SafeNet: A methodology for integrating general-purpose unsafe devices in safe-robot rehabilitation systems

Robot-assisted neurorehabilitation often involves networked systems of sensors ("sensory rooms") and powerful devices in physical interaction with weak users. Safety is unquestionably a primary concern. Some lightweight robot platforms and devices designed on purpose include safety properties using redundant sensors or intrinsic safety design (e.g. compliance and backdrivability, limited exchange of energy). Nonetheless, the entire "sensory room" shall be required to be fail-safe and safely monitored as a system at large. Yet, sensor capabilities and control algorithms used in functional therapies require, in general, frequent updates or re-configurations, making a safety-grade release of such devices hardly sustainable in cost-effectiveness and development time. As such, promising integrated platforms for human-in-the-loop therapies could not find clinical application and manufacturing support because of lacking in the maintenance of global fail-safe properties.Under the general context of cross-machinery safety standards, the paper presents a methodology called SafeNet for helping in extending the safety rate of Human Robot Interaction (HRI) systems using unsafe components, including sensors and controllers. SafeNet considers, in fact, the robotic system as a device at large and applies the principles of functional safety (as in ISO 13489-1) through a set of architectural procedures and implementation rules. The enabled capability of monitoring a network of unsafe devices through redundant computational nodes, allows the usage of any custom sensors and algorithms, usually planned and assembled at therapy planning-time rather than at platform design-time. A case study is presented with an actual implementation of the proposed methodology. A specific architectural solution is applied to an example of robot-assisted upper-limb rehabilitation with online motion tracking. © 2014 Elsevier Ireland Ltd.