Measuring Quality of Service in a Robotized Comprehensive Geriatric Assessment Scenario

Comprehensive Geriatric Assessment (CGA) is an integrated clinical process to evaluate frail elderly people in order to create therapy plans that improve their quality and quantity of life. The whole process includes the completion of standardized questionnaires or specific movements, which are performed by the patient and do not necessarily require the presence of a medical expert. With the aim of automatizing these parts of the CGA, we have designed and developed CLARC (smart CLinic Assistant Robot for CGA), a mobile robot able to help the physician to capture and manage data during the CGA procedures, mainly by autonomously conducting a set of predefined evaluation tests. Using CLARC to conduct geriatric tests will reduce the time medical professionals have to spend on purely mechanical tasks, giving them more time to develop individualised care plans for their patients. In fact, ideally, CLARC will perform these tests on its own. In parallel with the effort to correctly address the functional aspects, i.e., the development of the robot tasks, the design of CLARC must also deal with non-functional properties such as the degree of interaction or the performance. We argue that satisfying user preferences can be a good way to improve the acceptance of the robot by the patients. This paper describes the integration into the software architecture of the CLARC robot of the modules that allow these properties to be monitored at run-time, providing information on the quality of its service. Experimental evaluation illustrates that the defined quality of service metrics correctly capture the evolution of the aspects of the robot’s activity and its interaction with the patient covered by the non-functional properties that have been considered.

[1]  A. Bandera,et al.  Perceptions or Actions? Grounding How Agents Interact Within a Software Architecture for Cognitive Robotics , 2019, Cognitive Computation.

[2]  Peter Langhorne,et al.  Comprehensive geriatric assessment for older adults admitted to hospital: meta-analysis of randomised controlled trials , 2011, BMJ : British Medical Journal.

[3]  Juan F. Inglés-Romero,et al.  Modeling and Estimation of Non-functional Properties: Leveraging the Power of QoS Metrics , 2019, IWINAC.

[4]  Ana Iglesias,et al.  Towards long term acceptance of Socially Assistive Robots in retirement houses: use case definition , 2020, 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC).

[5]  Katherine M. Tsui,et al.  Accessible Human-Robot Interaction for Telepresence Robots: A Case Study , 2015, Paladyn J. Behav. Robotics.

[6]  Terrence Fong,et al.  Measuring robot performance in real-time for NASA robotic reconnaissance operations , 2009, PerMIS.

[7]  Praveen Damacharla,et al.  Common Metrics to Benchmark Human-Machine Teams (HMT): A Review , 2020, ArXiv.

[8]  Dimitri Voilmy,et al.  A new paradigm for autonomous human motion description and evaluation: Application to the Get Up & Go test use case , 2018, Pattern Recognit. Lett..

[9]  Thomas A. Henzinger,et al.  Probabilistic programming , 2014, FOSE.

[10]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[11]  Konrad Banachowicz,et al.  EARL—Embodied Agent-Based Robot Control Systems Modelling Language , 2020, Electronics.

[12]  Sofiane Boucenna,et al.  Evaluating the Engagement with Social Robots , 2015, International Journal of Social Robotics.

[13]  Lu Feng,et al.  Runtime Monitoring of Safety and Performance Requirements in Smart Cities , 2017, SafeThings@SenSys.

[14]  Seyyed Mohsen Hashemi,et al.  QoS Metrics for Cloud Computing Services Evaluation , 2014 .

[15]  Robin R. Murphy,et al.  Survey of metrics for human-robot interaction , 2013, 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[16]  Panayiotis Zaphiris,et al.  Research-derived web design guidelines for older people , 2005, Assets '05.

[17]  Juan F. Inglés-Romero,et al.  Managing Variability as a Means to Promote Composability: A Robotics Perspective , 2019 .

[18]  Carlo Ghezzi,et al.  Model evolution by run-time parameter adaptation , 2009, 2009 IEEE 31st International Conference on Software Engineering.

[19]  Fernando Fernández,et al.  Towards a robust robotic assistant for Comprehensive Geriatric Assessment procedures: updating the CLARC system* , 2018, 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[20]  Antonio Bandera,et al.  The CORTEX cognitive robotics architecture: Use cases , 2019, Cognitive Systems Research.

[21]  Luis J. Manso,et al.  Testing a Fully Autonomous Robotic Salesman in Real Scenarios , 2015, 2015 IEEE International Conference on Autonomous Robot Systems and Competitions.

[22]  José Carlos González,et al.  CLARC: A Cognitive Robot for Helping Geriatric Doctors in Real Scenarios , 2017, ROBOT.