Min Hun Lee
ABSTRACT
Older adult care technologies are increasingly explored to support the independent living of older adults by monitoring their abnormal activities and informing caregivers to provide intervention if necessary. However, the adoption of these technologies remains challenging due to several factors (e.g. lack of usability). In this work, we present a human-centered, intelligent system for older adult care. Our proposed designs of the system were created based on the findings from a focus group session with caregivers. This system monitors the abnormal activities of an older adult using wireless motion sensors and machine learning models. In addition, unlike previous work that only notifies an outcome of activity recognition and abnormal detection models to a caregiver, the system supports interactive dialogue responses to explain the abnormal activities of an older adult to a caregiver and allow the caregiver to elicit additional information about the older adult and the older adult to proactively share his/her status with the caregiver for an adequate intervention.
- Clara Berridge, Yuanjin Zhou, Amanda Lazar, Anupreet Porwal, Nora Mattek, Sarah Gothard, and Jeffrey Kaye. 2022. Control Matters in Elder Care Technology: Evidence and Direction for Designing It In. In Designing Interactive Systems Conference. 1831–1848.Google Scholar
- Andrew BL Berry, Catherine Y Lim, Tad Hirsch, Andrea L Hartzler, Linda M Kiel, Zoë A Bermet, and James D Ralston. 2019. Supporting communication about values between people with multiple chronic conditions and their providers. In proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–14.Google ScholarDigital Library
- Stephanie Blackman, Claudine Matlo, Charisse Bobrovitskiy, Ashley Waldoch, Mei Lan Fang, Piper Jackson, Alex Mihailidis, Louise Nygård, Arlene Astell, and Andrew Sixsmith. 2016. Ambient assisted living technologies for aging well: a scoping review. Journal of Intelligent Systems 25, 1 (2016), 55–69.Google ScholarCross Ref
- Virginia Braun and Victoria Clarke. 2019. Reflecting on reflexive thematic analysis. Qualitative research in sport, exercise and health 11, 4 (2019), 589–597.Google Scholar
- Dany Fortin-Simard, Jean-Sébastien Bilodeau, Kevin Bouchard, Sebastien Gaboury, Bruno Bouchard, and Abdenour Bouzouane. 2015. Exploiting passive RFID technology for activity recognition in smart homes. IEEE Intelligent systems 30, 4 (2015), 7–15.Google ScholarDigital Library
- Connie Guan, Anya Bouzida, Ramzy M Oncy-Avila, Sanika Moharana, and Laurel D Riek. 2021. Taking an (embodied) cue from community health: Designing dementia caregiver support technology to advance health equity. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–16.Google ScholarDigital Library
- Kiryong Ha, Zhuo Chen, Wenlu Hu, Wolfgang Richter, Padmanabhan Pillai, and Mahadev Satyanarayanan. 2014. Towards wearable cognitive assistance. In Proceedings of the 12th annual international conference on Mobile systems, applications, and services. 68–81.Google ScholarDigital Library
- Christina N Harrington, Lauren Wilcox, Kay Connelly, Wendy Rogers, and Jon Sanford. 2018. Designing health and fitness apps with older adults: Examining the value of experience-based co-design. In Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare. 15–24.Google ScholarDigital Library
- Tsipi Heart and Efrat Kalderon. 2013. Older adults: are they ready to adopt health-related ICT?International journal of medical informatics 82, 11 (2013), e209–e231.Google Scholar
- Min Hun Lee, Daniel P Siewiorek, Asim Smailagic, Alexandre Bernardino, and Sergi Bermudez i Badia. 2023. Design, development, and evaluation of an interactive personalized social robot to monitor and coach post-stroke rehabilitation exercises. User Modeling and User-Adapted Interaction 33, 2 (2023), 545–569.Google ScholarDigital Library
- 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 Technologies 5, 2 (2021), 1–19.Google ScholarDigital Library
- Min Hun Lee, Daniel P Siewiorek, Asim Smailagic, Alexandre Bernardino, and Sergi Bermúdez i Badia. 2022. Enabling AI and Robotic Coaches for Physical Rehabilitation Therapy: Iterative Design and Evaluation with Therapists and Post-stroke Survivors. International Journal of Social Robotics (2022), 1–22.Google ScholarCross Ref
- Matthew L Lee and Anind K Dey. 2015. Sensor-based observations of daily living for aging in place. Personal and Ubiquitous Computing 19, 1 (2015), 27–43.Google ScholarDigital Library
- Nora McDonald, Sarita Schoenebeck, and Andrea Forte. 2019. Reliability and inter-rater reliability in qualitative research: Norms and guidelines for CSCW and HCI practice. Proceedings of the ACM on human-computer interaction 3, CSCW (2019), 1–23.Google ScholarDigital Library
- Leysan Nurgalieva, Alisa Frik, Francesco Ceschel, Serge Egelman, and Maurizio Marchese. 2019. Information design in an aged care context: Views of older adults on information sharing in a care triad. In Proceedings of the 13th EAI international conference on pervasive computing technologies for healthcare. 101–110.Google ScholarDigital Library
- Henry Friday Nweke, Ying Wah Teh, Mohammed Ali Al-Garadi, and Uzoma Rita Alo. 2018. Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Systems with Applications 105 (2018), 233–261.Google ScholarCross Ref
- Fco Ordóñez, Paula De Toledo, Araceli Sanchis, 2013. Activity recognition using hybrid generative/discriminative models on home environments using binary sensors. Sensors 13, 5 (2013), 5460–5477.Google ScholarCross Ref
- Francisco Javier Ordonez, Gwenn Englebienne, Paula De Toledo, Tim Van Kasteren, Araceli Sanchis, and Ben Kröse. 2014. In-home activity recognition: Bayesian inference for hidden Markov models. IEEE Pervasive Computing 13, 3 (2014), 67–75.Google ScholarCross Ref
- Lawrence R Rabiner. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77, 2 (1989), 257–286.Google ScholarCross Ref
- Parisa Rashidi and Alex Mihailidis. 2012. A survey on ambient-assisted living tools for older adults. IEEE journal of biomedical and health informatics 17, 3 (2012), 579–590.Google Scholar
- Cynthia Rudin. 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature machine intelligence 1, 5 (2019), 206–215.Google Scholar
- Jae Hyuk Shin, Boreom Lee, and Kwang Suk Park. 2011. Detection of abnormal living patterns for elderly living alone using support vector data description. IEEE Transactions on Information Technology in Biomedicine 15, 3 (2011), 438–448.Google ScholarDigital Library
- Alan Yusheng Wu and Cosmin Munteanu. 2018. Understanding older users’ acceptance of wearable interfaces for sensor-based fall risk assessment. In Proceedings of the 2018 CHI conference on human factors in computing systems. 1–13.Google ScholarDigital Library
- Salifu Yusif, Jeffrey Soar, and Abdul Hafeez-Baig. 2016. Older people, assistive technologies, and the barriers to adoption: A systematic review. International journal of medical informatics 94 (2016), 112–116.Google ScholarCross Ref
- 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. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–15.Google ScholarDigital Library
Index Terms
- Designing a Human-Centered Intelligent System to Monitor & Explain Abnormal Patterns of Older Adults
Recommendations
Understanding Older Adults' Perceptions of In-Home Sensors Using an Obtrusiveness Framework
Proceedings, Part II, of the 10th International Conference on Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience - Volume 9744The aim of this study was to determine if dimensions and sub-categories of a previously-tested obtrusiveness framework were represented in interviews conducted with community-dwelling older adults at three- and six-month study visits during an in-home ...
Exploring autonomy in the design of an intelligent health assistant for older adults
IUI '19 Companion: Companion Proceedings of the 24th International Conference on Intelligent User InterfacesIn the last few years, interest in intelligent voice assistants (IVAs) to aid older adults in living healthier and longer at home has seen a drastic increase. As these devices become more integrated into the lives of older adults to assist with health ...
Recruiting Older Adults in the Wild: Reflections on Challenges and Lessons Learned from Research Experience
PervasiveHealth '18: Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for HealthcareIt is important to understand the older adults prior to the design process. The understanding can better facilitate design conversations between the researchers and the older adults. In this paper, we discussed our experiences of building a relationship ...
Comments