banner

Enhancing quality of life: Human-centered design of mobile and smartwatch applications for assisted ambient living

Gonçalo Augusto, Rui Duarte, Carlos Cunha

Abstract


Background: Assisted ambient living interfaces are technologies designed to improve the quality of life for people who require assistance with daily activities. They are crucial for individuals to maintain their independence for as long as possible. To this end, these interfaces have to be user-friendly, intuitive, and accessible, even for those who are not tech-savvy. Research in recent years indicates that people find it uncomfortable to wear invasive or large intrusive devices to monitor health status, and poor user interface design implies a lack of user engagement. Methods: This paper presents the design and implementation of non-intrusive mobile and smartwatch applications for detecting older adults when executing their routines. The solution uses an intuitive mobile application to set up beacons and incorporates biometric data acquired from the smartwatch to measure bio-signals correlated to the user’s location. User testing and interface evaluation are carried out using the User Experience Questionnaire (UEQ). Results: Six older adults participated in the evaluation of the interfaces. Results show that users found the interaction to be excellent in all the parameters of the UEQ in the evaluation of the mobile interface. For the smartwatch application, results vary from above average to excellent. Conclusions: The applications are intuitive and easy to use, and data obtained from integrating systems is essential to link information and provide feedback to the user.


Keywords


mobile application; smartwatch application; beacon technology; user experience; human-computer interaction

Full Text:

PDF

References


1. Covinsky KE, Palmer RM, Fortinsky RH, et al. Loss of independence in activities of daily living in older adults hospitalized with medical illnesses: Increased vulnerability with age. Journal of the American Geriatrics Society 2003; 51(4): 451–458. doi: 10.1046/j.1532-5415.2003.51152.x

2. Preece J, Sharp H, Rogers Y. Interaction Design: Beyond Human-Computer Interaction. John Wiley & Sons; 2015.

3. Nielsen J. Usability Engineering. Morgan Kaufmann; 1994.

4. ISO. Ergonomics of Human-System Interaction—Part 210: Human-Centred Design for Interactive Systems. ISO; 2019.

5. Neale DC, Carroll JM. The role of metaphors in user interface design. In: Handbook of Human-Computer Interaction. Elsevier; 1997. pp. 441–462.

6. Kwon LN, Yang DH, Hwang MG, et al. Automated classification of normal control and early-stage dementia based on activities of daily living (ADL) data acquired from smart home environment. International Journal of Environmental Research and Public Health 2021; 18(24): 13235. doi: 10.3390/ijerph182413235

7. van Kasteren Y, Bradford D, Zhang Q, et al. Understanding smart home sensor data for ageing in place through everyday household routines: A mixed method case study. JMIR Mhealth Uhealth 2017; 5(6): e52. doi: 10.2196/mhealth.5773

8. Caroux L, Consel C, Dupuy L, Sauzéon H. Towards context-aware assistive applications for aging in place via real-life-proof activity detection. Journal of Ambient Intelligence and Smart Environments 2018; 10(6): 445–459. doi: 10.3233/AIS-180505

9. Lach HW, Lorenz RA, Palmer JL, et al. Home monitoring to track activity and sleep patterns among older adults: A feasibility study. Computers, Informatics, Nursing: CIN 2019; 37(12): 628–637. doi: 10.1097/CIN.0000000000000569

10. Yu J, An N, Hassan T, Kong Q. A pilot study on a smart home for elders based on continuous in-home unobtrusive monitoring technology. Health Environments Research & Design Journal 2019; 12(3): 206–219. doi: 10.1177/1937586719826059

11. VandeWeerd C, Yalcin A, Aden-Buie G, et al. HomeSense: Design of an ambient home health and wellness monitoring platform for older adults. Health and Technology 2020; 10(5): 1291–1309. doi: 10.1007/s12553-019-00404-6

12. Wang Y, Yalcin A, VandeWeerd C. Health and wellness monitoring using ambient sensor networks. Journal of Ambient Intelligence and Smart Environments 2020; 12(2): 139–151. doi: 10.3233/AIS-200553

13. Austin J, Dodge HH, Riley T, et al. A smart-home system to unobtrusively and continuously assess loneliness in older adults. IEEE Journal of Translational Engineering in Health and Medicine 2016; 4: 2800311. doi: 10.1109/JTEHM.2016.2579638

14. Vildjiounaite E, Mäkelä SM, Keränen T, et al. Unsupervised illness recognition via in-home monitoring by depth cameras. Pervasive and Mobile Computing 2017; 38(1): 166–187. doi: 10.1016/j.pmcj.2016.07.004

15. Kim JY, Liu N, Tan HX, Chu CH. Unobtrusive monitoring to detect depression for elderly with chronic illnesses. IEEE Sensors Journal 2017; 17(17): 5694–5704. doi: 10.1109/JSEN.2017.2729594

16. Aramendi AA, Weakley A, Goenaga AA, et al. Automatic assessment of functional health decline in older adults based on smart home data. Journal of Biomedical Informatics 2018; 81: 119–130. doi: 10.1016/j.jbi.2018.03.009

17. Grgurić A, Mošmondor M, Huljenić D. The SmartHabits: An intelligent privacy-aware home care assistance system. Sensors 2019; 19(4): 907. doi: 10.3390/s19040907

18. Ghayvat H, Awais M, Pandya S, et al. Smart aging system: Uncovering the hidden wellness parameter for well-being monitoring and anomaly detection. Sensors 2019; 19(4): 766. doi: 10.3390/s19040766

19. Susnea I, Dumitriu L, Talmaciu M, et al. Unobtrusive monitoring the daily activity routine of elderly people living alone, with low-cost binary sensors. Sensors 2019; 19(10): 2264. doi: 10.3390/s19102264

20. Akl A, Snoek J, Mihailidis A. Unobtrusive detection of mild cognitive impairment in older adults through home monitoring. IEEE Journal of Biomedical and Health Informatics 2017; 21(2): 339–348. doi: 10.1109/JBHI.2015.2512273

21. Alberdi A, Weakley A, Schmitter-Edgecombe M, et al. Smart home-based prediction of multidomain symptoms related to alzheimer’s disease. IEEE Journal of Biomedical and Health Informatics 2018; 22(6): 1720–1731. doi: 10.1109/JBHI.2018.2798062

22. Ault L, Goubran R, Wallace B, et al. Smart home technology solution for night-time wandering in persons with dementia. Journal of Rehabilitation and Assistive Technologies Engineering 2020; 7: 1–8. doi: 10.1177/2055668320938591

23. Ahamed F, Shahrestani S, Cheung H. Internet of things and machine learning for healthy ageing: Identifying the early signs of dementia. Sensors 2020; 20(21): 6031. doi: 10.3390/s20216031

24. De Miguel K, Brunete A, Hernando M, Gambao E. Home camera-based fall detection system for the elderly. Sensors 2017; 17(12): 2864. doi: 10.3390/s17122864

25. Lotfi A, Albawendi S, Powell H, et al. Supporting independent living for older adults; Employing a visual based fall detection through analysing the motion and shape of the human body. IEEE Access 2018; 6: 70272–70282. doi: 10.1109/ACCESS.2018.2881237

26. Hu Y, Zhang F, Wu C, et al. A wifi-based passive fall detection system. In: Proceedings of the ICASSP 2020—2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 4–8 May 2020; Barcelona, Spain. pp. 1723–1727.

27. Wang B, Guo L, Zhang H, Guo YX. A millimetre-wave radar-based fall detection method using line kernel convolutional neural network. IEEE Sensors Journal 2020; 20(22): 13364–13370. doi: 10.1109/JSEN.2020.3006918

28. Ding C, Ding Z, Wang L, Jia Y. A fall detection method based on K-nearest neighbor algorithm with MIMO millimeter-wave radar. In: Proceedings of the 2021 IEEE 6th International Conference on Signal and Image Processing (ICSIP); 22–24 October 2021; Nanjing, China. pp. 624–628.

29. Ruan W, Sheng QZ, Yao L, et al. HOI-Loc: Towards unobstructive human localization with probabilistic multi-sensor fusion. In: Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops); 14–18 March 2016; Sydney, Australia. pp. 1–4.

30. Lan G, Liang J, Liu G, Hao Q. Development of a smart floor for target localization with bayesian binary sensing. In: Proceedings of the 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA); 27–29 March 2017; Taipei, Taiwan. pp. 447–453.

31. Lima M, Morgado JF, Duarte RP. Low-cost embedded system for customer loyalty. In: Yang XS, Sherratt S, Dey N, et al. (editors). Lecture Notes in Networks and Systems, Proceedings of Sixth International Congress on Information and Communication Technology; 25–26 February 2021; Singapore. Springer Singapore; 2022. Volume 216, pp. 777–785.

32. Kim JY, Chu CH, Kang MS. IoT-based unobtrusive sensing for sleep quality monitoring and assessment. IEEE Sensors Journal 2021; 21(3): 3799–3809. doi: 10.1109/JSEN.2020.3022915

33. König A, Crispim Junior CF, Derreumaux A, et al. Validation of an automatic video monitoring system for the detection of instrumental activities of daily living in dementia patients. Journal of Alzheimer’s disease: JAD 2015; 44(2): 675–685. doi: 10.3233/JAD-141767

34. Xia S, Liu Y, Yuan G, et al. Indoor fingerprint positioning based on Wi-Fi: An overview. International Journal of Geo-Information 2017; 6(5): 135. doi: 10.3390/ijgi6050135

35. De Blasio G, Quesada-Arencibia A, Garcia CR, et al. A protocol-channel-based indoor positioning performance study for bluetooth low energy. IEEE Access 2018; 6: 33440–33450. doi: 10.1109/ACCESS.2018.2837497

36. Long N, Lei Y, Peng L, et al. A scoping review on monitoring mental health using smart wearable devices. Mathematical Biosciences and Engineering: MBE 2022; 19(8): 7899–7919. doi: 10.3934/mbe.2022369

37. Ju W, Leifer LJ. The design of implicit interactions: Making interactive systems less obnoxious. Design Issues 2008; 24(3): 72–84. doi: 10.1162/desi.2008.24.3.72

38. Memon M, Wagner SR, Pedersen CF, et al. Ambient assisted living healthcare frameworks, platforms, standards, and quality attributes. Sensors 2014; 14(3): 4312–4341. doi: 10.3390/s140304312

39. Carbonell N. Ambient multimodality: Towards advancing computer accessibility and assisted living. Universal Access in the Information Society 2006; 5(1): 96–104. doi: 10.1007/s10209-006-0027-y

40. Al-Shaqi R, Mourshed M, Rezgui Y. Progress in ambient assisted systems for independent living by the elderly. SpringerPlus 2016; 5(1): 624. doi: 10.1186/s40064-016-2272-8

41. Kautz H, Fox D, Etzioni O, et al. An overview of the assisted cognition project. American Association for Artificial Intelligence 2002.

42. Kientz JA, Patel SN, Jones B, et al. The Georgia Tech aware home. In: Proceedings of the CHI EA '08: CHI '08 Extended Abstracts on Human Factors in Computing Systems; 5–10 April 2008; Florence, Italy. pp. 3675–3680.

43. Cook DJ, Youngblood M, Heierman EO, et al. MavHome: An agent-based smart home. In: Proceedings of the First IEEE International Conference on Pervasive Computing and Communications (PerCom 2003); 26 March 2003; Fort Worth, TX, USA. pp. 521–524.

44. Lotfi A, Langensiepen C, Mahmoud SM, Akhlaghinia MJ. Smart homes for the elderly dementia sufferers: Identification and prediction of abnormal behaviour. Journal of Ambient Intelligence and Humanized Computing 2012; 3(3): 205–218. doi: 10.1007/s12652-010-0043-x

45. Bal M, Shen W, Hao Q, Xue H. Collaborative smart home technologies for senior independent living: A review. In: Proceedings of the 2011 15th International Conference on Computer Supported Cooperative Work in Design (CSCWD); 8–10 June 2011; Lausanne, Switzerland. pp. 481–488.

46. Rocha A, Martins A, Freire Junior JC, et al. Innovations in health care services: The CAALYX system. International Journal of Medical Informatics 2013; 82(11): e307–e320. doi: 10.1016/j.ijmedinf.2011.03.003

47. Wolf P, Schmidt AP, Klein M. SOPRANO—An extensible, open AAL platform for elderly people based on semantical contracts. In: Proceedings of the 3rd Workshop on Artificial Intelligence Techniques for Ambient Intelligence (AITAmI’08), 18th European Conference on Artificial Intelligence (ECAI 08); 21–22 July 2008; Patras, Greece.

48. Rivero-Espinosa J, Iglesias-Pérez A, Gutiérrez-Dueñas JA, Rafael-Palou X. SAAPHO: An AAL architecture to provide accessible and usable active aging services for the elderly. ACM SIGACCESS Accessibility and Computing 2013; (107): 17–24. doi: 10.1145/2535803.2535806

49. Rieman J, Franzke M, Redmiles D. Usability evaluation with the cognitive walkthrough. In: Proceedings of the Conference Companion on Human Factors in Computing Systems (CHI '95); 7–11 May 1995; Denver Colorado, USA. pp. 387–388.

50. Schrepp M, Thomaschewski J, Hinderks A. Design and evaluation of a short version of the user experience questionnaire (UEQ-S). International Journal of Interactive Multimedia and Artificial Intelligence 2017; 4(6): 103–108. doi: 10.9781/ijimai.2017.09.001

51. Boivie I, Aborg C, Persson J, Lofberg M. Why usability gets lost or usability in in-house software development. Interacting with Computers 15(4): 623–639. doi: 10.1016/S0953-5438(03)00055-9

52. MongoDB. Available online: https://www.mongodb.com (accessed on 2 May 2023).

53. Adam S, Mukasa KS, Breiner K, Trapp M. An apartment-based metaphor for intuitive interaction with ambient assisted living applications. In: Proceedings of People and Computers XXII Culture, Creativity, Interaction; 1–5 September 2008; Liverpool, UK. pp. 67–75. doi: 10.14236/ewic/HCI2008.7

54. Kalimullah K, Sushmitha D. Influence of design elements in mobile applications on user experience of elderly people. Procedia Computer Science 2017; 113: 352–359. doi: 10.1016/j.procs.2017.08.344

55. Johnson J, Finn K. Designing User Interfaces for an Aging Population: Towards Universal Design. Morgan Kaufmann; 2017.

56. Ide android studio. Available online: https://developer.android.com/studio (accessed on 17 May 2023).

57. Kotlin programming language. https://developer.android.com/kotlin (accessed on 17 May 2023).

58. Material design. Available online: https://m3.material.io (accessed on 17 May 2023).

59. Figma: The collaborative Interface Design Tool. Available online: https://www.figma.com (accessed on 17 May 2023).

60. Morville P, Rosenfeld L. Information Architecture for the World Wide Web: Designing Large-Scale Web Sites, 3rd ed. O'Reilly Media; 2007.

61. Czaja SJ, Boot WR, Charness N, Rogers WA. Designing for Older Adults: Principles and Creative Human Factors Approaches. CRC Press; 2019.




DOI: https://doi.org/10.32629/jai.v7i1.762

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Gonçalo Augusto, Rui Duarte, Carlos Cunha

License URL: https://creativecommons.org/licenses/by-nc/4.0/