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STUDY DESIGN: Prospective, open label, observational. OBJECTIVES: To present results of the first clinical study on a newly developed robotic exoskeleton (Atalante®, Wandercraft, Paris, France) that enables individuals with spinal cord injury (SCI) to perform ambulatory functions without technical aids. SETTING: Two sites specialized in SCI rehabilitation, France. METHODS: Inclusion criteria were presence of chronic complete SCI (AIS A) ranging from T5 to T12. The study protocol included 12 one-hour training sessions during 3 weeks. Patients walked on floor with robotic assistance and wore a harness connected to a mobile suspension system (without weight-bearing) to prevent from falling. Main outcome was the ability to walk 10 meters unassisted, secondary outcomes were assessment of other ambulatory functions, bladder and bowel functions, pain and spasticity. RESULTS: Twelve patients were enrolled, and 11 completed the protocol, mean age 33,9 years. Six patients had T6 levels of lesion or above. Seven patients passed the 10mWT at the 12th session unassisted (mean walking speed 0.13 m/s) while four required some human help. All patients succeeded at the other ambulatory tests (stand-up, sit-down, balance, turn). There were no significant change for bladder (Qualiveen) or bowel (NBD) functions, neuropathic pain (NPSI, NPRS), yet five patients reported a subjective improvement of their bowel function. Impact on spasticity was variable depending on the muscle examined (Ashworth). Ischial skin erosion was seen in one patient that needed local dressing. CONCLUSION: The Atalante system is safe and enables to perform ambulatory functions in patients with complete SCI.

International audience; Automation of smart home for ambient assisted living is cur- rently based on a widespread use of sensors. As e cient as it seems to be, this solution can sometimes be problematic when one focus on user acceptability intimately related to cost and intrusivity. In this paper, we propose a context-aware system based on the semantic analysis of each user request at runtime. Our goal is to infer user data usually sensored by using advanced semantic web tools to adapt home automation services to people with special needs. To take up this challenge, an ontology, automatically derived from a model-driven process, rstly de nes user-system interactions. Then, the use of rules allows an inference engine to deduce user location and intention leading to adapted service delivery.