Carlos A. Cifuentes

Multimodal Human–Robot Interaction for Walker-Assisted Gait

Human mobility is affected by different types of pathologies and also decreases gradually with age. In this context, Smart Walkers may offer important benefits for human assisted-gait in rehabilitation and functional compensation scenarios. This paper proposes a new interaction strategy for human-walker cooperation. The presented strategy is based on the acquisition of human gait parameters by means of data fusion from inertial measurement units and a laser range finder. This paper includes the mathematical formulation of the controller, simulations, and practical experimentation of the interaction strategy, in order to show the performance of the control system, including the parameter detection methodology. In the experimental study, despite the continuous oscillation during the walking, the parameter estimation was suitable for assisted ambulation, showing an appropriate adaptive behavior with changes in human linear velocity. Finally, the controller keeps the walker continuously following in front of the human gait, and it is shown how the walker orientation follows the human orientation during the real experiments.

Human-robot interaction based on wearable IMU sensor and laser range finder

Service robots are not only expected to navigate within the environment, as they also will may with people. Human tracking by mobile robots is essential for service robots and human interaction applications. In this work, the goal is to add a more natural robot-human following in front based on the normal human gait model. This approach proposes implementing and evaluating a human-robot interaction strategy, using the integration of a LRF (Laser Range Finder) tracking of human legs with wearable IMU (Inertial Measurement Unit) sensors for capturing the human movement during the gait. The work was carried out in four stages: first, the definition of the model of human-robot interaction and the control proposal were developed. Second, the parameters based on the human gait were estimated. Third, the robot and sensor integration setup are also proposed. Finally, the description of the algorithm for parameters detection is presented. In the experimental study, despite of the continuous oscillation during the walking, the parameters estimation was precise and unbiased, showing also repeatability with human linear velocities changes. The controller was evaluated with an eight-shaped curve, showing the stability of the controller even with sharp changes in the human path during real experiments.

Development of a Zigbee platform for bioinstrumentation

This paper presents the development of a network platform which allows connecting multiple individual wireless devices for transmitting bioelectrics and biomechanics signals for application in a hospital network, or continuous monitoring in a patients diary life. The Zigbee platform development proposal was made in three stages: 1) Hardware development, including the construction of a prototype network node and the integration of sensors, (2) Evaluation, in order to define the specifications of each node and scope of communication and (3) The Zigbee Network Implementation for bioinstrumentation based on ZigBee Health Care public application profile (ZHC). Finally, this work presents the experimental results based on measurements of Lost Packets and LQI (Link Quality Indicator), and the Zigbee Platform configuration for Bioinstrumentation in operation.

Robot-Assisted Rehabilitation Therapy: Recovery Mechanisms and Their Implications for Machine Design

Robot-assisted rehabilitation therapy interventions are emerging as a new technique to help individuals with motor impairment recover lost motor control. While initial clinical studies indicate the devices can reduce impairment, the mechanisms of recovery behind their effectiveness are not well understood. Thus, there is still uncertainty on how best to design robotic therapy devices. Ideally at the onset of designing a robotic therapy device, the designer would fully understand the physiological mechanisms of recovery, then shape the machine design to target those mechanisms. This chapter reviews key potential mechanisms by which robotic therapy devices may promote motor recovery. We discuss the evidence for each mechanism, how initial devices have targeted these mechanisms, and the implications of this evidence for optimal design of robotic therapy machines.

Human-walker interaction on slopes based on LRF and IMU sensors

Smart Walkers should be able to safely deal with inclinations in order to become a device effectively useful in the daily life of the elderly population. This paper presents a novel model of human-walker interaction on slopes. The interaction parameters are obtained from a Laser Range Finder (LRF) and an Inertial Measurement Unit (IMU). This model is integrated into the conventional closed control loop as a supervisor block. This block modifies, based on inclinations, the control set points to provide an adaptable human-walker desired position to improve comfort and safety and enhance users confidence in the walker. The practical evaluation shows that the parameters extracted from the natural behavior of the user and the estimated set points determined with the model proposal are highly correlated, presenting a similar trend. This correlation allows performing a more natural control.

Evaluation of IMU ZigBee Sensors for Upper Limb Rehabilitation

This research presents a novel wireless sensor technology designed to improve post-stroke rehabilitation robotics. This new system consists in a ZigBee network, based on wearable Inertial Measurement Units (IMU) easily adaptable to patient clothing or orthotic frames, allowing kinematic measurements in continuous therapy motion all over the body segments as a Body Sensor Network (BSN). The developed system was applied to upper limb movement during repetitions of a standard trial of reaching and grasping, due to its importance in post-stroke victims daily life. The presented tool was evaluated through simultaneous capture of the movement by an optical tracking system. This allowed to contrast the elbow angle variation obtained from IMU measurement with 3D photogrammetry data, recognized as the golden standard method.

Sensor fusion to control a robotic walker based on upper-limbs reaction forces and gait kinematics

This work proposes the implementation and validation of a new sensor fusion strategy based on force sensors and LRF (Laser Range Finder) to control a robotic walker. This approach combines user information about forearm reaction forces and gait kinematics from the legs scanning localization, to develop a more natural, safer and adaptable human-walker interaction. The work was carried out in four phases. First, a robotic walker platform was developed and the sensor subsystems were integrated. Second, a sensor fusion strategy to obtain the control parameters are defined. Third, the control strategy is presented. Finally, an experimental study to evaluate the sensor architecture and control was developed. One of the advantages of the human-walker interaction here proposed is the computational efficiency. The sensor processing algorithms and the control strategy are executed in real-time, showing stable performance with human speed changes.

Smart Walkers: Advanced Robotic Human Walking-Aid Systems

In this book chapter, the authors present the Smart Walkers as robotic functional compensation devices for assisting mobility dysfunctions and empowering the human gait. First, general concepts of locomotion, mobility dysfunctions and assistive devices are presented. A special attention is given to the walkers, considering not only the large number of users, but mainly the rehabilitation and functional compensation potential of empowering the natural mobility. Following, robotic versions of wheeled-walkers for assisting locomotion dysfunctions are presented. In this context, the UFES Smart Walker is presented as an example of a robotic device focused on the user-machine multimodal interaction for obtaining a natural control strategy for the robotic device. Two developments are discussed: (i) an adaptive filtering strategy of the upper-body interaction forces is used in a Fuzzy-Logic based control system to generate navigation commands, and (ii) a robust inverse kinematics controller based on users-motion is presented as a new solution for controlling the Smart Walker motion. Finally, conclusions and future works in the field of walker-assisted gait is presented in the last section.

Development of a Cognitive HRI Strategy for Mobile Robot Control

The concept of a physical and cognitive HRI for walker-assisted gait was presented in the previous chapter. The HRI is implemented by means of a multimodal interface, which is used to develop a natural human-robot interaction in the context of human mobility assistance. That way, both cHRI and pHRI were included in this interface. Specifically, this chapter describes the cHRI component, which combines two sensor modalities: active ranging sensing (LRF) and human motion capturing (IMU) to perform the human tracking. This sensor combination presents important advantages to monitor the human gait from a mobile robot point of view, such as mentioned in the previous last chapter.

T-FLEX: Variable Stiffness Ankle-Foot Orthosis for Gait Assistance

The development and a preliminary evaluation of a new active ankle-foot orthosis for gait assistance called T-FLEX are presented in this paper. The purpose of this device is to support patients with locomotion disabilities during rehabilitation treatment of the ankle joint. This device is based on bio-inspired actuation, in which the stiffness can be adjusted according to a gait phase detection, thereby reproducing the behavior of antagonistic muscles. A preliminary trial with a healthy subject (kinematic analysis) reveals an increase in the range of motion in ankle kinematics, which is desirable for ankle rehabilitation and assistance.