Exploring the viability of robotic technology integrated with Vivaldi artificial intelligence for functional assessment in amyotrophic lateral sclerosis
In this study, we explore the feasibility and efficacy of leveraging Sanbot Elf – a humanoid intelligent assistive robot – integrated with artificial intelligence (AI), specifically the Vivaldi AI system, for functional assessment in amyotrophic lateral sclerosis (ALS) patients. Our investigation involves evaluating and comparing the performance of the Sanbot Elf in administering the ALS Functional Rating Scale–Revised (ALSFRS-R) to that of human operators, using a structured format where patients respond with either “yes” or “no” answers. This approach is intentionally adopted to minimize ambiguity in patient responses. Patients were given the option to respond either verbally or by utilizing the touchscreen display, particularly beneficial for those experiencing dysarthria or hypophonia. In addition, we examined patient emotional responses to this novel approach. A cohort of 28 ALS patients participated in the study, with a subset undergoing longitudinal follow-up assessments. Our results demonstrate strong agreement between human and robotic administrations of the ALSFRS-R, indicating the potential for AI-enabled robotics to accurately assess ALS functional status. Furthermore, the patients’ feedback underscores their acceptability of this technology as a supportive tool in healthcare settings. Our findings also highlight the potential benefits of employing robotic devices with algorithmic capabilities, such as the binary tree method, in hospitals. Moreover, such integration has the potential to alleviate operators’ workload. Importantly, this research contributes to the burgeoning field of AI-enabled healthcare operations, highlighting the promising role of robotic systems in enhancing functional assessment and management of ALS.
- Hardiman O, Al-Chalabi A, Chio A, et al. Amyotrophic lateral sclerosis. Nat Rev Dis Primers. 2017;3:17071. doi: 10.1038/nrdp.2017.71
- Kiernan MC, Vucic S, Cheah BC, et al. Amyotrophic lateral sclerosis. Lancet. 2011;377(9769):942-955. doi: 10.1016/S0140-6736(10)61156-7
- Brown RH, Al-Chalabi A. Amyotrophic lateral sclerosis. N Engl J Med. 2017;377(2):162-172. doi: 10.1056/NEJMra1603471
- Chio A, Calvo A, Moglia C, Mazzini L, Mora G, PARALS Study Group. Phenotypic heterogeneity of amyotrophic lateral sclerosis: A population based study. J Neurol Neurosurg Psychiatry. 2011;82(7):740-746. doi: 10.1136/jnnp.2010.235952
- Franchignoni F, Mora G, Giordano A, Volanti P, Chio A. Evidence of multidimensionality in the ALSFRS-R Scale: A critical appraisal on its measurement properties using Rasch analysis. J Neurol Neurosurg Psychiatry. 2013;84(12):1340-1345. doi: 10.1136/jnnp-2012-304701
- European College of Neuropsychopharmacology. Clinical investigation of medicinal products for treatment of Amyotrophic Lateral Sclerosis (ALS). Eur Neuropsychopharmacol. 2001;11(2):187-189. doi: 10.1016/s0924-977x(01)00067-0
- Gordon PH, Miller RG, Moore DH. Alsfrs-R. Amyotroph Lateral Scler Other Motor Neuron Disord. 2004;5(Suppl 1):90-93. doi: 10.1080/17434470410019906
- Kaufmann P, Levy G, Thompson JLP, et al. The ALSFRSr predicts survival time in an ALS clinic population. Neurology. 2005;64(1):38-43. doi: 10.1212/01.WNL.0000148648.38313.64
- Miller RG, Moore DH 2nd, Gelinas DF, et al. Phase III randomized trial of gabapentin in patients with amyotrophic lateral sclerosis. Neurology. 2001;56(7):843-848. doi: 10.1016/s0022-510x(01)00632-3
- Pugliese R, Sala R, Regondi S, Beltrami B, Lunetta C. Emerging technologies for management of patients with amyotrophic lateral sclerosis: From telehealth to assistive robotics and neural interfaces. J Neurol. 2022;269(6):2910- 2921. doi: 10.1007/s00415-022-10971-w
- Maier A, Eicher C, Kiselev J, et al. Acceptance of enhanced robotic assistance systems in people with amyotrophic lateral sclerosis-associated motor impairment: Observational online study. JMIR Rehabil Assist Technol. 2021;8(4):e18972. doi: 10.2196/18972
- Coser O, Tamantini C, Soda P, Zollo L. AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: A review. Front Robot AI. 2024;11:1341580. doi: 10.3389/frobt.2024.1341580
- Botta M, Camilleri D, Cena F, et al. Cloud-based user modeling for social robots: A first attempt. arXiv. 2022.
- Beraldo G, Menegatti E, de Tommasi V, Mancin R, Benini F. A Preliminary Investigation of Using Humanoid Social Robots as Non-pharmacological Techniques With Children. In: 15th IEEE International Conference on Advanced Robotics and its Social Impacts; 2019. doi: 10.1109/ARSO46408.2019.8948760
- Kimmig R, Verheijen RHM, Rudnicki M, for SERGS Council. Robot assisted surgery during the COVID-19 pandemic, especially for gynecological cancer: A statement of the Society of European Robotic Gynaecological Surgery (SERGS). J Gynecol Oncol. 2020;31(3):e59. doi: 10.3802/jgo.2020.31.e59
- Bauer J, Dengler S, Faubel L, et al. Pandemic robot. Curr Dir Biomed Eng. 2021;7(2):601-604. doi: 10.1515/cdbme-2021-2153
- Di Napoli C, Ercolano G, Rossi S. Personalized home-care support for the elderly: A field experience with a social robot at home. User Model User Adap Inter. 2022;33:405-440. doi: 10.1007/s11257-022-09333-y
- Gena C, Botta M, Cena F, Mattutino C. User modeling for social robots. arXiv. 2021. doi: 10.5281/zenodo.4781442
- Gao A, Murphy RR, Chen W, et al. Progress in robotics for combating infectious diseases. Sci Robot. 2021;6(52):eabf1462. doi: 10.1126/scirobotics.abf1462
- Amabili G, Maranesi E, Margaritini A, et al. Usability and feasibility assessment of a social assistive robot for the older people: Results from the GUARDIAN project. Bioengineering (Basel). 2023;11(1):20. doi: 10.3390/bioengineering11010020
- Luperto M, Monroy J, Renoux J, et al. Integrating social assistive robots, IoT, virtual communities and smart objects to assist at-home independently living elders: The movecare project. Int J Soc Robot. 2023;15(3):517-545. doi: 10.1007/s12369-021-00843-0
- Ghafurian M, Hoey J, Dautenhahn K. Social robots for the care of persons with dementia: A systematic review. ACM Trans Hum Robot Interact. 2021;10(4):1-31. doi: 10.1145/346965
- Saunders J, Syrdal DS, Koay KL, Burke N. “Teach me-show me”-End-user personalization of a smart home and companion robot. IEEE Trans Hum Mach Syst. 2016;46(1):27-40.
- Schroeter C, Mueller S, Volkhardt M, et al. Realization and User Evaluation of a Companion Robot for People with Mild Cognitive Impairments. In: 2013 IEEE International Conference on Robotics and Automation. Vol. 1. IEEE; 2013. doi: 10.1109/ICRA.2013.6630717
- Fischinger D, Einramhof P, Papoutsakis K, et al. Hobbit, a care robot supporting independent living at home: First prototype and lessons learned. Robot Auton Syst. 2016;75(Part A):60-78. doi: 10.1016/j.robot.2014.09.029
- Casey D, Felzmann H, Pegman G, et al. What people with dementia want: Designing MARIO an acceptable robot companion. In: Computers Helping People with Special Needs. Vol. 1. Cham: Springer; 2016. doi: 10.1007/978-3-319-41264-1_44
- Neerincx MA, van Vught W, Blanson Henkemans O, et al. Socio-cognitive engineering of a robotic partner for child’s diabetes self-management. Front Robot AI. 2019;6:118. doi: 10.3389/frobt.2019.00118
- Brooks BR, Miller RG, Swash M, Munsat TL, World Federation of Neurology Research Group on Motor Neuron Diseases. El Escorial revisited: Revised criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotroph Lateral Scler Other Motor Neuron Disord. 2000;1(5):293-299. doi: 10.1080/146608200300079536
- Shahid A, Wilkinson K, Marcu S, Shapiro CM. State-Trait Anxiety Inventory (STAI). United Kingdom: Psychology Press; 2011. doi: 10.1007/978-1-4419-9893-4_90
- John OP, Naumann LP. Paradigm shift to the integrative Big Five trait taxonomy: History, measurement, and conceptual issues. In: Handbook of Personality: Theory and Research. New York: The Guilford Press; 2008.
- Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1(8476):307-310.
- Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res. 1999;8(2):135-160. doi: 10.1177/096228029900800204
- Ranganathan P, Pramesh CS, Aggarwal R. Common pitfalls in statistical analysis: Measures of agreement. Perspect Clin Res. 2017;8(4):187-191. doi: 10.4103/picr.PICR_123_17
- Koo TK, Li MY. A Guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155-163. doi: 10.1016/j.jcm.2016.02.012