Publications by authors named "Arash Ajoudani"

13 Publications

  • Page 1 of 1

The Sensor-Based Biomechanical Risk Assessment at the Base of the Need for Revising of Standards for Human Ergonomics.

Sensors (Basel) 2020 Oct 10;20(20). Epub 2020 Oct 10.

Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, Monte Porzio Catone, 00040 Rome, Italy.

Due to the epochal changes introduced by "Industry 4.0", it is getting harder to apply the varying approaches for biomechanical risk assessment of manual handling tasks used to prevent work-related musculoskeletal disorders (WMDs) considered within the International Standards for ergonomics. In fact, the innovative human-robot collaboration (HRC) systems are widening the number of work motor tasks that cannot be assessed. On the other hand, new sensor-based tools for biomechanical risk assessment could be used for both quantitative "direct instrumental evaluations" and "rating of standard methods", allowing certain improvements over traditional methods. In this light, this Letter aims at detecting the need for revising the standards for human ergonomics and biomechanical risk assessment by analyzing the WMDs prevalence and incidence; additionally, the strengths and weaknesses of traditional methods listed within the International Standards for manual handling activities and the next challenges needed for their revision are considered. As a representative example, the discussion is referred to the lifting of heavy loads where the revision should include the use of sensor-based tools for biomechanical risk assessment during lifting performed with the use of exoskeletons, by more than one person (team lifting) and when the traditional methods cannot be applied. The wearability of sensing and feedback sensors in addition to human augmentation technologies allows for increasing workers' awareness about possible risks and enhance the effectiveness and safety during the execution of in many manual handling activities.
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http://dx.doi.org/10.3390/s20205750DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599507PMC
October 2020

An Intuitive Formulation of the Human Arm Active Endpoint Stiffness.

Sensors (Basel) 2020 Sep 18;20(18). Epub 2020 Sep 18.

Human Robot Interfaces and physical Interaction lab (HRI2), Istituto Italiano di Tecnologia (IIT), 16163 Genova, Italy.

In this work, we propose an intuitive and real-time model of the human arm active endpoint stiffness. In our model, the symmetric and positive-definite stiffness matrix is constructed through the eigendecomposition Kc=VDVT, where V is an orthonormal matrix whose columns are the normalized eigenvectors of Kc, and D is a diagonal matrix whose entries are the eigenvalues of Kc. In this formulation, we propose to construct V and D directly by exploiting the geometric information from a reduced human arm skeleton structure in 3D and from the assumption that human arm muscles work synergistically when co-contracted. Through the perturbation experiments across multiple subjects under different arm configurations and muscle activation states, we identified the model parameters and examined the modeling accuracy. In comparison to our previous models for predicting human active arm endpoint stiffness, the new model offers significant advantages such as fast identification and personalization due to its principled simplicity. The proposed model is suitable for applications such as teleoperation, human-robot interaction and collaboration, and human ergonomic assessments, where a personalizable and real-time human kinodynamic model is a crucial requirement.
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http://dx.doi.org/10.3390/s20185357DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570772PMC
September 2020

An Online Method to Detect and Locate an External Load on the Human Body with Applications in Ergonomics Assessment.

Sensors (Basel) 2020 Aug 10;20(16). Epub 2020 Aug 10.

1HRI2 Laboratory, Istituto Italiano di Tecnologia, 16163 Genoa, Italy.

In this work, we propose an online method to detect and approximately locate an external load induced on the body of a person interacting with the environment. The method is based on a torque equilibrium condition on the human sagittal plane, which takes into account a reduced-complexity model of the whole-body centre of pressure (CoP) along with the measured one, and the vertical component of the ground reaction forces (vGRFs). The latter is combined with a statistical analysis approach to improve the localisation accuracy, (which is subject to uncertainties) to the extent of the industrial applications we target. The proposed technique eliminates the assumption of known contact position of an external load on the human limbs, allowing a more flexible online body-state tracking. The accuracy of the proposed method is first evaluated via a simulation study in which various contact points on different body postures are considered. Next, experiments on human subjects with three different contact locations applied to the human body are presented, revealing the validity of the proposed methodology. Lastly, its benefit in the estimation of human dynamic states is demonstrated. These results add another layer to the online human ergonomics assessment framework developed in our laboratory, extending it to more realistic and varying interaction conditions.
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http://dx.doi.org/10.3390/s20164471DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7474424PMC
August 2020

A Novel Soft Robotic Supernumerary Hand for Severely Affected Stroke Patients.

IEEE Trans Neural Syst Rehabil Eng 2020 05 31;28(5):1168-1177. Epub 2020 Mar 31.

Upper limb functions are severely affected in 23% of the chronic stroke patients, compromising their life quality. To re-enable hand use, providing a degree of functionality and motivating against learned non-use, we propose a robotic supernumerary limb, the SoftHand X (SHX), consisting of a robotic hand, a gravity support system, and different sensors to detect the patient's intent for controlling the robotic hand. In this paper, this novel compensational approach is introduced and experimentally evaluated in stroke patients, assessing its efficacy, usability and safety. Ten patients were asked to perform tasks of a modified Action Research Arm Test with the SHX, by using three input methods. The mARAT scores rated the potentiality of the system. Usability was evaluated with the System Usability Scale, while spasticity before and after use was measured by the modified Ashworth Scale (mAS). Nine patients, not able to perform any tasks without external support, completed the whole experimental procedure using the proposed system with a median score greater than 12/30. Among the three input methods tested, the usability of one was rated as "good" while the other two were rated as "ok". Seven patients exhibited a reduction of the mAS. All nine patients stated that they would use the system frequently. Results obtained suggest that the SHX has the potential to partially compensate severely impaired hand function in stroke patients.
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http://dx.doi.org/10.1109/TNSRE.2020.2984717DOI Listing
May 2020

Design and Validation of a Modular One-To-Many Actuator for a Soft Wearable Exosuit.

Front Neurorobot 2019 18;13:39. Epub 2019 Jun 18.

Institut für Technische Informatik (ZITI), Heidelberg University, Heidelberg, Germany.

The size, weight, and power consumption of soft wearable robots rapidly scale with their number of active degrees of freedom. While various underactuation strategies have been proposed, most of them impose hard constrains on the kinetics and kinematics of the device. Here we propose a paradigm to independently control multiple degrees of freedom using a set of modular components, all tapping power from a single motor. Each module consists of three electromagnetic clutches, controlled to convert a constant unidirectional motion in an arbitrary output trajectory. We detail the design and functioning principle of each module and propose an approach to control the velocity and position of its output. The device is characterized in free space and under loading conditions. Finally, we test the performance of the proposed actuation scheme to drive a soft exosuit for the elbow joint, comparing it with the performance obtained using a traditional DC motor and an unpowered-exosuit condition. The exosuit powered by our novel scheme reduces the biological torque required to move by an average of 46.2%, compared to the unpowered condition, but negatively affects movement smoothness. When compared to a DC motor, using the our paradigm slightly deteriorates performance. Despite the technical limitations of the current design, the method proposed in this paper is a promising way to design more portable wearable robots.
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http://dx.doi.org/10.3389/fnbot.2019.00039DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6591529PMC
June 2019

A Bio-inspired Grasp Stiffness Control for Robotic Hands.

Front Robot AI 2018 26;5:89. Epub 2018 Jul 26.

Human-Robot Interfaces and Physical Interaction Department, Istituto Italiano di Tecnologia, Genova, Italy.

This work presents a bio-inspired grasp stiffness control for robotic hands based on the concepts of Common Mode Stiffness (CMS) and Configuration Dependent Stiffness (CDS). Using an ellipsoid representation of the desired grasp stiffness, the algorithm focuses on achieving its geometrical features. Based on preliminary knowledge of the fingers workspace, the method starts by exploring the possible hand poses that maintain the grasp contacts on the object. This outputs a first selection of feasible grasp configurations providing the base for the CDS control. Then, an optimization is performed to find the minimum joint stiffness (CMS control) that would stabilize these grasps. This joint stiffness can be increased afterwards depending on the task requirements. The algorithm finally chooses among all the found stable configurations the one that results in a better approximation of the desired grasp stiffness geometry (CDS). The proposed method results in a reduction of the control complexity, needing to independently regulate the joint positions, but requiring only one input to produce the desired joint stiffness. Moreover, the usage of the fingers pose to attain the desired grasp stiffness results in a more energy-efficient configuration than only relying on the joint stiffness (i.e., joint torques) modifications. The control strategy is evaluated using the fully actuated Allegro Hand while grasping a wide variety of objects. Different desired grasp stiffness profiles are selected to exemplify several stiffness geometries.
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http://dx.doi.org/10.3389/frobt.2018.00089DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805693PMC
July 2018

Soft brain-machine interfaces for assistive robotics: A novel control approach.

IEEE Int Conf Rehabil Robot 2017 07;2017:863-869

Robotic systems offer the possibility of improving the life quality of people with severe motor disabilities, enhancing the individual's degree of independence and interaction with the external environment. In this direction, the operator's residual functions must be exploited for the control of the robot movements and the underlying dynamic interaction through intuitive and effective human-robot interfaces. Towards this end, this work aims at exploring the potential of a novel Soft Brain-Machine Interface (BMI), suitable for dynamic execution of remote manipulation tasks for a wide range of patients. The interface is composed of an eye-tracking system, for an intuitive and reliable control of a robotic arm system's trajectories, and a Brain-Computer Interface (BCI) unit, for the control of the robot Cartesian stiffness, which determines the interaction forces between the robot and environment. The latter control is achieved by estimating in real-time a unidimensional index from user's electroencephalographic (EEG) signals, which provides the probability of a neutral or active state. This estimated state is then translated into a stiffness value for the robotic arm, allowing a reliable modulation of the robot's impedance. A preliminary evaluation of this hybrid interface concept provided evidence on the effective execution of tasks with dynamic uncertainties, demonstrating the great potential of this control method in BMI applications for self-service and clinical care.
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http://dx.doi.org/10.1109/ICORR.2017.8009357DOI Listing
July 2017

A real-time and reduced-complexity approach to the detection and monitoring of static joint overloading in humans.

IEEE Int Conf Rehabil Robot 2017 07;2017:828-834

This paper proposes a novel technique for the real-time estimation of the joint torques variations in humans while performing heavy manipulation tasks. To achieve this, the method is based on the deviations of the Centre of Pressure (CoP) and Ground Reaction Force (GRF) in the presence of interaction forces. The CoP and GRF variations are calculated from the difference between the estimated values (assuming no interaction forces) using a pre-identified statically equivalent serial chain (SESC) and the measured ones (with the effect of interaction forces) using an external device. The calculated variation vectors and the measured joint angles of the human body are then used for the estimation of the overloading joint torques in real-time. We evaluated the efficacy of the proposed method both in simulations and experiments, in various poses of the human and interaction force profiles.
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http://dx.doi.org/10.1109/ICORR.2017.8009351DOI Listing
July 2017

A Human-Robot Co-Manipulation Approach Based on Human Sensorimotor Information.

IEEE Trans Neural Syst Rehabil Eng 2017 07 17;25(7):811-822. Epub 2017 Apr 17.

This paper aims to improve the interaction and coordination between the human and the robot in cooperative execution of complex, powerful, and dynamic tasks. We propose a novel approach that integrates online information about the human motor function and manipulability properties into the hybrid controller of the assistive robot. Through this human-in-the-loop framework, the robot can adapt to the human motor behavior and provide the appropriate assistive response in different phases of the cooperative task. We experimentally evaluate the proposed approach in two human-robot co-manipulation tasks that require specific complementary behavior from the two agents. Results suggest that the proposed technique, which relies on a minimum degree of task-level pre-programming, can achieve an enhanced physical human-robot interaction performance and deliver appropriate level of assistance to the human operator.
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http://dx.doi.org/10.1109/TNSRE.2017.2694553DOI Listing
July 2017

Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography.

Front Neurorobot 2014 15;8:22. Epub 2014 Aug 15.

Institute of Biomedical Engineering, University of New Brunswick Fredericton, NB, Canada.

One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS)-Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PMI has been selected to denote human-machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the PNS in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it.
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http://dx.doi.org/10.3389/fnbot.2014.00022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4133701PMC
September 2014

Exploring teleimpedance and tactile feedback for intuitive control of the Pisa/IIT SoftHand.

IEEE Trans Haptics 2014 Apr-Jun;7(2):203-15

This paper proposes a teleimpedance controller with tactile feedback for more intuitive control of the Pisa/IIT SoftHand. With the aim to realize a robust, efficient and low-cost hand prosthesis design, the SoftHand is developed based on the motor control principle of synergies, through which the immense complexity of the hand is simplified into distinct motor patterns. Due to the built-in flexibility of the hand joints, as the SoftHand grasps, it follows a synergistic path while allowing grasping of objects of various shapes using only a single motor. The DC motor of the hand incorporates a novel teleimpedance control in which the user's postural and stiffness synergy references are tracked in real-time. In addition, for intuitive control of the hand, two tactile interfaces are developed. The first interface (mechanotactile) exploits a disturbance observer which estimates the interaction forces in contact with the grasped object. Estimated interaction forces are then converted and applied to the upper arm of the user via a custom made pressure cuff. The second interface employs vibrotactile feedback based on surface irregularities and acceleration signals and is used to provide the user with information about the surface properties of the object as well as detection of object slippage while grasping. Grasp robustness and intuitiveness of hand control were evaluated in two sets of experiments. Results suggest that incorporating the aforementioned haptic feedback strategies, together with user-driven compliance of the hand, facilitate execution of safe and stable grasps, while suggesting that a low-cost, robust hand employing hardware-based synergies might be a good alternative to traditional myoelectric prostheses.
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http://dx.doi.org/10.1109/TOH.2014.2309142DOI Listing
December 2015

A neuro-sliding-mode control with adaptive modeling of uncertainty for control of movement in paralyzed limbs using functional electrical stimulation.

IEEE Trans Biomed Eng 2009 Jul 27;56(7):1771-80. Epub 2009 Mar 27.

Department of Biomedical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran.

During the past several years, several strategies have been proposed for control of joint movement in paraplegic subjects using functional electrical stimulation (FES), but developing a control strategy that provides satisfactory tracking performance, to be robust against time-varying properties of muscle-joint dynamics, day-to-day variations, subject-to-subject variations, muscle fatigue, and external disturbances, and to be easy to apply without any re-identification of plant dynamics during different experiment sessions is still an open problem. In this paper, we propose a novel control methodology that is based on synergistic combination of neural networks with sliding-mode control (SMC) for controlling FES. The main advantage of SMC derives from the property of robustness to system uncertainties and external disturbances. However, the main drawback of the standard sliding modes is mostly related to the so-called chattering caused by the high-frequency control switching. To eliminate the chattering, we couple two neural networks with online learning without any offline training into the SMC. A recurrent neural network is used to model the uncertainties and provide an auxiliary equivalent control to keep the uncertainties to low values, and consequently, to use an SMC with lower switching gain. The second neural network consists of a single neuron and is used as an auxiliary controller. The control law will be switched from the SMC to neural control, when the state trajectory of system enters in some boundary layer around the sliding surface. Extensive simulations and experiments on healthy and paraplegic subjects are provided to demonstrate the robustness, stability, and tracking accuracy of the proposed neuroadaptive SMC. The results show that the neuro-SMC provides accurate tracking control with fast convergence for different reference trajectories and could generate control signals to compensate the muscle fatigue and reject the external disturbance.
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http://dx.doi.org/10.1109/TBME.2009.2017030DOI Listing
July 2009

Neuro-sliding mode control with modular models for control of knee-joint angle using quadriceps electrical stimulation.

Annu Int Conf IEEE Eng Med Biol Soc 2007 ;2007:2424-7

Department of Biomedical Engineering, Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, IRAN.

In this paper, we propose a control methodology which is based on synergistic combination of a single-neuron controller with sliding mode control (SMC) for control of knee-joint position in paraplegic subjects with quadriceps stimulation. The control law will be switched from the sliding mode control to neural control, when the state trajectory of system enters in some boundary layer around the sliding surface. The main drawback of the standard sliding modes is mostly related to the so-called chattering caused by the high-frequency control switching. The value of switching gain depends on the bounds of system uncertainties. The system with large uncertainties needs to use a higher switching gain. This will, however, result in the high-frequency control switching and chattering across the sliding surface. To avoid such a condition, it is necessary to decrease the system uncertainty. To decrease the uncertainty, an accurate model of the system is required. For this purpose, we present a modular approach to modeling the knee-joint dynamics. Extensive experiments on healthy and paraplegic subjects are provided to demonstrate the robustness, stability and tracking accuracy of the neuro-SMC. The experimental results show that the neuro-SMC provides excellent tracking control for different reference trajectories and could generate control signals to compensate the muscle fatigue.
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http://dx.doi.org/10.1109/IEMBS.2007.4352817DOI Listing
March 2008