Roberto S. Inoue

Markov Jump Linear Systems-Based Position Estimation for Lower Limb Exoskeletons

In this paper, we deal with Markov Jump Linear Systems-based filtering applied to robotic rehabilitation. The angular positions of an impedance-controlled exoskeleton, designed to help stroke and spinal cord injured patients during walking rehabilitation, are estimated. Standard position estimate approaches adopt Kalman filters (KF) to improve the performance of inertial measurement units (IMUs) based on individual link configurations. Consequently, for a multi-body system, like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank) are not taken into account in other link position estimation (e.g., the foot). In this paper, we propose a collective modeling of all inertial sensors attached to the exoskeleton, combining them in a Markovian estimation model in order to get the best information from each sensor. In order to demonstrate the effectiveness of our approach, simulation results regarding a set of human footsteps, with four IMUs and three encoders attached to the lower limb exoskeleton, are presented. A comparative study between the Markovian estimation system and the standard one is performed considering a wide range of parametric uncertainties.

Sit–Stand Tables With Semi-Automated Position Changes: A New Interactive Approach for Reducing Sitting in Office Work

OCCUPATIONAL APPLICATIONS Sit–stand tables with semi-automated position changes were developed in order to remind users to switch regularly between sitting and standing postures during office work. Tests of the system showed good user compliance: Desk usage patterns were sustained during the entire 2 months following intervention. Users reported the new system did not interfere with their work, that it impacted their perception of health and well-being positively, and that they would have liked to continue using the system beyond the intervention period. This could thus be a promising intervention to ensure adequate use of sit–stand desks and sustain their use over time. TECHNICAL ABSTRACT Background: Introducing sit–stand tables has been proposed as an initiative to decrease sedentary behavior among office workers and thus reduce risks of negative cardiometabolic health effects. However, ensuring proper and sustainable use of such tables has remained a challenge for successful implementation. Purpose: Assess a new system developed to promote and sustain the use of sit–stand tables. Methods: The system was programmed to change the position of the table between “sit” and “stand” positions per a regular preset pattern if the user agreed to the system-generated prompts prior to each change. The user could respond to the system-generated prompts by agreeing, refusing, or postponing the changes by 2 minutes. We obtained user compliance data when this system was programmed to a schedule of 10 minutes of standing after every 50 minutes of sitting. Compliance was investigated among nine office workers who were offered the semi-automated sit–stand table for 2 months. Results: The system issued 12 to 14 alerts per day throughout the period. Mean acceptance rates ranged from 75.0% to 82.4%, and refusal rate ranged from 11.8% to 10.1% between the first and eighth weeks of intervention (difference not statistically significant). During the first week after introduction, the table was in a standing position for a mean of 75.2 minutes—increasing slightly to 77.5 minutes in the eighth week. Conclusions: Since the workers were essentially sitting down before the table was introduced, these results suggest that the system was well accepted, and led to an effective reduction of sitting during working hours. Users also reported that the system contributed positively to their health and well-being, without interrupting their regular work, and that they would like to continue using the sit–stand table even beyond the 2-month period as part of their regular work. Compliance beyond 2 months of use, however, needs to be verified.

Global Kalman filter approaches to estimate absolute angles of lower limb segments

BackgroundIn this paper we propose the use of global Kalman filters (KFs) to estimate absolute angles of lower limb segments. Standard approaches adopt KFs to improve the performance of inertial sensors based on individual link configurations. In consequence, for a multi-body system like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank) are not taken into account in other link angle estimations (e.g., foot). Global KF approaches, on the other hand, correlate the collective contribution of all signals from lower limb segments observed in the state-space model through the filtering process. We present a novel global KF (matricial global KF) relying only on inertial sensor data, and validate both this KF and a previously presented global KF (Markov Jump Linear Systems, MJLS-based KF), which fuses data from inertial sensors and encoders from an exoskeleton. We furthermore compare both methods to the commonly used local KF.ResultsThe results indicate that the global KFs performed significantly better than the local KF, with an average root mean square error (RMSE) of respectively 0.942° for the MJLS-based KF, 1.167° for the matrical global KF, and 1.202° for the local KFs. Including the data from the exoskeleton encoders also resulted in a significant increase in performance.ConclusionThe results indicate that the current practice of using KFs based on local models is suboptimal. Both the presented KF based on inertial sensor data, as well our previously presented global approach fusing inertial sensor data with data from exoskeleton encoders, were superior to local KFs. We therefore recommend to use global KFs for gait analysis and exoskeleton control.

Applications of Robust Descriptor Kalman Filter in Robotics

In this chapter we are interested in designing estimators for the internal variables of two kind of robots, wheeled mobile and robotic leg prosthesis, based on a recently developed robust descriptor Kalman filter. The proposed approach is reasonable since descriptor formulation can cope with algebraic restrictions on system’s signals. Further, the recursiveness of this class of filter is useful for on-line applications. Different procedures have been used to deal with mobile robots localization problem. Measurement systems based on odometric, inertial sensors and ultrasounds are self-contained, simple to use, and able to guarantee a high data rate. However, the problem of these systems is that they integrate the relative increments, and the localization errors considerably grow over time if an appropriate sensor fusion algorithm is not used, see for instance [17], [18] and references therein. The examples developed in these references do not take into account robust approaches, in the line we are proposing here. In the context of robotic leg prosthesis, we deal with the development of devices for above knee amputees. Robotic prosthesis are devices intended to replace parts of the human body. They should be able to sense the environment and complain with the movement of the body in such a way to aid the user to perform the most common tasks. This is a very interesting and current research topic [7]. Environment sensing is one of the most difficult tasks, mainly in the case of leg prosthesis because of the great diversity of walking conditions and terrains. The use of Electromyographic (EMG) signal processing for detecting the main properties of the walking terrain is the focus of [15]. However, in the case of above knee prosthesis, there is no EMG signal available to allow automatic reorientation of the robotic foot. When the foot of a robotic leg is not in contact with ground, its configuration should be estimated to allow its control with respect to ground. This can be useful for controlling its orientation, mainly in the end of phase where the foot is not in contact with ground. In this chapter, it is shown a solution for this problem using multisensor data fusion by a robust descriptor Kalman filter. This chapter is divided in three main parts. In the first part we present basic definitions and concepts of descriptor systems and some examples to clarify the use of this kind of approach. In the second part we present three algorithms for the computation of the

Wheeled Mobile Robot Formation Using Recursive Robust Regulator with Discrete-Time Markov Linear System

In this paper we use a recursive robust regulator for discrete time Markovian jump linear systems to control a group of wheeled mobile robots in formation. The formation has a direct communication topology and a leader. The robustness is checked with a communication fault in blind areas and, if the fault affects the leader, this robot leader is changed and the formation continues to follow the defined trajectory. When the communication is reestablished the robot returns to its position in the formation. We use the properties of e-Puck wheeled mobile robots to perform realistic simulations. The simulations show the effectiveness of the formation control approach used.

Information filtering and array algorithms for discrete-time Markovian jump linear systems subject to parametric uncertainties

This paper deals with computational issues of robust filtering for discrete-time Markovian jump linear systems. We develop information filter and array algorithms to estimate this kind of system. We present numerical examples to demonstrate the advantages of the approaches we are proposing.

Robust recursive control of a skid-steering mobile robot

This paper is concerned with the robust control of a skid-steering mobile robot (SSMR). A robust recursive controller for linear state-space systems subject to parametric uncertainties is considered to deal with the SSMR tracking problem. Simulation results of the application of this regulator using the dynamic model equations of a SSMR shows the effectiveness of this approach.

Robust Discrete-Time Markovian Control for Wheeled Mobile Robot Formation: A Fault Tolerant Approach

In this paper we use a recursive robust regulator for discrete time Markovian jump linear systems to control a group of wheeled mobile robots in formation. A leader-following formation is used with directed communication topology. The robustness is checked with a communication fault in blind areas and, if the fault affects the leader, the leadership is changed and the formation continues to follow the defined trajectory. When the communication is reestablished all robots that lost communication return to their position in the formation. Results based on simulation and real implementation are presented to show the effectiveness of the formation control approach used.

Cooperative UAV formation control simulated in X-plane

Unmanned Aerial Vehicles (UAVs) have been used in a wide range of applications, due to high availability and low cost of their core components. One subject of research in the UAV field is the formation flying of a group of aircraft. In this paper, it is described the problem formulation for a formation of UAVs and the control law used to keep the aircraft in position. Following the theoretic model definition, additional information about its implementation and testing are provided: the algorithms were first tested in MATLAB® and then implemented as a plugin for the X-Plane Flight Simulator. The results obtained by the simulation are then presented in this paper, showing the effectiveness of the formation representation and control law used to keep it in a predefined state.

Markov Jump Linear Systems-based position estimation for lower limbs exoskeletons

This paper deals with Markov Jump Linear Systems-based filtering applied to robotic rehabilitation. Angular positions of an impedance-controlled exoskeleton, designed to help stroke and spinal cord injured patients during walking rehabilitation, are estimated. Standard position estimate approaches have adopted Kalman Filters (KF) to improve measurement quality of inertial sensors based on individual link configurations. That is, for a multi-body system, like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank) are not taken into account in other link position estimation (e.g, the foot). In this paper it is proposed a collective modeling of all inertial sensors attached to the device, combining them in a Markovian estimation model, in order to get the best information from each sensor. To demonstrate the efficiency of our approach, a simulation was performed regarding a set of human footsteps, with four IMUs and three encoders attached to the lower limb exoskeleton. A comparative study between the Markovian estimation system and the standard one is performed considering a wide range of parametric uncertainties.