Fuzzy-description logic for supporting the rehabilitation of the elderly

[EN] According to the latest statistics, the proportion of the elderly (+65) is increasing and is expected to double within the European Union in a period of 50 years. This ageing is due to the improvement of quality of life and advances in medicine in the last decades. Gerontechnology is receiving a great deal of attention as a way of providing the elderly with sustainable products, environments, and services combining gerontology and technology. One of the most important aspects to consider by gerontechnology is the mobility/rehabilitation technologies, because there is an important relationship between mobility and the elderly's quality of life. Telerehabilitation systems have emerged to allow the elderly to perform their rehabilitation exercises remotely. However, in many cases, the proposed systems assist neither the patients nor the experts about the progress of the rehabilitation. To address this problem, we propose in this paper, a fuzzy-semantic system for evaluating patient's physical state during the rehabilitation process based on well-known standard for patients' evaluation. Moreover, a tool called FINE has been developed that facilitates the evaluation be accomplished in a semi-automatic way first asking patients to carry out a set of standard tests and then inferencing their state by means of a fuzzy-semantic approach using the data captured during the rehabilitation tasks.This research was funded by the Spanish Ministry of Economy and Competitiveness and by EU FEDER funds under project grants TIN2016-79100-R and TIN2015-72931-EXP. It has also been funded by the Junta de Comunidades de Castilla¿La Mancha scholarship 2018-UCLM1-9131Moya, A.; Navarro, E.; Jaén Martínez, FJ.; González, P. (2020). Fuzzy-description logic for supporting the rehabilitation of the elderly. Expert Systems. 37(2):1-16. https://doi.org/10.1111/exsy.12464116372Alamri, A., Cha, J., & El Saddik, A. (2010). AR-REHAB: An Augmented Reality Framework for Poststroke-Patient Rehabilitation. IEEE Transactions on Instrumentation and Measurement, 59(10), 2554-2563. doi:10.1109/tim.2010.2057750Antoniou, G., & van Harmelen, F. (2004). Web Ontology Language: OWL. Handbook on Ontologies, 67-92. doi:10.1007/978-3-540-24750-0_4Bobillo F.(2008).Managing vagueness in ontologies. Universidad de Granada.Bobillo F. (2015).The fuzzyDL system. Retrieved July 10 2018 fromhttp://www.umbertostraccia.it/cs/software/fuzzyDL/fuzzyDL.htmlBobillo, F., Delgado, M., & Gómez-Romero, J. (2012). DeLorean: A reasoner for fuzzy OWL 2. Expert Systems with Applications, 39(1), 258-272. doi:10.1016/j.eswa.2011.07.016Bobillo, F., & Straccia, U. (2016). The fuzzy ontology reasoner fuzzyDL. Knowledge-Based Systems, 95, 12-34. doi:10.1016/j.knosys.2015.11.017Boucenna, S., Narzisi, A., Tilmont, E., Muratori, F., Pioggia, G., Cohen, D., & Chetouani, M. (2014). Interactive Technologies for Autistic Children: A Review. Cognitive Computation, 6(4), 722-740. doi:10.1007/s12559-014-9276-xCarter J. E. L.(2002).The Heath‐Carter anthropometric somatotype—Instruction manual. San Diego:State University.Chiu, Y.-H., Chen, T.-W., Chen, Y. J., Su, C.-I., Hwang, K.-S., & Ho, W.-H. (2018). Fuzzy logic-based mobile computing system for hand rehabilitation after neurological injury. Technology and Health Care, 26(1), 17-27. doi:10.3233/thc-171403Fernández-Caballero, A., González, P., & Navarro, E. (2017). Gerontechnologies - Current achievements and future trends. Expert Systems, 34(2), e12203. doi:10.1111/exsy.12203Giles, R. (1976). Łukasiewicz logic and fuzzy set theory. International Journal of Man-Machine Studies, 8(3), 313-327. doi:10.1016/s0020-7373(76)80003-xHsieh, Y.-W., Hsueh, I.-P., Chou, Y.-T., Sheu, C.-F., Hsieh, C.-L., & Kwakkel, G. (2007). Development and Validation of a Short Form of the Fugl-Meyer Motor Scale in Patients With Stroke. Stroke, 38(11), 3052-3054. doi:10.1161/strokeaha.107.490730Karime, A., Eid, M., Alja’am, J. M., Saddik, A. E., & Gueaieb, W. (2014). A Fuzzy-Based Adaptive Rehabilitation Framework for Home-Based Wrist Training. IEEE Transactions on Instrumentation and Measurement, 63(1), 135-144. doi:10.1109/tim.2013.2277536Krynicki, K., Jaen, J., & Navarro, E. (2016). An ACO-based personalized learning technique in support of people with acquired brain injury. Applied Soft Computing, 47, 316-331. doi:10.1016/j.asoc.2016.04.039Leap Motion INC. (2018).Leap Motion. Retrieved July 10 2018 fromhttps://www.leapmotion.com/Lukasiewicz, T., & Straccia, U. (2008). Managing uncertainty and vagueness in description logics for the Semantic Web. Journal of Web Semantics, 6(4), 291-308. doi:10.1016/j.websem.2008.04.001Metz, D. . (2000). Mobility of older people and their quality of life. Transport Policy, 7(2), 149-152. doi:10.1016/s0967-070x(00)00004-4Nassabi M. H. Den Akker H. &Vollenbroek‐Hutten M. (2014).An ontology‐based recommender system to promote physical activity for pre‐frail elderly 181–184.Navarro, E., González, P., López-Jaquero, V., Montero, F., Molina, J. P., & Romero-Ayuso, D. (2018). Adaptive, Multisensorial, Physiological and Social: The Next Generation of Telerehabilitation Systems. Frontiers in Neuroinformatics, 12. doi:10.3389/fninf.2018.00043OpenNI Pioneering Members. (2018).OpenNI. Retrieved July 10 2018 fromhttp://openni.ru/about/index.htmlOrbbec 3D. (2018).Orbbec Astra Pro. Retrieved July 10 2018 fromhttps://orbbec3d.com/product‐astra‐pro/Rodríguez, A. C., Roda, C., Montero, F., González, P., & Navarro, E. (2015). An Interactive Fuzzy Inference System for Teletherapy of Older People. Cognitive Computation, 8(2), 318-335. doi:10.1007/s12559-015-9356-6Shaughnessy, M., Resnick, B. M., & Macko, R. F. (2006). Testing a Model of Post-Stroke Exercise Behavior. Rehabilitation Nursing, 31(1), 15-21. doi:10.1002/j.2048-7940.2006.tb00005.xSu, C.-J., Chiang, C.-Y., & Huang, J.-Y. (2014). Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic. Applied Soft Computing, 22, 652-666. doi:10.1016/j.asoc.2014.04.020Velozo, C. A., & Woodbury, M. L. (2011). Translating measurement findings into rehabilitation practice: An example using Fugl-Meyer Assessment-Upper Extremity with patients following stroke. The Journal of Rehabilitation Research and Development, 48(10), 1211. doi:10.1682/jrrd.2010.10.0203W3C. (2012).OWL 2 web ontology language. Retrieved July 10 2018 from https://www.w3.org/TR/owl2‐overview/Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-xZhang, Z., Fang, Q., & Gu, X. (2014). Fuzzy inference system based automatic Brunnstrom stage classification for upper-extremity rehabilitation. Expert Systems with Applications, 41(4), 1973-1980. doi:10.1016/j.eswa.2013.08.09

An objective evaluation method for rehabilitation exergames

The aim of this work is to objectively evaluate the performance of patients using a virtual rehabilitation system called MIRA. MIRA is a software platform which converts conventional therapeutic exercises into games, enabling the user to practice the given exercise by playing a game. The system includes a motion sensor to track and capture user's movements. Our assessment of the performance quality is based on the recorded trajectories of the human skeleton joints. We employ two different machine learning approaches, dynamic time warping (DTW) and hidden Markov modeling (HMM), both widely used for gesture recognition, to compare the user's performance with that of a reference as ground truth

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection and human pose estimation

A review of computer vision-based approaches for physical rehabilitation and assessment

The computer vision community has extensively researched the area of human motion analysis, which primarily focuses on pose estimation, activity recognition, pose or gesture recognition and so on. However for many applications, like monitoring of functional rehabilitation of patients with musculo skeletal or physical impairments, the requirement is to comparatively evaluate human motion. In this survey, we capture important literature on vision-based monitoring and physical rehabilitation that focuses on comparative evaluation of human motion during the past two decades and discuss the state of current research in this area. Unlike other reviews in this area, which are written from a clinical objective, this article presents research in this area from a computer vision application perspective. We propose our own taxonomy of computer vision-based rehabilitation and assessment research which are further divided into sub-categories to capture novelties of each research. The review discusses the challenges of this domain due to the wide ranging human motion abnormalities and difficulty in automatically assessing those abnormalities. Finally, suggestions on the future direction of research are offered

Rehabilitation Exergames: use of motion sensing and machine learning to quantify exercise performance in healthy volunteers

Background: Performing physiotherapy exercises in front of a physiotherapist yields qualitative assessment notes and immediate feedback. However, practicing the exercises at home lacks feedback on how well or not patients are performing the prescribed tasks. The absence of proper feedback might result in patients doing the exercises incorrectly, which could worsen their condition. Objective: We propose the use of two machine learning algorithms, namely Dynamic Time Warping (DTW) and Hidden Markov Model (HMM), to quantitively assess the patient’s performance with respects to a reference. Methods: Movement data were recorded using a Kinect depth sensor, capable of detecting 25 joints in the human skeleton model, and were compared to those of a reference. 16 participants were recruited to perform four different exercises: shoulder abduction, hip abduction, lunge, and sit-to-stand. Their performance was compared to that of a physiotherapist as a reference. Results: Both algorithms show a similar trend in assessing participants' performance. However, their sensitivity level was different. While DTW was more sensitive to small changes, HMM captured a general view of the performance, being less sensitive to the details. Conclusions: The chosen algorithms demonstrated their capacity to objectively assess physical therapy performances. HMM may be more suitable in the early stages of a physiotherapy program to capture and report general performance, whilst DTW could be used later on to focus on the detail

An instrumental approach for monitoring physical exercises in a visual markerless scenario: A proof of concept

none8This work proposes a real-time monitoring tool aimed to support clinicians for remote assessing exercise performances during home-based rehabilitation. The study relies on clinician indications to define kinematic features, that describe five motor tasks (i.e., the lateral tilt of the trunk, lifting of the arms, trunk rotation, pelvis rotation, squatting) usually adopted in the rehabilitation program for axial disorders. These features are extracted by the Kinect v2 skeleton tracking system and elaborated to return disaggregated scores, representing a measure of subjects performance. A bell-shaped function is used to rank the patient performances and to provide the scores. The proposed rehabilitation tool has been tested on 28 healthy subjects and on 29 patients suffering from different neurological and orthopedic diseases. The reliability of the study has been performed through a cross-sectional controlled design methodology, comparing algorithm scores with respect to blinded judgment provided by clinicians through filling a specific questionnaire. The use of task-specific features and the comparison between the clinical evaluation and the score provided by the instrumental approach constitute the novelty of the study. The proposed methodology is reliable for measuring subject's performance and able to discriminate between the pathological and healthy condition.Capecci, Marianna; Ceravolo, Maria Gabriella; Ferracuti, Francesco; Grugnetti, Martina; Iarlori, Sabrina; Longhi, Sauro; Romeo, Luca; Verdini, FedericaCapecci, Marianna; Ceravolo, Maria Gabriella; Ferracuti, Francesco; Grugnetti, Martina; Iarlori, Sabrina; Longhi, Sauro; Romeo, Luca; Verdini, Federic