Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

Yang, Sibo; Garg, Neha P.; Gao, Ruobin; Yuan, Meng; Noronha, Bernardo; Ang, Wei Tech; Accoto, Dino

doi:10.3390/s23062998

Cited by 8 publications

(3 citation statements)

References 44 publications

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“…A similar setting was demonstrated by Tortora et al [42] while combining IMU and EMG sensors to classify between several discrete targets. In a work more related to ours, two IMU's were combined with EMG and Mechanomyography (MMG) to predict future targets among several discrete ones [43]. However, the reaching was conducted only on a table with short trajectories to the targets and while the user is sitting down.…”

Section: Introductionmentioning

confidence: 99%

Learning Human-Arm Reaching Motion Using a Wearable Device in Human–Robot Collaboration

Kahanowich,

Sintov

2024

IEEE Access

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Many tasks performed by two humans require mutual interaction between arms such as handing-over tools and objects. In order for a robotic arm to interact with a human in the same way, it must reason about the location of the human arm in real-time. Furthermore, to acquire interaction in a timely manner, the robot must be able predict the final target of the human in order to plan and initiate motion beforehand. In this paper, we explore the use of a low-cost wearable device equipped with two inertial measurement units (IMU) for learning reaching motion in real-time applications of Human-Robot Collaboration (HRC). A wearable device can replace or be complementary to visual perception in cases of bad lighting or occlusions in a cluttered environment. We first train a neural-network model to estimate the current location of the arm. Then, we propose a novel model based on a recurrent neural-network to predict the future target of the human arm during motion in real-time. Early prediction of the target grants the robot with sufficient time to plan and initiate motion during the motion of the human. The accuracies of the models are analyzed concerning the features included in the motion representation. Through experiments and real demonstrations with a robotic arm, we show that sufficient accuracy is achieved for feasible HRC without any visual perception. Once trained, the system can be deployed in various spaces with no additional effort. The models exhibit high accuracy for various initial poses of the human arm. Moreover, the trained models are shown to provide high success rates with additional human participants not included in the training of the model.

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Section: Introductionmentioning

confidence: 99%

Learning Human-Arm Reaching Motion Using a Wearable Device in Human–Robot Collaboration

Kahanowich,

Sintov

2024

IEEE Access

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“…The integration of technology allows for personalized therapy, real-time feedback, and the potential for ramified rehabilitation exercises, thereby enhancing patient engagement [7]. Moreover, these new technologies have already been tested for the rehabilitation of the shoulder, indicating how this complex articulation can benefit from such a new approach [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Robotic-Assisted Rehabilitation for Post-Stroke Shoulder Pain: A Systematic Review

Gnasso,

Palermi,

Picone

et al. 2023

Sensors

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Post-stroke shoulder pain (PSSP) is a debilitating consequence of hemiplegia, often hindering rehabilitation efforts and further limiting motor recovery. With the advent of robotic-assisted therapies in neurorehabilitation, there is potential for innovative interventions for PSSP. This study systematically reviewed the current literature to determine the effectiveness of robotic-assisted rehabilitation in addressing PSSP in stroke patients. A comprehensive search of databases was conducted, targeting articles published up to August 2023. Studies were included if they investigated the impact of robotic-assisted rehabilitation on PSSP. The outcome of interest was pain reduction. The risk of bias was assessed using the Cochrane database. Of the 187 initially identified articles, 3 studies met the inclusion criteria, encompassing 174 patients. The reviewed studies indicated a potential benefit of robotic-assisted rehabilitation in reducing PSSP, with some studies also noting improvements in the range of motion and overall motor function. However, the results varied across studies, with some showing more significant benefits than others, because these use different protocols and robotic equipment.

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“…The execution of such an automated CM framework is also paramount by considering the enormous requirements of cleaning robots and opting for a suitable rental strategy for manufacturers or cleaning contract companies, followed by a fixed rental policy [13] for deployment irrespective of workspace conditions. Inertial measurement unit (IMU) sensors are versatile, especially for vibration-based applications, not only for robots but for human motion studies as well [14,15]. However, heterogeneous data are preferred for effective CM application in mobile robots with high-accuracy prediction, which complement each other during different scenarios affecting the robot's health and operational safety.…”

Section: Introductionmentioning

confidence: 99%

AI-Enabled Condition Monitoring Framework for Indoor Mobile Cleaning Robots

Pookkuttath,

Veerajagadheswar,

Rajesh Elara

2023

Mathematics

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Autonomous mobile cleaning robots are ubiquitous today and have a vast market need. Current studies are mainly focused on autonomous cleaning performances, and there exists a research gap on monitoring the robot’s health and safety. Vibration is a key indicator of system deterioration or external factors causing accelerated degradation or threats. Hence, this work proposes an artificial intelligence (AI)-enabled automated condition monitoring (CM) framework using two heterogeneous sensor datasets to predict the sources of anomalous vibration in mobile robots with high accuracy. This allows triggering proper maintenance or corrective actions based on the condition of the robot’s health or workspace, easing condition-based maintenance (CbM). Anomalous vibration sources are classified as induced by uneven Terrain, Collision with obstacles, loose Assembly, and unbalanced Structure, which causes accelerated system deterioration or potential hazards. Here, an unexplored heterogeneous sensor dataset using inertial measurement unit (IMU) and current sensors is proposed for effective recognition across different vibration classes, resulting in higher-accuracy prediction. A simple-structured 1D convolutional neural network (1D CNN) is developed for training and real-time prediction. A 2D CbM map is generated by fusing the predicted classes in real time on an occupancy grid map of the workspace to monitor the conditions of the robot and workspace remotely. The evaluation test results of the proposed method show that the usage of heterogeneous sensors performs significantly more accurately (98.4%) than previous studies, which used IMU (92.2%) and camera (93.8%) sensors individually. Also, this model is comparatively fast, fit for the environment, and ideal for real-time applications in mobile robots based on field trial validations, enhancing mobile robots’ productivity and operational safety.

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Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

Cited by 8 publications

References 44 publications

Learning Human-Arm Reaching Motion Using a Wearable Device in Human–Robot Collaboration

Learning Human-Arm Reaching Motion Using a Wearable Device in Human–Robot Collaboration

Robotic-Assisted Rehabilitation for Post-Stroke Shoulder Pain: A Systematic Review

AI-Enabled Condition Monitoring Framework for Indoor Mobile Cleaning Robots

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