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Insights from Designing Context-Aware Meal Preparation Assistance for Older Adults with Mild Cognitive Impairment (MCI) and Their Care Partners

T0 review · 0 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read An iterative design study identifies three contexts that meal-prep assistance for older adults with MCI must handle.

desk verdict Useful three-context taxonomy for meal-prep support in MCI dyads; the Phase 3 miss-rate data is a delivery-design problem, not a refutation of the taxonomy. read the letter →

arxiv 2506.05663 v1 pith:ZACKKZPO submitted 2025-06-06 cs.HC

Szeyi Chan , Jiachen Li , Siman Ao , Yufei Wang , Ibrahim Bilau , Brian Jones , Eunhwa Yang , Elizabeth D Mynatt
show 1 more author
Xiang Zhi Tan
This is my paper · ORCID
classification cs.HC
keywords mildcognitiveimpairmentmealpreparationcontext-awareassistanceassistivetechnologyolderadultscarepartnersparticipatorydesignaginginplace
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish what a context-aware meal-preparation assistant for older adults with mild cognitive impairment (MCI) should pay attention to, and how to personalize it. Working iteratively with older adults with MCI and their care partners, the authors moved from a low-tech lightbox to a digital instruction panel and distilled their findings into three critical contexts: routine-based, real-time, and situational. The value of the claim is practical: if designers build reminders and instructions around these three contexts, and include care partners in setting them up, older adults could cook more safely and independently while care partners carry less burden.

What carries the argument

The central objects are the three technology probes—a low-tech Lightbox, a Central Hub Lightbox with modular instruction panels, and a Digital Instruction Panel—and the three-context categorization they produced. Routine-based contexts are recurring habits and preferences that trigger reminders (e.g., medication, grocery lists); real-time contexts are critical moments during cooking that require just-in-time prompts (e.g., switching microwave power); situational contexts are physical kitchen states such as an open fridge, stove left on, or dirty counters. The Digital Instruction Panel, evaluated in a Wizard of Oz walkthrough, is the mechanism that demonstrates how these contexts would be delivered through digital sticky notes with audio readout and checkboxes.

What would settle it

Conduct a field study in real homes where a sensor-equipped system must detect kitchen states (fridge open, stove on, task completed) without a Wizard of Oz; if state detection is frequently wrong or too slow, the situational-context support—one of the paper's three pillars—fails.

Watch

Extended reading notes

Core claim

Through three iterative design phases with dyads of older adults with MCI and their care partners, the paper establishes that effective meal-preparation assistance must recognize and respond to three critical contexts: routine-based (recurring habits and preferences), real-time (moments during cooking when a prompt is needed), and situational (the physical state of the kitchen, such as an open fridge or stove left on). The paper further claims that care partners must be active participants in configuring and delivering this support, that low-tech simplicity alone is insufficient because users' needs are dynamic, and that assistive technology should build on the technology routines older adults already have. If the paper is right, designers of such systems should organize reminders around these three context types and treat the older adult and care partner as the unit of design.

Load-bearing premise

The situational-context support depends on sensors being able to reliably detect kitchen states such as an open fridge or a completed task, because in the Phase 3 test a researcher manually sent those reminders by hand.

Editorial extensions

If this is right

  • Reminder content should be categorized by whether it is routine (recurring habit), real-time (needed at a critical cooking moment), or situational (reactive to the kitchen's physical state), with different delivery mechanisms for each.
  • Care partners should be able to create, schedule, and send reminders from the system, including remotely, so the device serves both members of the dyad.
  • Safety-related prompts (e.g., stove off, fridge closed) should take priority and use more prominent alerts than routine or convenience reminders.
  • Low-tech static devices will likely fail to meet needs; future systems should be digital, 'always-on,' and reactive to household state, while still building on familiar tools like voice assistants.
  • The system should support multiple placements in the home and flexible levels of detail, since routines and cognitive needs change over time.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The three-context taxonomy may generalize beyond meal preparation to other instrumental activities of daily living such as medication management and appointment keeping, since the same routine/real-time/situational split appears in those domains.
  • If reliable sensing for situational contexts is not achievable, the situational pillar—one of the paper's three contexts—would need to be redesigned or dropped; the paper itself flags this dependency in its Wizard of Oz evaluation.
  • Care partners adding reminders for themselves in the final phase suggests the device may function as a household coordination tool, not just an aid for the person with MCI; a testable extension is measuring whether dual-user use actually reduces caregiver burden.
  • The paper's observation that some participants missed all reminders points to an open research problem in notification salience for this population; future work could compare loud audio, visual, and multimodal alerts in real kitchen conditions.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

0 major / 6 minor

Summary. This paper reports a three-phase iterative design study with older adults with mild cognitive impairment (MCI) and their care partners, developing and refining a context-aware meal preparation assistance prototype from a low-tech lightbox to a digital instruction panel. The authors identify three contexts—routine-based, real-time, and situational—that they argue are essential for designing such assistive systems, and they derive design implications emphasizing personalization, care-partner collaboration, and the need to balance simplicity with technological integration.

Significance. If accepted, the paper provides a useful empirical taxonomy and design guidance for assistive technology in a growing area. Strengths include the iterative multi-phase design process grounded in participant feedback, the inclusion of care partners as co-designers, the use of concrete participant quotes and observational notes, and a thoughtful reflection on power dynamics in the design process (Section 7.2.4). The paper also honestly documents limitations including the single-site recruitment and the Wizard of Oz evaluation.

minor comments (6)
  1. [Abstract and Section 8] The wording "critical" and "essential" is stronger than the evidence supports; in the Phase 3 walkthrough, four of six participants missed some or all real-time reminders (Section 6.6.1), and the situational-context functionality was simulated (Section 6.5). Please hedge these claims to "important design considerations" and explicitly note that reliable sensing and alert delivery remain open challenges.
  2. [Section 6.3] The participant count is inconsistent: "19 participants, including 11 older adults with MCI and nine care partners" sums to 20; please correct the numbers.
  3. [Section 5.3] The description of Phase 2 says "about 10 pairs" without further demographic or analytic detail; please provide the exact number and a brief description of how the group feedback was analyzed.
  4. [Section 1] The sentence "Context-aware systems, which respond to users’ surroundings, can enhance assistive technologies by providing situation-specific support [59] has the potential to assist older adults with MCI and their care partners" is grammatically garbled; please rephrase.
  5. [Section 7.3] The limitation paragraph acknowledges the Wizard of Oz assumption but could more explicitly state that the situational-context category was evaluated with a simulated rather than a functional sensing system, and that this limits the conclusions about the feasibility of the situational pillar.
  6. [Figure 1] The timeline is dense and the phase labels overlap; consider redrawing for clarity.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the three-context taxonomy is grounded in participant feedback, and self-citations are background only.

full rationale

This paper is a qualitative, iterative design study rather than a formal derivation, so most circularity patterns do not apply. The central claim—that three contexts (routine-based, real-time, and situational) are key for meal-preparation assistance—is induced directly from participant interviews and feedback across three phases (e.g., Sections 4.3.1, 5.4.1, 6.4, and 6.6.1), not from fitting any parameter to a target quantity or invoking a self-authored uniqueness theorem. Self-citations such as Chan et al. [10], Zubatiy et al. [70], and Mathur et al. [41] appear only as related-work background and motivation; none is load-bearing evidence for the proposed taxonomy. The Wizard-of-Oz simulation of situational sensing (Section 6.5) is an explicitly acknowledged limitation (Section 7.3), not a circular reduction: the paper does not claim to have evaluated real sensing, and the observed reminder miss-rate is reported honestly. No equation or construction makes the conclusion equal to its inputs. Therefore no significant circularity is present.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters or invented entities. The study is qualitative; its central claim rests on interview and observation data, not on fitted constants. The main untested premise is that simulated context sensing will be realizable and reliable, alongside the generalizability of a small convenience sample.

assumptions (3)
  • domain assumption Wizard of Oz simulation can stand in for future sensor-based context detection.
    Section 7.3 states the prototype assumed relevant contexts could be captured by sensors; the feasibility and reliability of such sensing is untested.
  • domain assumption Participant and care partner self-reports during design sessions reflect real meal preparation needs.
    The analysis is built on interview quotes and observational notes from a small, self-selected sample; no objective behavioral logging was used.
  • domain assumption The Cognitive Empowerment Program sample is adequate for drawing generalizable design insights.
    Section 7.3 acknowledges the limited diversity and lack of retention across phases; the paper still treats the insights as a foundation for future design.

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Cite this review

Pith. "Pith review of Insights from Designing Context-Aware Meal Preparation Assistance for Older Adults with Mild Cognitive Impairment (MCI) and Their Care Partners." pith.science (2026). https://pith.science/paper/ZACKKZPO

@misc{pith2026250605663,
  author       = {Pith},
  title        = {Pith review of: Insights from Designing Context-Aware Meal Preparation Assistance for Older Adults with Mild Cognitive Impairment (MCI) and Their Care Partners},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZACKKZPO}},
  note         = {Machine review of arXiv:2506.05663}
}
read the original abstract

Older adults with mild cognitive impairment (MCI) often face challenges during meal preparation, such as forgetting ingredients, skipping steps, or leaving appliances on, which can compromise their safety and independence. Our study explores the design of context-aware assistive technologies for meal preparation using a user-centered iterative design process. Through three iterative phases of design and feedback, evolving from low-tech lightbox to a digital screen, we gained insights into managing diverse contexts and personalizing assistance through collaboration with older adults with MCI and their care partners. We concluded our findings in three key contexts--routine-based, real-time, and situational--that informed strategies for designing context-aware meal prep assistance tailored to users' needs. Our results provide actionable insights for creating technologies to assist meal preparation that are personalized for the unique lifestyles of older adults with MCI, situated in the complex and dynamic homebound context, and respecting the collaboration between older adults and their care partners.

Figures

Figures reproduced from arXiv: 2506.05663 by the authors.

Figure 1
Figure 1. An overview of our three-phase iterative study conducted from Spring 2022 to Spring 2024. Each phase included a [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The interaction flow for the Lightbox concept. The process begins by pressing the Start button to initiate the system. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Design sketches and comic strip scenarios illustrating four prototypes for context-aware meal preparation assistance, [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The Central Hub Lightbox includes two components: the central hub and the instruction panel. The central hub [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: The system flow of the Digital Instruction Panel. The panel, designed to be attached to surfaces like a refrigerator [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Both care partners and older adults with MCI com [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Our study was conducted in the common kitchen [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

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Reference graph

Works this paper leans on

74 extracted references · 69 canonical work pages

  1. [1]

    2024.What Is Mild Cognitive Impairment?https://www

    Alzheimers.gov. 2024.What Is Mild Cognitive Impairment?https://www. alzheimers.gov/alzheimers-dementias/mild-cognitive-impairment

    work page 2024

  2. [2]

    Prashanti Angara, Miguel Jiménez, Kirti Agarwal, Harshit Jain, Roshni Jain, Ulrike Stege, Sudhakar Ganti, Hausi A Müller, and Joanna W Ng. 2017. Foodie fooderson a conversational agent for the smart kitchen.. InCASCON, Vol. 17. 247–253

    work page 2017

  3. [3]

    Arman Anzanpour, Amir-Mohammad Rahmani, Pasi Liljeberg, and Hannu Ten- hunen. 2016. Context-aware early warning system for in-home healthcare using internet-of-things. InInternet of Things. IoT Infrastructures: Second International Summit, IoT 360°2015, Rome, Italy, October 27–29, 2015, Revised Selected Papers, Part I. Springer, 517–522

    work page 2016

  4. [4]

    Ali Ayub, Chrystopher L Nehaniv, and Kerstin Dautenhahn. 2022. Don’t forget to buy milk: Contextually aware grocery reminder household robot. In2022 IEEE International Conference on Development and Learning (ICDL). IEEE, 299–306

    work page 2022

  5. [5]

    Brooke E Beatie, Corey S Mackenzie, Laura Funk, Dylan Davidson, Lesley Koven, and Kristin A Reynolds. 2021. Caregiver identity in care partners of persons living with mild cognitive impairment.Dementia20, 7 (2021), 2323–2339

    work page 2021

  6. [6]

    Athanasios Bimpas, John Violos, Aris Leivadeas, and Iraklis Varlamis. 2024. Lever- aging pervasive computing for ambient intelligence: A survey on recent advance- ments, applications and open challenges.Computer Networks239 (2024), 110156

    work page 2024

  7. [7]

    Rubén Blasco, Álvaro Marco, Roberto Casas, Diego Cirujano, and Richard Picking

    work page

  8. [8]

    Bruno Bouchard, Kevin Bouchard, and Abdenour Bouzouane. 2014. A smart range helping cognitively-impaired persons cooking. InProceedings of the AAAI Conference on Artificial Intelligence, Vol. 28. 2960–2965

    work page 2014

Show all 74 references
  1. [9]

    Bruno Bouchard, Kevin Bouchard, and Abdenour Bouzouane. 2020. A smart cook- ing device for assisting cognitively impaired users.Journal of reliable intelligent environments6 (2020), 107–125

    2020

  2. [10]

    Mango Mango, How to Let The Lettuce Dry Without A Spinner?

    Szeyi Chan, Jiachen Li, Bingsheng Yao, Amama Mahmood, Chien-Ming Huang, Holly Jimison, Elizabeth D Mynatt, and Dakuo Wang. 2023. " Mango Mango, How to Let The Lettuce Dry Without A Spinner?”: Exploring User Perceptions of Using An LLM-Based Conversational Assistant Toward Cook...

    2023 arXiv

  3. [11]

    Shih-Hao Chang, Rui-Dong Chiang, Shih-Jung Wu, and Wei-Ting Chang. 2016. A context-aware, interactive M-health system for diabetics.IT professional18, 3 (2016), 14–22

    2016

  4. [12]

    Jen-Hao Chen, Peggy Pei-Yu Chi, Hao-Hua Chu, Cheryl Chia-Hui Chen, and Polly Huang. 2010. A smart kitchen for nutrition-aware cooking.IEEE Pervasive Computing9, 4 (2010), 58–65

    2010

  5. [13]

    Li-Hao Chen and Yi-Chien Liu. 2022. Interface Design for Products for Users with Advanced Age and Cognitive Impairment.International Journal of Environmental Research and Public Health19, 4 (2022), 2466

    2022

  6. [14]

    Fergus IM Craik and Ellen Bialystok. 2006. Planning and task management in older adults: Cooking breakfast.Memory & Cognition34, 6 (2006), 1236–1249

    2006

  7. [15]

    Shakila Dada, Charene Van der Walt, Adele A May, and Janice Murray. 2024. Intelligent assistive technology devices for persons with dementia: a scoping review.Assistive Technology36, 5 (2024), 338–351

    2024

  8. [16]

    Dey and G Abowd

    Anind K. Dey and G Abowd. 1999. Towards a better understanding of context and context-awareness.Springer google schola2 (1999), 23–32

    1999

  9. [17]

    Ramin Fallahzadeh, Yuchao Ma, and Hassan Ghasemzadeh. 2016. Context-aware system design for remote health monitoring: An application to continuous edema assessment.IEEE Transactions on Mobile Computing16, 8 (2016), 2159–2173

    2016

  10. [18]

    Alexander Frummet, Alessandro Speggiorin, David Elsweiler, Anton Leuski, and Jeff Dalton. 2024. Cooking with Conversation: Enhancing User Engagement and Learning with a Knowledge-Enhancing Assistant.ACM Transactions on DIS ’25, July 5–9, 2025, Funchal, Portugal Chan and Li, et...

    2024

  11. [19]

    Serge Gauthier, Barry Reisberg, Michael Zaudig, Ronald C Petersen, Karen Ritchie, Karl Broich, Sylvie Belleville, Henry Brodaty, David Bennett, Howard Chertkow, et al. 2006. Mild cognitive impairment.The lancet367, 9518 (2006), 1262–1270

    2006

  12. [20]

    Maya Goodwill, Roy Bendor, and Mieke van der Bijl-Brouwer. 2021. Beyond good intentions: Towards a power literacy framework for service designers. International Journal of Design15, 3 (2021), 45–59

    2021

  13. [21]

    Katherine Hackett, Sarah Lehman, Ross Divers, Matthew Ambrogi, Likhon Gomes, Chiu C Tan, and Tania Giovannetti. 2022. Remind Me To Remember: A pilot study of a novel smartphone reminder application for older adults with dementia and mild cognitive impairment.Neuropsychological...

    2022

  14. [22]

    Karen Zita Haigh, Liana M Kiff, and Geoffrey Ho. 2006. The independent lifestyle assistant: Lessons learned.Assistive Technology18, 1 (2006), 87–106

    2006

  15. [23]

    Reiko Hamada, Jun Okabe, Ichiro Ide, Shin’ichi Satoh, Shuichi Sakai, and Hidehiko Tanaka. 2005. Cooking navi: assistant for daily cooking in kitchen. InProceedings of the 13th annual ACM international conference on Multimedia. 371–374

    2005

  16. [24]

    Torhild Holthe, Liv Halvorsrud, and Anne Lund. 2022. Digital assistive tech- nology to support everyday living in community-dwelling older adults with mild cognitive impairment and dementia.Clinical Interventions in Aging(2022), 519–544

    2022

  17. [25]

    Qinzhi Jiang, Mustafa Naseem, Jamie Lai, Kentaro Toyama, and Panos Pa- palambros. 2022. Understanding power differentials and cultural differences in co-design with marginalized populations. InProceedings of the 5th ACM SIG- CAS/SIGCHI conference on computing and sustainable s...

    2022

  18. [26]

    The Cognitive Kitchen

    K Johansson, S Lundberg, and L Borell. 2011. “The Cognitive Kitchen”–Key principles and suggestions for design that includes older adults with cognitive impairments as kitchen users.Technology and disability23, 1 (2011), 29–40

    2011

  19. [27]

    Karl-Ebbe Jönsson, Kajsa Ornstein, Jonas Christensen, and Jeanette Eriksson

  20. [28]

    Dong-Oh Kang, Hyung-Jik Lee, Eun-Jung Ko, Kyuchang Kang, and Jeunwoo Lee. 2006. A wearable context aware system for ubiquitous healthcare. In2006 International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 5192–5195

    2006

  21. [29]

    Simon Klakegg, Kennedy Opoku Asare, Niels van Berkel, Aku Visuri, Eija Ferreira, Simo Hosio, Jorge Goncalves, Hanna-Leena Huttunen, and Denzil Ferreira. 2021. CARE: Context-awareness for elderly care.Health and Technology11 (2021), 211–226

    2021

  22. [30]

    Thomas Kosch, Kevin Wennrich, Daniel Topp, Marcel Muntzinger, and Albrecht Schmidt. 2019. The digital cooking coach: using visual and auditory in-situ instructions to assist cognitively impaired during cooking. InProceedings of the 12th ACM international conference on pervasiv...

    2019

  23. [31]

    Thomas Kosch, Paweł W Woźniak, Erin Brady, and Albrecht Schmidt. 2018. Smart kitchens for people with cognitive impairments: A qualitative study of design requirements. InProceedings of the 2018 CHI conference on human factors in computing systems. 1–12

    2018

  24. [32]

    Prajakta Kulkarni and Yusuf Ozturk. 2011. mPHASiS: Mobile patient healthcare and sensor information system.Journal of Network and Computer Applications 34, 1 (2011), 402–417

    2011

  25. [33]

    Sanna Kuoppamäki, Sylvaine Tuncer, Sara Eriksson, and Donald McMillan. 2021. Designing Kitchen Technologies for Ageing in Place: A Video Study of Older Adults’ Cooking at Home.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies5, 2 (2021), 1–19

    2021

  26. [34]

    Sofoklis Kyriazakos, Mihail Mihaylov, Bayu Anggorojati, Albena Mihovska, Raz- van Craciunescu, Octavian Fratu, and Ramjee Prasad. 2016. eWALL: an intelligent caring home environment offering personalized context-aware applications based on advanced sensing.Wireless Personal Co...

    2016

  27. [35]

    Powell Lawton and Elaine M

    M. Powell Lawton and Elaine M. Brody. 1969. Assessment of Older People: Self-Maintaining and Instrumental Activities of Daily Living1. The Gerontologist9, 3_Part_1 (10 1969), 179–186. doi:10.1093/geront/ 9.3_Part_1.179 arXiv:https://academic.oup.com/gerontologist/article- pdf/...

    1969 doi

  28. [36]

    Franklin Mingzhe Li, Jamie Dorst, Peter Cederberg, and Patrick Carrington. 2021. Non-visual cooking: exploring practices and challenges of meal preparation by people with visual impairments. InProceedings of the 23rd International ACM SIGACCESS Conference on Computers and Acce...

    2021

  29. [37]

    Franklin Mingzhe Li, Ashley Wang, Patrick Carrington, and Shaun K Kane. 2024. A Recipe for Success? Exploring Strategies for Improving Non-Visual Access to Cooking Instructions. InProceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility. 1–15

    2024

  30. [38]

    Clara Löbe and Hanan AboJabel. 2022. Empowering people with dementia via using intelligent assistive technology: a scoping review.Archives of Gerontology and Geriatrics101 (2022), 104699

    2022

  31. [39]

    Anja Maier, Ali Gürcan Özkil, Maria Mimi Bang, and Birgitte Hysse Forchhammer

  32. [40]

    Camilla Malinowsky, Ove Almkvist, Anders Kottorp, and Louise Nygård. 2010. Ability to manage everyday technology: a comparison of persons with dementia or mild cognitive impairment and older adults without cognitive impairment. Disability and rehabilitation: Assistive technolo...

    2010

  33. [41]

    Niharika Mathur, Kunal Dhodapkar, Tamara Zubatiy, Jiachen Li, Brian Jones, and Elizabeth Mynatt. 2022. A collaborative approach to support medication management in older adults with mild cognitive impairment using conversa- tional assistants (CAs). InProceedings of the 24th In...

    2022

  34. [42]

    Why Did You Say That?

    Niharika Mathur, Tamara Zubatiy, Agata Rozga, and Elizabeth Mynatt. 2023. “Why Did You Say That?”: Understanding Explainability in Conversational AI Systems for Older Adults with Mild Cognitive Impairment (MCI). InInternational Conference on Ubiquitous Computing and Ambient In...

    2023

  35. [43]

    Hayley R McCarron, Rachel Zmora, and Joseph E Gaugler. 2019. A web-based mobile app with a smartwatch to support social engagement in persons with memory loss: pilot randomized controlled trial.JMIR aging2, 1 (2019), e13378

    2019

  36. [44]

    Sarah Mennicken, Thorsten Karrer, Peter Russell, and Jan Borchers. 2010. First- person cooking: a dual-perspective interactive kitchen counter. InCHI’10 Ex- tended Abstracts on Human Factors in Computing Systems. 3403–3408

    2010

  37. [45]

    Sami Miniaoui, Shadi Atalla, and Kamarul Faizal Bin Hashim. 2019. Introducing innovative item management process towards providing smart fridges. In2019 2nd International Conference on Communication Engineering and Technology (ICCET). IEEE, 62–67

    2019

  38. [46]

    Sarah Morris, Karla Conn Welch, and Michael Schroeder. 2021. Inventory Man- agement of the Refrigerator’s Produce Bins Using Classification Algorithms and Hand Analysis. InSoutheastCon 2021. IEEE, 1–8

    2021

  39. [47]

    Elizabeth D Mynatt, Irfan Essa, and Wendy Rogers. 2000. Increasing the oppor- tunities for aging in place. InProceedings on the 2000 conference on Universal Usability. 65–71

    2000

  40. [48]

    kognichef

    Alexander Neumann, Christof Elbrechter, Nadine Pfeiffer-Leßmann, Risto Kõiva, Birte Carlmeyer, Stefan Rüther, Michael Schade, André Ückermann, Sven Wachsmuth, and Helge J Ritter. 2017. “kognichef”: A cognitive cooking assistant. KI-Künstliche Intelligenz31 (2017), 273–281

    2017

  41. [49]

    Sonja A O’Neill, Sarah Mason, Guido Parente, Mark P Donnelly, Christopher D Nugent, Sally McClean, Bryan Scotney, and David Craig. 2011. Video reminders as cognitive prosthetics for people with dementia.Ageing International36 (2011), 267–282

    2011

  42. [50]

    Ilia Papachristou, Nikolette Giatras, and Michael Ussher. 2013. Impact of de- mentia progression on food-related processes: a qualitative study of caregivers’ perspectives.American Journal of Alzheimer’s Disease & Other Dementias®28, 6 (2013), 568–574

    2013

  43. [51]

    Matt Paradise, Donna McCade, Ian B Hickie, Keri Diamond, Simon JG Lewis, and Sharon L Naismith. 2015. Caregiver burden in mild cognitive impairment.Aging & mental health19, 1 (2015), 72–78

    2015

  44. [52]

    Ronald C Petersen. 2016. Mild cognitive impairment.CONTINUUM: lifelong Learning in Neurology22, 2 (2016), 404–418

    2016

  45. [53]

    Martha E Pollack. 2005. Intelligent technology for an aging population: The use of AI to assist elders with cognitive impairment.AI magazine26, 2 (2005), 9–9

    2005

  46. [54]

    Martha E Pollack, Laura Brown, Dirk Colbry, Colleen E McCarthy, Cheryl Orosz, Bart Peintner, Sailesh Ramakrishnan, and Ioannis Tsamardinos. 2003. Autominder: An intelligent cognitive orthotic system for people with memory impairment. Robotics and autonomous systems44, 3-4 (200...

    2003

  47. [55]

    Alisha Pradhan, Amanda Lazar, and Leah Findlater. 2020. Use of intelligent voice assistants by older adults with low technology use.ACM Transactions on Computer-Human Interaction (TOCHI)27, 4 (2020), 1–27

    2020

  48. [56]

    Nisha Raghunath, Jessamyn Dahmen, Katelyn Brown, Diane Cook, and Maureen Schmitter-Edgecombe. 2020. Creating a digital memory notebook application for individuals with mild cognitive impairment to support everyday functioning. Disability and Rehabilitation: Assistive Technology(2020)

    2020

  49. [57]

    Lena Rosenberg, Anders Kottorp, Bengt Winblad, and Louise Nygård. 2009. Per- ceived difficulty in everyday technology use among older adults with or without cognitive deficits.Scandinavian journal of occupational therapy16, 4 (2009), 216–226

    2009

  50. [58]

    Kelly A Ryan, Annie Weldon, Nicole M Huby, Carol Persad, Arijit K Bhaumik, Judith L Heidebrink, Nancy Barbas, Nancy Staffend, Lindsay Franti, and Bruno Giordani. 2010. Caregiver support service needs for patients with mild cognitive impairment and Alzheimer disease.Alzheimer D...

    2010

  51. [59]

    Mahadev Satyanarayanan. 2001. Pervasive computing: Vision and challenges. IEEE Personal communications8, 4 (2001), 10–17

    2001

  52. [60]

    Laura I Schmidt and Hans-Werner Wahl. 2019. Predictors of performance in everyday technology tasks in older adults with and without mild cognitive impairment.The Gerontologist59, 1 (2019), 90–100

    2019

  53. [61]

    Hey Alexa, What’s Up?

    Alex Sciuto, Arnita Saini, Jodi Forlizzi, and Jason I Hong. 2018. " Hey Alexa, What’s Up?" A Mixed-Methods Studies of In-Home Conversational Agent Usage. Insights from Designing Context-Aware Meal Preparation Assistance for Older Adults with MCI and Their Care Partners DIS ’25...

    2018

  54. [62]

    L Shrinidhi, Nalinadevi Kadiresan, and Latha Parameswaran. 2022. A novel approach for context-aware sensor optimization in a smart home.Procedia Computer Science215 (2022), 350–360

    2022

  55. [63]

    Vince Stanford. 2002. Using pervasive computing to deliver elder care.IEEE Pervasive computing1, 1 (2002), 10–13

    2002

  56. [64]

    Yuta Sugiura, Daisuke Sakamoto, Anusha Withana, Masahiko Inami, and Takeo Igarashi. 2010. Cooking with robots: designing a household system working in open environments. InProceedings of the SIGCHI conference on human factors in computing systems. 2427–2430

    2010

  57. [65]

    John Vines, Gary Pritchard, Peter Wright, Patrick Olivier, and Katie Brittain. 2015. An age-old problem: Examining the discourses of ageing in HCI and strategies for future research.ACM Transactions on Computer-Human Interaction (TOCHI) 22, 1 (2015), 1–27

    2015

  58. [66]

    Johanna Weber, Margarita Esau-Held, Marvin Schiller, Eike Martin Thaden, Diet- rich Manstetten, and Gunnar Stevens. 2023. Designing an Interaction Concept for Assisted Cooking in Smart Kitchens: Focus on Human Agency, Proactivity, and Multimodality. InProceedings of the 2023 A...

    2023

  59. [67]

    Amel Yaddaden, Mélanie Couture, Mireille Gagnon-Roy, Patricia Belchior, Maxime Lussier, Carolina Bottari, Sylvain Giroux, Hélène Pigot, and Nathalie Bier. 2020. Using a cognitive orthosis to support older adults during meal prepa- ration: Clinicians’ perspective on COOK techno...

    2020

  60. [68]

    MinYoung Yoo, William Odom, Arne Berger, Samuel Barnett, Sadhbh Kenny, Priscilla Lo, Samein Shamsher, Gillian Russell, and Lauren Knight. 2024. Remem- bering through Sound: Co-creating Sound-based Mementos together with People with Blindness. InProceedings of the 2024 CHI Conf...

    2024

  61. [69]

    Ruby Yu, D Lai, G Leung, C Tong, S Yuen, and J Woo. 2023. A Dyadic Cooking- Based Intervention for Improving Subjective Health and Well-Being of Older Adults with Subjective Cognitive Decline and Their Caregivers: A Randomized Controlled Trial.The Journal of nutrition, health ...

    2023

  62. [70]

    Tamara Zubatiy, Kayci L Vickers, Niharika Mathur, and Elizabeth D Mynatt. 2021. Empowering dyads of older adults with mild cognitive impairment and their care partners using conversational agents. InProceedings of the 2021 CHI conference on human factors in computing systems. 1–15

    2021

  63. [71]

    Tamara Zubatiy, Kayci L Vickers, Jessica L Saurman, Felicia Goldstein, Amy Ro- driguez, Niharika Mathur, and Elizabeth Mynatt. 2023. A Distributed Cognition Approach to Understanding Compensatory Calendaring Cognitive Systems of Older Adults with Mild Cognitive Impairment and ...

    2023

  64. [2014]

    A smart kitchen for ambient assisted living.Sensors14, 1 (2014), 1629–1653

    2014

  65. [2015]

    In20th International Conference on Engineering Design: Design for Life

    Remember to remember: A feasibility study adapting wearable technology to the needs of people aged 65 and older with Mild Cognitive Impairment (MCI) and Alzheimer’s Dementia. In20th International Conference on Engineering Design: Design for Life. Design Society, 331–340

  66. [2019]

    InProceedings of the 12th ACM international conference on pervasive technologies related to assistive environments

    A reminder system for independence in dementia care: a case study in an assisted living facility. InProceedings of the 12th ACM international conference on pervasive technologies related to assistive environments. 176–185

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.