Assistive technologies are playing vital role in frail seniors.
OBJECTIVE:
The paper presents the methodology and findings from the assessment of an active “intelligent” robotic rollator incorporating navigation assistance in frail seniors.
METHODS:
Thirty seniors, having moderate motor impairment were grouped into two categories according to their cognitive status. A complicated trail was defined to test their performance using a robotic rollator. The main descriptive statistics were given, while inference was performed via the corresponding parametric tests and the non-parametric Mann-Whitney U or Kruskal-Wallis tests. A principal component analysis was utilized to extract the variable classes that explain the overall behavior of the sample.
RESULTS:
Navigation assistance was proved helpful to the cognitive impaired group. The results between the two groups were comparable in terms of completion time and success rate. The provided directional audio cues led to smoother walking paths and better orientation. There was no correlation between the MMSE values and the total time of the walk ( 0.696 0.05 for Pearson’s correlation between MMSE and total walk time).
CONCLUSIONS:
The integration of navigation assistance in mobility assistance devices can help the elderly to effectively orient themselves in challenging and unknown environments.
WHO. Chapter 1: Global Health: today’s challenges [Internet]. Who.int. 2020 [cited 30 January 2020]. Available from: https://www.who.int/whr/2003/chapter1/en/index1.html
2.
United Nations, Department of Economic and Social Affairs, Population Division; 2017. World Population Prospects: The 2017 Revision, Key Findings and Advance Tables. (ESA/P/ WP/248).
3.
World Health Organization [Internet]. Ageing and Health. [cited 30 January 2020]. Available from: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health.
4.
United Nations, Department of Economic and Social Affairs, Population Division; 2015. World Population Ageing 2015 (ST/ESA/SER.A/390).
5.
United States Census Bureau [Internet]. Mobility is Most Common Disability Among Older Americans, Census Bureau Reports. [cited 30 January 2020]. Available from: https://www.census.gov/newsroom/press-releases/2014/cb14-218.html.
6.
WalshTCaraceniAFainsingerRFoleyKGlarePGohC, et al. Palliative Medicine. 1st ed.. Elsevier; 2009, pp. 570-573.
7.
CharronPKirbyRMacLeodD. Epidemiology of walker-related injuries and deaths in the united states. American Journal of Physical Medicine & Rehabilitation. 1995; 74(3): 237-239. doi: 10.1097/00002060-199505000-00011.
8.
Roman de MettelingeTCambierD. Understanding the relationship between walking aids and falls in older adults. Journal of Geriatric Physical Therapy. 2015; 38(3): 127-132. doi: 10.1519/jpt.0000000000000031.
9.
FortinJDYeawEMCampbellSJamesonS. An analysis of risk assessment tools for falls in the elderly. Home Healthc Nurse. 1998; 16: 624-629.
10.
BateniHMakiB. Assistive devices for balance and mobility: benefits, demands, and adverse consequences. Arch Phys Med Rehabil. 2005; 86(1): 134-145.
11.
StevensJAThomasKTheL, et al. Unintentional fall injuries associated with walkers and canes in older adults treated in U.S. emergency departments. J Am Geriatr Soc. 2009; 57(8): 1464-1469.
12.
MartinsMMFrizera-NetoAUrendesEdos SantosCCeresRBastos-FilhoT. A novel human-machine interface for guiding: The NeoASAS smart walker. Proceedings of the 2012 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC); Manaus; 2012, pp. 1-7. 2.
13.
CetinEMuzemboJPardessusV, et al. Impact of different types of walking aids on the physiological energy cost during gait for elderly individuals with several pathologies and dependent on a technical aid for walking. Ann Phys Rehabil Med. 2010 Aug-Sep; 53(6-7): 399-405.
14.
ThomasSHalbertJMackintoshS, et al. Walking aid use after discharge following hip fracture is rarely reviewed and often inappropriate: an observational study. J Physiother. 2010; 56(4): 267-272.
15.
SchererMJ. The impact of assistive technology on the lives of people with disabilities. In: GrayDBQuatranoLALiebermanML, eds. Designing and using assistive technology: the human perspective. Baltimore: PH Brooks; 1998, pp. 99-116.
16.
AkinboSRSokunbiOOgunbameruT. Factors contributing to possession of walking aids among persons with osteoarthritis of the knee. Nig Q J Hosp Med. 2008 Apr-Jun; 18(2): 64-68.
17.
LezzoniLIRaoSRKinkelRP. Patterns of mobility aid use among working-age persons with multiple sclerosis living in the community in the United States. Disabil Health J. 2009 Apr; 2(2): 67-76.
18.
Van der LoosHMReinkensmeyerDJ. Rehabilitation and Health Care Robotics. In: SicilianoBKhatibO, eds. Springer Handbook of Robotics. Springer, Berlin, Heidelberg.
19.
FrizeraACeresRPonsLJ, et al. The Smart Walkers as Geriatric Assistive Device. the Simbiosis Purpose. In Proceedings of the 6th International Conference of the International Society for Gerontechnology; 2008. pp. 1-6.
20.
MartinsMMSantosCPFrizera-NetoA, et al. Assistive mobility devices focusing on smart walkers: classification and review. Robotics and Autonomous Systems. 2012 Apr; 60(4): 548-562.
21.
KoCAgrawalSK. Walk-Assist Robot: A Novel Approach to Gain Selection of a Braking Controller Using Differential Flatness. Proceedings of the 2010 American Control Conference; Baltimore, MD; 2010, pp. 2799-2804.
22.
KulyukinVKutiyanawalaALoPrestiE, et al. iWalker: Toward a Rollator-Mounted Wayfinding System for the Elderly. In 2008 IEEE International Conference On RFID; 2008; pp. 303-311.
23.
SpenkoMYuHDubowskyS. Robotic personal aids for mobility and monitoring for the elderly. IEEE Trans Neural Syst Rehabil Eng. 2006 Sep; 14(3): 344-351.
24.
ShinJIttenDRusakovA, et al. SmartWalker: Towards an Intelligent Robotic Walker for the Elderly. In 2015 International Conference on Intelligent Environments, Prague; 2015, 9-16.
25.
GeunhoLJungEJOhnumaT, et al. JAIST Robotic Walker Control Based on a Two-Layered Kalman Filter. In 2011 IEEE International Conference on Robotics and Automation, Shanghai; 2011, pp. 3682-3687.
26.
GrafBMatthiasHRolfS. Care-o-bot II – development of a next generation robotic home assistant. Autonomous Robots. 2004; 16(2): 193-205.
27.
ThordardottirBFängeAMLethinCGattaDRChiattiC. Acceptance and Use of Innovative Assistive Technologies among People with Cognitive Impairment and Their Caregivers: A Systematic Review. BioMed Research International. 2019; Article ID 9196729, 18 pages. doi: 10.1155/2019/9196729.
28.
KoumpourosY. A Systematic Review on Existing Measures for the Subjective Assessment of Rehabilitation and Assistive Robot Devices. Journal of Healthcare Engineering. 2016; Article ID 1048964. doi: 10.1155/2016/1048964.
29.
MOBOT project website [Internet]. MOBOT project. [cited 23 January 2020]. Available from: http://www.mobot-project.eu/?Description.
30.
GeravandMKorondiPZWernerC, et al. Human sit-to-stand transfer modeling towards intuitive and biologically-inspired robot assistance. Auton Robots. 2017; 41: 575-592.
31.
PeerAGeravandMTzafestasCSMoustrisGP. Deliverable D3.2: Report on autonomous navigation algorithms, online configuration optimization, motion control, and physical human-robot interaction and collaboration. 2015.
32.
WernerCMoustrisGPTzafestasCS, et al. User-oriented evaluation of a robotic rollator that provides navigation assistance in frail older adults with and without cognitive impairment. Gerontology. 2018; 64(3): 278-290.
33.
MoustrisGPTzafestasCS. Intention-based front-following control for an intelligent robotic rollator in indoor environments. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI); 6–9 Dec. 2016, pp. 1-7.
34.
GeravandMWernerCHauerK, et al. An integrated decision making approach for adaptive shared control of mobility assistance robots. Int J Soc Robot. 2016; 8(5): 631-648.
35.
EfthimiouEFotineaSEGoulasT, et al. The MOBOT Platform – Showcasing Multimodality in Human-Assistive Robot Interaction. In Universal Access in Human-Computer Interaction Techniques and Environments: 10th International Conference, UAHCI 2016, Held as Part of HCI International 2016, Toronto, ON, Canada, July 17–22, 2016, Proceedings, Part II. Edited by Antona M, Stephanidis C. Cham: Springer International Publishing; 2016, pp. 382-391.
36.
QuigleyMConleyKGerkeyB, et al. ROS: An Open-Source Robot Operating System. In ICRA Workshop on Open Source Software. Kobe, Japan; 2009.
37.
EmmiLGonzalez-de-SotoMPajaresG, et al. New Trends in Robotics for Agriculture: Integration and Assessment of a Real Fleet of Robots. The Scientific World Journal. 2014; Article ID: 04059. doi: 10.1155/2014/404059.
38.
ROS.org [Internet]. Navigation – ROS Wiki. [cited 28 January 2020]. Available from: http://wiki.ros.org/navigation.
39.
KlancarGZdesarABlazicS, et al. Wheeled Mobile Robotics: From Fundametals Towards Autonomous Systems. Butterworth-Heinemann; 2017.
40.
BallagiÁTihamérÁ. Fuzzy odometry error approximation of a mobile robot. Annals of Craiova University, series. Automation, Computers, Electronics and Mechatronics. 2007; 4(31): 13-17.
41.
ThrunSFoxDBurgardW, et al. Robust monte carlo localization for mobile robots. Artificial Intelligence. 2001; 128: 99-141.
42.
KoumpourosYKaravasiliAPapageorgiouE, et al. Validation of the Greek version of the device subscale of the Quebec user evaluation of satisfaction with assistive technology 2.0 (Quest 2.0). Assistive Technology. 2016; 28: 152-158.
43.
KoumpourosYPapageorgiouEKaravasiliA. Development of a new psychometric scale (PYTHEIA) to assess the satisfaction of users with any assistive technology. In DuffyVGLightnerN, eds. Advances in Human Factors and Ergonomics in Healthcare Proceedings of the AHFE 2016 International Conference on Human Factors and Ergonomics in Healthcare, July 27–31. Walt Disney World, Florida, USA, Springer, 2016.
44.
KoumpourosYPapageorgiouEKaravasiliA, et al. Translation and validation of the assistive technology device predisposition assessment in Greek in order to assess satisfaction with use of the selected assistive device. Disabil Rehabil Assist Technol. 2017 Jul; 12(5): 535-542.
45.
KoumpourosYPapageorgiouEKaravasiliA, et al. PYTHEIA: A scale for assessing rehabilitation and assistive robotics. In ICPMRSM 2016: 18th International Conference on Physical Medicine, Rehabilitation and Sports Medicine, Nov 3–4. Cape Town, South Africa, 2016.
46.
ShahSVanclayFCooperB. Improving the sensitivity of the barthel index for stroke rehabilitation. Journal of Clinical Epidemiology. 1989; 42: 703-709.
47.
SheikhJIYesavageJA. Geriatric Depression Scale (GDS): Recent evidence and development of a shorter version. In BrinkTL, ed. Clinical Gerontology: A Guide to Assessment and Intervention. New York, Haworth Press; 1986, pp. 165-173.
48.
FountoulakisKNTsolakiMIacovidesA, et al. The validation of the short form of the geriatric depression scale (GDS) in greece. Aging Clin Exp Res. 1999; 11(6): 367-372.
49.
DelbaereKCloseJCTMikolaizakAS, et al. The falls efficacy scale international (FES-I). A comprehensive longitudinal validation study. Age and Ageing. 2010; 39(2): 210-216.
50.
TinettiME. Performance-oriented assessment of mobility problems in elderly patients. J Am Geriatr Soc. 1986; 34: 119-126.
51.
KoumpourosYPapageorgiouEKaravasiliA, et al. PYTHEIA: a scale for assessing rehabilitation and assistive robotics. International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering. 2016; 11: 522-526.
52.
KoumpourosYKaravasiliAEfthimiouE, et al. User evaluation of the MOBOT rollator type robotic mobility assistive device. Technologies. 2017; 5(4): 73.
53.
SchwenkMHauerKZieschangT, et al. Sensor-derived physical activity parameters can predict future falls in people with dementia. Gerontology. 2014; 60: 483-492.
54.
Montero-OdassoMMuirSWSpeechleyM. Dual-task complexity affects gait in people with mild cognitive impairment: the interplay between gait variability, dual tasking, and risk of falls. Arch Phys Med Rehabil. 2012; 93: 293-299.
55.
SteinfeldAFongTKaberD, et al. Goodrich, Common Metrics for Human-Robot Interaction. In HRI ’06 Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-robot Interaction; 2006, pp. 33-40.
56.
HauerKWernerCTzafestasCMoustrisGMaragosPPitsikalisVKardarisNRodomagoulakisIKoumpourosY. Deliverable D5.4: Report on performance metrics and final evaluation study. 2016.
