Intention-based EMG control for powered exoskeletons.

Electromyographical (EMG) signals have been frequently used to estimate human muscular torques. In the field of human-assistive robotics, these methods provide valuable information to provide effectively support to the user. However, their usability is strongly limited by the necessity of complex user-dependent and session-dependent calibration procedures, which confine their use to the laboratory environment. Nonetheless, an accurate estimate of muscle torque could be unnecessary to provide effective movement assistance to users. The natural ability of human central nervous system of adapting to external disturbances could compensate for a lower accuracy of the torque provided by the robot and maintain the movement accuracy unaltered, while the effort is reduced. In order to explore this possibility, in this paper we study the reaction of ten healthy subjects to the assistance provided through a proportional EMG control applied by an elbow powered exoskeleton. This system gives only a rough estimate of the user muscular torque but does not require any specific calibration. Experimental results clearly show that subjects adapt almost instantaneously to the assistance provided by the robot and can reduce their effort while keeping full control of the movement under different dynamic conditions (i.e., no alterations of movement accuracy are observed).

Development of gait segmentation methods for wearable foot pressure sensors.

We present an automated segmentation method based on the analysis of plantar pressure signals recorded from two synchronized wireless foot insoles. Given the strict limits on computational power and power consumption typical of wearable electronic components, our aim is to investigate the capability of a Hidden Markov Model machine-learning method, to detect gait phases with different levels of complexity in the processing of the wearable pressure sensors signals. Therefore three different datasets are developed: raw voltage values, calibrated sensor signals and a calibrated estimation of total ground reaction force and position of the plantar center of pressure. The method is tested on a pool of 5 healthy subjects, through a leave-one-out cross validation. The results show high classification performances achieved using estimated biomechanical variables, being on average the 96%. Calibrated signals and raw voltage values show higher delays and dispersions in phase transition detection, suggesting a lower reliability for online applications.

Proportional EMG control for upper-limb powered exoskeletons.

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.

Development of an in-shoe pressure-sensitive device for gait analysis.

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.

Soft artificial tactile sensors for the measurement of human-robot interaction in the rehabilitation of the lower limb.

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.