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Gait recovery is a fundamental goal in patients with spinal cord injury to attain greater autonomy and quality of life. Robotics is becoming a valid tool in improving motor, balance, and gait function in this patient population. Moreover, other innovative approaches are leading to promising results. The aim of this study was to investigate new rehabilitative methods for gait recovery in people who have suffered spinal cord injuries. A systematic review of the last 10 years of the literature was performed in three databases (PubMed, PEDro, andCochrane). We followed this PICO of the review: P: adults with non-progressive spinal cord injury; I: new rehabilitative methods; C: new methods vs. conventional methods; and O: improvement of gait parameters. When feasible, a comparison through ES forest plots was performed. A total of 18 RCTs of the 599 results obtained were included. The studies investigated robotic rehabilitation ( n = 10), intermittent hypoxia (N = 3) and external stimulation (N = 5). Six studies of the first group (robotic rehabilitation) were compared using a forest plot for 10MWT, LEMS, WISCI-II, and SCIM-3. The other clinical trials were analyzed through a narrative review of the results. We found weak evidence for the claim that robotic devices lead to better outcomes in gait independence compared to conventional rehabilitation methods. External stimulation and intermittent hypoxia seem to improve gait parameters associated with other rehabilitation methods. Research investigating the role of innovative technologies in improving gait and balance is needed since walking ability is a fundamental issue in patients with SCI.

In recent years, applications like Apple's Siri or Microsoft's Cortana have created the illusion that one can actually "chat" with a machine. However, a perfectly natural human-machine interaction is far from real as none of these tools can empathize. This issue has raised an increasing interest in speech emotion recognition systems, as the possibility to detect the emotional state of the speaker. This possibility seems relevant to a broad number of domains, ranging from man-machine interfaces to those of diagnostics. With this in mind, in the present work, we explored the possibility of applying a precision approach to the development of a statistical learning algorithm aimed at classifying samples of speech produced by children with developmental disorders(DD) and typically developing(TD) children. Under the assumption that acoustic features of vocal production could not be efficiently used as a direct marker of DD, we propose to apply the Emotional Modulation function(EMF) concept, rather than running analyses on acoustic features per se to identify the different classes. The novel paradigm was applied to the French Child Pathological & Emotional Speech Database obtaining a final accuracy of 0.79, with maximum performance reached in recognizing language impairment (0.92) and autism disorder (0.82).