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When compared to other cueing, it seems that audio could be a better neurofeedback for reducing the risk of falling over different walking surfaces, which represent important risk factors for persons with gait disorder or lost functional autonomy.

The increasing use of parallel cable-driven mechanisms calls for a better understanding of their behavior and highly efficient algorithms to attenuate their drawbacks at the design stage. One of these drawbacks is the high probability of mechanical interferences between the moving parts of the mechanism. In this paper, the phenomenon is described under the assumption that a cable is a line segment in space. When a mechanical contact occurs between two cables or between a cable and an edge of the end effector, these entities necessarily lie in the same plane, and then the three-dimensional problem becomes two-dimensional. This fact is used to simplify the equations, and leads to exhaustive descriptions of the associated interference loci in the constant-orientation workspace of a cable-driven mechanism. These results provide a fast method to graphically represent all interference regions in the manipulator workspace, given its geometry and the orientation of its end effector.

This paper presents a time-domain vibration observer and controller for physical Human-Robot Interaction (pHRI). The proposed observer/controller aims at reducing or eliminating vibrations that may occur in stiff interactions. The vibration observer algorithm first detects minima and maxima of a given signal with robustness in regards to noise. Based on these extrema, a vibration index is computed and then used by an adaptive controller to adjust the control gains in order to reduce vibrations. The controller is activated only when the amplitude of the vibrations exceeds a given threshold and thus it does not influence the performance in normal operation. Also, the observer does not require a model and can analyze a wide time frame with only a few computations. Finally, the algorithm is implemented on two different prototypes that use an admittance controller.

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The aim of this study is to improve and facilitate the methods used to assess risk of falling at home among older people through the computation of a risk of falling in real time in daily activities. In order to increase a real time computation of the risk of falling, a closed-loop balance model is proposed and compared with One-Leg Standing Test (OLST). This balance model allows studying the postural response of a person having an unpredictable perturbation. Twenty-nine volunteers participated in this study for evaluating the effectiveness of the proposed system which includes seventeen elder participants: ten healthy elderly ( 68.4 ±5.5 years), seven Parkinson's disease (PD) subjects ( 66.28 ±8.9 years), and twelve healthy young adults ( 28.27 ±3.74 years). Our work suggests that there is a relationship between OLST score and the risk of falling based on center of pressure measurement with four low cost force sensors located inside an instrumented insole, which could be predicted using our suggested closed-loop balance model. For long term monitoring at home, this system could be included in a medical electronic record and could be useful as a diagnostic aid tool.

Abstract-It is known that the physical conditions of an environment might represent an important risk of falling. In this paper, we report an ongoing project toward the creation of intelligent clothes aiming at preventing falls related to such conditions. The package described here is centered on an intelligent shoe. The developed prototype counts two main parts: hardware and software. The material is composed of a set of sensors and actuators, distributed in strategic positions of the shoe, while the software is a soft real-time system running on a Smartphone. Our prototype has been served for the differentiation of physical properties of soils (concrete, broken stone, sand and dust stone).