults: The LIFE Study. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 71, 4 (April 2016), 521–528. https://doi.org/10.1093/gerona/glv204

[102]

AARP Research. 2016. Project Catalyst and HomeLab. Building a Better Tracker: Older Consumers Weigh In on Activity and Sleep Monitoring Devices. https://doi.org/10.26419/res.00294.001

[103]

Melanie Revilla, Mick P Couper, Oriol J Bosch, and Marc Asensio. 2020. Testing the use of voice input in a smartphone web survey. Social Science Computer Review 38, 2 (2020), 207–224. https://doi.org/10.1177/0894439318810715

Digital Library

[104]

Dori Rosenberg, Rod Walker, Mikael Anne Greenwood-Hickman, John Bellettiere, Yunhua Xiang, KatieRose Richmire, Michael Higgins, David Wing, Eric B Larson, Paul K Crane, and Andrea Z LaCroix. 2020. Device-assessed physical activity and sedentary behavior in a community-based cohort of older adults.BMC Public Health 20 (8 2020), 1256. Issue 1. https://doi.org/10.1186/s12889-020-09330-z

[105]

Sherry Ruan, Jacob O Wobbrock, Kenny Liou, Andrew Ng, and James A Landay. 2018. Comparing Speech and Keyboard Text Entry for Short Messages in Two Languages on Touchscreen Phones. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 4 (2018), 1–23. https://doi.org/10.1145/3161187

Digital Library

[106]

Cormac G. Ryan, Paul M. Grant, William W. Tigbe, and Malcolm H. Granat. 2006. The Validity and Reliability of a Novel Activity Monitor as a Measure of Walking. British Journal of Sports Medicine 40, 9 (2006), 779–784. https://doi.org/10.1136/bjsm.2006.027276 arXiv:https://bjsm.bmj.com/content/40/9/779.full.pdf

[107]

James F. Sallis and Brian E. Saelens. 2000. Assessment of Physical Activity by Self-Report: Status, Limitations, and Future Directions. Research Quarterly for Exercise and Sport 71, sup2 (June 2000), 1–14. https://doi.org/10.1080/02701367.2000.11082780

[108]

Sirat Samyoun and John Stankovic. 2021. VoiSense: Harnessing Voice Interaction on a Smartwatch to Collect Sensor Data: Demo Abstract. In Proceedings of the 20th International Conference on Information Processing in Sensor Networks (Co-Located with CPS-IoT Week 2021). ACM, New York, NY, USA, 388–389. https://doi.org/10.1145/3412382.3458777

Digital Library

[109]

Jennifer A. Schrack, Rachel Cooper, Annemarie Koster, Eric J. Shiroma, Joanne M. Murabito, W. Jack Rejeski, Luigi Ferrucci, and Tamara B. Harris. 2016. Assessing Daily Physical Activity in Older Adults: Unraveling the Complexity of Monitors, Measures, and Methods. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 71, 8 (Aug. 2016), 1039–1048. https://doi.org/10.1093/gerona/glw026

[110]

Sedentary Behaviour Research Network. 2012. Letter to the Editor: Standardized Use of the Terms “Sedentary” and “Sedentary Behaviours”. Applied Physiology, Nutrition, and Metabolism 37, 3 (June 2012), 540–542. https://doi.org/10.1139/h2012-024

[111]

Lee Smith, Ulf Ekelund, and Mark Hamer. 2015. The Potential Yield of Non-Exercise Physical Activity Energy Expenditure in Public Health. Sports Medicine 45, 4 (April 2015), 449–452. https://doi.org/10.1007/s40279-015-0310-2

[112]

Phillip B. Sparling, Bethany J. Howard, David W. Dunstan, and Neville Owen. 2015. Recommendations for Physical Activity in Older Adults. BMJ 350, jan20 6 (Jan. 2015), h100–h100. https://doi.org/10.1136/bmj.h100

[113]

Arjun Srinivasan, Bongshin Lee, Nathalie Henry Riche, Steven M. Drucker, and Ken Hinckley. 2020. InChorus: Designing Consistent Multimodal Interactions for Data Visualization on Tablet Devices. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). ACM, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376782

Digital Library

[114]

Anita L. Stewart, Kristin M. Mills, Abby C. King, William L. Haskell, Dawn Gillis, and Philip L. Ritter. 2001. CHAMPS Physical Activity Questionnaire for Older Adults: Outcomes for Interventions. Medicine & Science in Sports & Exercise 33, 7 (2001), 1126–1141. https://journals.lww.com/acsm-msse/Fulltext/2001/07000/CHAMPS_Physical_Activity_Questionnaire_for_Older.10.aspx

[115]

Allan Stisen, Henrik Blunck, Sourav Bhattacharya, Thor Siiger Prentow, Mikkel Baun Kjærgaard, Anind Dey, Tobias Sonne, and Mads Møller Jensen. 2015. Smart Devices are Different: Assessing and Mitigating Mobile Sensing Heterogeneities for Activity Recognition. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems(SenSys ’15). ACM, New York, NY, USA, 127–140.

Digital Library

[116]

Ali Raza Syed, Andrew Rosenberg, and Michael Mandel. 2017. Active Learning for Low-Resource Speech Recognition: Impact of Selection Size and Language Modeling Data. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Washington, DC, USA, 5315–5319. https://doi.org/10.1109/ICASSP.2017.7953171

Digital Library

[117]

Catrine Tudor-Locke, Cora L Craig, Yukitoshi Aoyagi, Rhonda C Bell, Karen A Croteau, Ilse De Bourdeaudhuij, Ben Ewald, Andrew W Gardner, Yoshiro Hatano, Lesley D Lutes, Sandra M Matsudo, Farah A Ramirez-Marrero, Laura Q Rogers, David A Rowe, Michael D Schmidt, Mark A Tully, and Steven N Blair. 2011. How Many Steps/day Are Enough? For Older Adults And Special Populations. International Journal of Behavioral Nutrition and Physical Activity 8 (7 2011), 80. Issue 1. https://doi.org/10.1186/1479-5868-8-80

[118]

Catrine Tudor-Locke and David A Rowe. 2012. Using Cadence to Study Free-Living Ambulatory Behaviour. Sports Medicine 42, 5 (May 2012), 381–398. https://doi.org/10.2165/11599170-000000000-00000

[119]

Yonatan Vaizman, Katherine Ellis, Gert Lanckriet, and Nadir Weibel. 2018. ExtraSensory App: Data Collection In-the-Wild with Rich User Interface to Self-Report Behavior. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18). ACM, New York, NY, USA, Article 554, 12 pages. https://doi.org/10.1145/3173574.3174128

Digital Library

[120]

Niels van Berkel, Denzil Ferreira, and Vassilis Kostakos. 2017. The Experience Sampling Method on Mobile Devices. ACM Comput. Surv. 50, 6, Article 93 (Dec. 2017), 40 pages. https://doi.org/10.1145/3123988

Digital Library

[121]

Dimitri Vargemidis, Kathrin Gerling, Vero Vanden Abeele, Luc Geurts, and Katta Spiel. 2021. Irrelevant Gadgets or a Source of Worry: Exploring Wearable Activity Trackers with Older Adults. ACM Trans. Access. Comput. 14, 3, Article 16 (Aug. 2021), 28 pages. https://doi.org/10.1145/3473463

Digital Library

[122]

Dimitri Vargemidis, Kathrin Gerling, Katta Spiel, Vero Vanden Abeele, and Luc Geurts. 2020. Wearable Physical Activity Tracking Systems for Older Adults—A Systematic Review. ACM Trans. Comput. Healthcare 1, 4, Article 25 (Sept. 2020), 37 pages. https://doi.org/10.1145/3402523

Digital Library

[123]

William N. Venables and Brian D. Ripley. 2002. Modern Applied Statistics with S(4th ed.). Springer, New York. https://www.stats.ox.ac.uk/pub/MASS4/ ISBN 0-387-95457-0.

[124]

Ravichander Vipperla, Steve Renals, and Joe Frankel. 2008. Longitudinal Study of ASR Performance on Ageing Voices. In Interspeech 2008. ISCA, Winona, MN, USA, 2550–2553.

[125]

Marjolein Visser and Annemarie Koster. 2013. Development of a Questionnaire to Assess Sedentary Time in Older Persons – a Comparative Study using Accelerometry. BMC Geriatrics 13, 1 (Dec. 2013), 80. https://doi.org/10.1186/1471-2318-13-80

[126]

Emily A. Vogels. 2020. About One-in-Five Americans Use a Smartwatch or Fitness Tracker | Pew Research Center. Retrieved Sep 09, 2021 from https://www.pewresearch.org/fact-tank/2020/01/09/about-one-in-five-americans-use-a-smart-watch-or-fitness-tracker/

[127]

Darren E.R. Warburton, Crystal Whitney Nicol, and Shannon S.D. Bredin. 2006. Health Benefits of Physical Activity: The Evidence. CMAJ: Canadian Medical Association Journal 174, 6 (2006), 801–809.

[128]

Alanna Weisberg, Alexandre Monte Campelo, Tanzeel Bhaidani, and Larry Katz. 2020. Physical Activity Tracking Wristbands for Use in Research With Older Adults: An Overview and Recommendations. Journal for the Measurement of Physical Behaviour 3, 4 (Dec. 2020), 265–273. https://doi.org/10.1123/jmpb.2019-0050

[129]

Gary M. Weiss. 2019. WISDM: Smartphone and Smartwatch Activity and Biometrics Dataset Data Set. https://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+

[130]

Gary M. Weiss, Kenichi Yoneda, and Thaier Hayajneh. 2019. Smartphone and Smartwatch-Based Biometrics using Activities of Daily Living. IEEE Access 7(2019), 133190–133202.

[131]

Douglas J. Wiebe, Bernadette A. D’Alonzo, Robin Harris, Margot Putukian, and Carolyn Campbell-McGovern. 2018. Joint Prevalence of Sitting Time and Leisure-Time Physical Activity Among US Adults, 2015-2016. Journal of American Medical Association 320, 19 (Nov. 2018), 2035. https://doi.org/10.1001/jama.2018.14165

[132]

Christopher K. Wong, Helena M. Mentis, and Ravi Kuber. 2018. The Bit Doesn’t Fit: Evaluation of a Commercial Activity-Tracker at Slower Walking Speeds. Gait & posture 59(2018), 177–181.

[133]

W. Xiong, L. Wu, F. Alleva, J. Droppo, X. Huang, and A. Stolcke. 2018. The Microsoft 2017 Conversational Speech Recognition System. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Washington, DC, USA, 5934–5938. https://doi.org/10.1109/ICASSP.2018.8461870

Digital Library

[134]

Xinghui Yan, Shriti Raj, Bingjian Huang, Sun Young Park, and Mark W. Newman. 2020. Toward Lightweight In-situ Self-reporting: An Exploratory Study of Alternative Smartwatch Interface Designs in Context. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 4 (Dec. 2020), 1–22. https://doi.org/10.1145/3432212

Digital Library

[135]

Lin Yang, Chao Cao, Elizabeth D. Kantor, Long H. Nguyen, Xiaobin Zheng, Yikyung Park, Edward L. Giovannucci, Charles E. Matthews, Graham A. Colditz, and Yin Cao. 2019. Trends in Sedentary Behavior Among the US Population, 2001-2016. JAMA 321, 16 (04 2019), 1587–1597. https://doi.org/10.1001/jama.2019.3636

[136]

Hanlu Ye, Meethu Malu, Uran Oh, and Leah Findlater. 2014. Current and Future Mobile and Wearable Device Use by People with Visual Impairments. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Toronto, Ontario, Canada) (CHI ’14). Association for Computing Machinery, New York, NY, USA, 3123–3132. https://doi.org/10.1145/2556288.2557085

Digital Library

Cited By

Le HLakshminarayanan RLi JMishra VIntille S(2024)Collecting Self-reported Physical Activity and Posture Data Using Audio-based Ecological Momentary AssessmentProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785848:3(1-35)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678584
Cuadra ABreuch JEstrada SIhim DHung IAskaryar DHassanien MFessele KLanday J(2024)Digital Forms for All: A Holistic Multimodal Large Language Model Agent for Health Data EntryProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596248:2(1-39)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659624
Lyu HBai YLiang XDas UShi CGong LLi YSun MGe MMa X(2024)FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers’ Preference ElicitationProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645145(344-369)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645145
Show More Cited By

Index Terms

MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Sound-based input / output
  2. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing

Recommendations

Activity Recognition in Older Adults with Training Data from Younger Adults: Preliminary Results on in Vivo Smartwatch Sensor Data
ASSETS '21: Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility

Self-tracking using commodity wearables such as smartwatches can help older adults reduce sedentary behaviors and engage in physical activity. However, activity recognition applications that are typically deployed in these wearables tend to be trained ...
Redefining Activity Tracking Through Older Adults' Reflections on Meaningful Activities
CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems

Activity tracking has the potential to promote active lifestyles among older adults. However, current activity tracking technologies may inadvertently perpetuate ageism by focusing on age-related health risks. Advocating for a personalized approach in ...
Wearable Technologies: Acceptance Model for Smartwatch Adoption Among Older Adults
Human Aspects of IT for the Aged Population. Technologies, Design and User Experience
Abstract
Wrist-worn wearable technologies such as smartwatches are seen to be one of the breakthrough devices that would help older adults age successfully. However, to date, there is still a lack of systematic evaluation of smartwatch adoption for older ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

April 2022

10459 pages

ISBN:9781450391573

DOI:10.1145/3491102

Editors:
Simone Barbosa
PUC-Rio, Brazil
,
Cliff Lampe
University of Michigan, USA
,
Caroline Appert
Université Paris-Saclay, France
,
David A. Shamma
Toyota Research Institute, USA
,
Steven Drucker
Microsoft Research, USA
,
Julie Williamson
University of Glasgow, UK
,
Koji Yatani
University of Tokyo, Japan

Copyright © 2022 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 April 2022

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

National Science Foundation

Conference

CHI '22

Sponsor:

SIGCHI

CHI '22: CHI Conference on Human Factors in Computing Systems

April 29 - May 5, 2022

LA, New Orleans, USA

Acceptance Rates

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Upcoming Conference

CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
2,616
Total Downloads

Downloads (Last 12 months)1,263
Downloads (Last 6 weeks)135

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Le HLakshminarayanan RLi JMishra VIntille S(2024)Collecting Self-reported Physical Activity and Posture Data Using Audio-based Ecological Momentary AssessmentProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36785848:3(1-35)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3678584
Cuadra ABreuch JEstrada SIhim DHung IAskaryar DHassanien MFessele KLanday J(2024)Digital Forms for All: A Holistic Multimodal Large Language Model Agent for Health Data EntryProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596248:2(1-39)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659624
Lyu HBai YLiang XDas UShi CGong LLi YSun MGe MMa X(2024)FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers’ Preference ElicitationProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645145(344-369)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645145
Wei JKim SJung HKim Y(2024)Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported DataProceedings of the ACM on Human-Computer Interaction10.1145/36373648:CSCW1(1-35)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637364
Prinos KPatwari NPower C(2024)Speaking of accent: A content analysis of accent misconceptions in ASR researchProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658969(1245-1254)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658969
Lim JKoh YKim ALee U(2024)Exploring Context-Aware Mental Health Self-Tracking Using Multimodal Smart Speakers in Home EnvironmentsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642846(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642846
Dwivedi UElsayed-Ali SBonsignore EKacorri H(2024)Exploring AI Problem Formulation with Children via Teachable MachinesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642692(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642692
Wang YLi MKim YLee BDanilovich MLazar AConroy DKacorri HChoe E(2024)Redefining Activity Tracking Through Older Adults' Reflections on Meaningful ActivitiesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642170(1-15)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642170
Markopoulos PKhanshan ABormans STisza GKang LVan Gorp P(2023)Comparative Evaluation of Touch-Based Input Techniques for Experience Sampling on SmartwatchesProceedings of the 22nd International Conference on Mobile and Ubiquitous Multimedia10.1145/3626705.3627768(74-86)Online publication date: 3-Dec-2023
https://dl.acm.org/doi/10.1145/3626705.3627768
Hester JLe HIntille SMeier E(2023)A feasibility study on the use of audio-based ecological momentary assessment with persons with aphasiaProceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3597638.3608419(1-7)Online publication date: 22-Oct-2023
https://dl.acm.org/doi/10.1145/3597638.3608419
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten