Advanced Robotic Therapy Integrated Centers (ARTIC): An international collaboration facilitating the application of rehabilitation technologies

Hubertus J.A. Van Hedel*, Giacomo Severini, Alessandra Scarton, Anne O'Brien, Tamsin Reed, Deborah Gaebler-Spira, Tara Egan, Andreas Meyer-Heim, Judith Graser, Karen Chua, Daniel Zutter, Raoul Schweinfurther, J. Carsten Möller, Liliana P. Paredes, Alberto Esquenazi, Steffen Berweck, Sebastian Schroeder, Birgit Warken, Anne Chan, Amber DeversJakub Petioky, Nam Jong Paik, Won Seok Kim, Paolo Bonato, Michael Boninger, Eric Fabara, Catherine Adans-Dester, Jean O'Brien Murby, Lori Laliberte, Gadi Revivo, Stella Lee, Theresa Toczylowski, Kay Fei Chan, Seng Kwee Wee, Pang Hung Lim, Wei Sheong Lim, Juliana Yun Ying Wang, Wing Kuen Lee, Chui Ni Ong, Cheng Hong Ong, Charlene Cheryl Pereira, Siew Yee Lee, Alexander Dewor, Michael Urban, Tabea Aurich, Anja Lucic, Thomas Nastulla, Katharina Badura, Josephine Steinbichler, Myungki Ji, Yunsung Oh, Salvatore Calabro, Leslie Van Hiel, Martina Spiess, Lars Lünenburger, Gery Colombo, Irin Maier

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


Background: The application of rehabilitation robots has grown during the last decade. While meta-analyses have shown beneficial effects of robotic interventions for some patient groups, the evidence is less in others. We established the Advanced Robotic Therapy Integrated Centers (ARTIC) network with the goal of advancing the science and clinical practice of rehabilitation robotics. The investigators hope to exploit variations in practice to learn about current clinical application and outcomes. The aim of this paper is to introduce the ARTIC network to the clinical and research community, present the initial data set and its characteristics and compare the outcome data collected so far with data from prior studies. Methods: ARTIC is a pragmatic observational study of clinical care. The database includes patients with various neurological and gait deficits who used the driven gait orthosis Lokomat® as part of their treatment. Patient characteristics, diagnosis-specific information, and indicators of impairment severity are collected. Core clinical assessments include the 10-Meter Walk Test and the Goal Attainment Scaling. Data from each Lokomat® training session are automatically collected. Results: At time of analysis, the database contained data collected from 595 patients (cerebral palsy: n = 208; stroke: n = 129; spinal cord injury: n = 93; traumatic brain injury: n = 39; and various other diagnoses: n = 126). At onset, average walking speeds were slow. The training intensity increased from the first to the final therapy session and most patients achieved their goals. Conclusions: The characteristics of the patients matched epidemiological data for the target populations. When patient characteristics differed from epidemiological data, this was mainly due to the selection criteria used to assess eligibility for Lokomat® training. While patients included in randomized controlled interventional trials have to fulfill many inclusion and exclusion criteria, the only selection criteria applying to patients in the ARTIC database are those required for use of the Lokomat®. We suggest that the ARTIC network offers an opportunity to investigate the clinical application and effectiveness of rehabilitation technologies for various diagnoses. Due to the standardization of assessments and the use of a common technology, this network could serve as a basis for researchers interested in specific interventional studies expanding beyond the Lokomat®.

Original languageEnglish (US)
Article number30
JournalJournal of neuroengineering and rehabilitation
Issue number1
StatePublished - Apr 6 2018

ASJC Scopus subject areas

  • Health Informatics
  • Rehabilitation


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