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Estimation of the temporal derivatives of a noisy position signal is a ubiquitous problem in industrial and robotics engineering. Here, we propose a new approach to get velocity and acceleration estimates of cyclical/periodic signals near to steady-state regime, by using adaptive oscillators. Our method combines the advantages of introducing no delay, and filtering out the high-frequency noise. We expect this method to be useful in control applications requiring undelayed but smooth estimates of velocity and acceleration (e.g., velocity control and inverse dynamics) of quasi-periodic tasks (e.g., active vibration compensation, robot locomotion, and lower-limb movement assistance).

The field of wearable robotics is gaining momentum thanks to its potential application in rehabilitation engineering, assistive robotics, and power augmentation. These devices are designed to be used in direct contact with the user to aid with movement or increase the power of specific skeletal joints. The design of the so-called physical human-robot interface is critical, since it determines not only the efficacy of the robot but the kinematic compatibility of the device with the human skeleton and the degree of adaptation to different anthropometries as well. Failing to deal with these problems causes misalignments between the robot and the user joint. Axes misalignment leads to the impossibility of controlling the torque effectively transmitted to the user joint and causes undesired loading forces on articulations and soft tissues. In this paper, we propose a general analytical method for the design of exoskeletons able to assist human joints without being subjected to misalignment effects. This method is based on a kinetostatic analysis of a coupled mechanism (robot-human skeleton) and can be applied in the design of self-aligning mechanisms. The method is exemplified in the design of an assistive robotic chain for a two-degree-of-freedom (DOF) human articulation.

This paper presents the design and experimental testing of the robotic elbow exoskeleton NEUROBOTICS Elbow Exoskeleton (NEUROExos). The design of NEUROExos focused on three solutions that enable its use for poststroke physical rehabilitation. First, double-shelled links allow an ergonomic physical human-robot interface and, consequently, a comfortable interaction. Second, a four-degree-of-freedom passive mechanism, embedded in the link, allows the user's elbow and robot axes to be constantly aligned during movement. The robot axis can passively rotate on the frontal and horizontal planes 30° and 40°, respectively, and translate on the horizontal plane 30 mm. Finally, a variable impedance antagonistic actuation system allows NEUROExos to be controlled with two alternative strategies: independent control of the joint position and stiffness, for robot-in-charge rehabilitation mode, and near-zero impedance torque control, for patient-in-charge rehabilitation mode. In robot-in-charge mode, the passive joint stiffness can be changed in the range of 24-56 N·m/rad. In patient-in-charge mode, NEUROExos output impedance ranges from 1 N·m/rad, for 0.3 Hz motion, to 10 N·m/rad, for 3.2 Hz motion.

In this work, we present the development of an in-shoe device to monitor plantar pressure distribution for gait analysis. The device consists in a matrix of 64 sensitive elements, integrated with in-shoe electronics and battery which provide an high-frequency data acquisition, wireless transmission and an average autonomy of 7 hours in continuous working mode. The device is presented along with its experimental characterization and a preliminary validation on a healthy subject.

Electromyography (EMG) has been frequently proposed as the driving signal for controlling powered exoskeletons. Lot of effort has been spent to design accurate algorithms for muscular torque estimation, while very few studies attempted to understand to what extent an accurate torque estimate is indeed necessary to provide effective movement assistance through powered exoskeletons. In this study, we focus on the latter aspect by using a simple and “low-accuracy” torque estimate, an EMG-proportional control, to provide assistance through an elbow exoskeleton. Preliminary results show that subjects adapt almost instantaneously to the assistance provided by the exoskeleton and can reduce their effort while keeping full control of the movement.

A new and alternative method to measure the interaction force between the user and a lower-limb gait rehabilitation exoskeleton is presented. Instead of using a load cell to measure the resulting interaction force, we propose a distributed measure of the normal interaction pressure over the whole contact area between the user and the machine. To obtain this measurement, a soft silicone tactile sensor is inserted between the limb and commonly used connection cuffs. The advantage of this approach is that it allows for a distributed measure of the interaction pressure, which could be useful for rehabilitation therapy assessment purposes, or for control. Moreover, the proposed solution does not change the comfort of the interaction; can be applied to connection cuffs of different shapes and sizes; and can be manufactured at a low cost. Preliminary results during gait assistance tasks show that this approach can precisely detect changes in the pressure distribution during a gait cycle.

A pair of muscles powering the human joint in an antagonistic configuration exemplifies the main difference between standard industrial robots and biological motor systems. Since muscles have a natural stiffness that varies with the muscle activation level, the central nervous system can generate stable equilibrium postures, towards which the arm is attracted, by properly regulating the activation levels of antagonistic muscles [1] The elastic properties of muscles contribute to the finite stiffness/compliance properties of the limb, to the stability of the neuro-musculo-skeletal system in the face of significant feedback delays and even allow for the generation of target movements in absence of sensory feedback, by shifting the equilibrium point [2]. Control theories based on the presence of the EP in biological motor systems [3] suggest that movements are programmed as a shift of equilibrium positions rather than through an explicit computation of forces. Thus, there is no need to solve the “inverse dynamics problem” for calculating the torque required to move the arm on the desired trajectory. The implementation of a given neuroscientific hypothesis on a real mechanical system could provide a tool under the full control of the experimenter, reproducing the main functional features of the human arm and being able to interact with the same physical environment of the human. To this end we developed the NEURARM platform, a bio-mimetic planar robotic arm reproducing key features of the human arm as identified at the level of joints and muscles. In particular