Tailoring Upper-Limb Robot-Aided Orthopedic Rehabilitation on Patients’ Psychophysiological State

Tamantini, Christian; Cordella, Francesca; Lauretti, Clemente; Luzio, Francesco Scotto di; Campagnola, Benedetta; Cricenti, Laura; Bravi, Marco; Bressi, Federica; Draicchio, Francesco; Sterzi, Silvia; Zollo, Loredana

doi:10.1109/tnsre.2023.3298381

Cited by 16 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, Equation ( 1) can be rewritten as M e (q) q + M ∆ (q) q = −C(q, q) q − g(q) − δ(q, q) + τ d + τ. (6) Then, the angular acceleration is q = M e (q) −1 τ − M e (q) −1 [M ∆ (q) q + C(q, q) q + g(q) + δ(q, q) − τ d ].…”

Section: Control Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Design of a Lower Limb Exoskeleton: Robust Control, Simulation and Experimental Results

Salcido,

Centeno-Barreda,

Rosales

et al. 2023

Algorithms

View full text Add to dashboard Cite

This paper presents the development of a robust control algorithm to be applied in a knee and ankle joint exoskeleton designed for rehabilitation of flexion/extension movements. The goal of the control law is to follow the trajectory of a straight leg extension routine in a sitting position. This routine is commonly used to rehabilitate an injury on an Anterior Cruciate Ligament (ACL) and it is applied to the knee and ankle joints. Moreover, the paper presents the development and implementation of the robotic structure of the ankle joint to integrate it into an exoskeleton for gait rehabilitation. The development of the dynamic model and the implementation of the control algorithm in simulation and experimental tests are presented, showing that the proposed control guarantees the convergence of the tracking error.

show abstract

Section: Control Algorithmmentioning

confidence: 99%

“…In [6], the suggested robotic architecture considered the patients' kinematic performance in addition to their psychophysiological condition, for example, heart and respiratory activity and galvanic skin response. Ref.…”

Section: Introductionmentioning

confidence: 99%

Design of a Lower Limb Exoskeleton: Robust Control, Simulation and Experimental Results

Salcido,

Centeno-Barreda,

Rosales

et al. 2023

Algorithms

View full text Add to dashboard Cite

show abstract

“…Among all the devices purposely designed to assist patients' lower-limbs, exoskeletons are wearable robots that closely interact with humans. Typically, they are mechatronic structures that operate alongside human limbs and increase human locomotory economy, augment joint strength, and increase endurance and strength (Tamantini et al, 2023a). Nowadays, exoskeleton devices are studied and employed in many application scenarios such as industry (Masood et al, 2016), space (Lovasz et al, 2017) and healthcare (Bortole et al, 2015;Pecoraro et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review

Coser,

Tamantini,

Soda

et al. 2024

Front. Robot. AI

Self Cite

View full text Add to dashboard Cite

Over the past few years, there has been a noticeable surge in efforts to design novel tools and approaches that incorporate Artificial Intelligence (AI) into rehabilitation of persons with lower-limb impairments, using robotic exoskeletons. The potential benefits include the ability to implement personalized rehabilitation therapies by leveraging AI for robot control and data analysis, facilitating personalized feedback and guidance. Despite this, there is a current lack of literature review specifically focusing on AI applications in lower-limb rehabilitative robotics. To address this gap, our work aims at performing a review of 37 peer-reviewed papers. This review categorizes selected papers based on robotic application scenarios or AI methodologies. Additionally, it uniquely contributes by providing a detailed summary of input features, AI model performance, enrolled populations, exoskeletal systems used in the validation process, and specific tasks for each paper. The innovative aspect lies in offering a clear understanding of the suitability of different algorithms for specific tasks, intending to guide future developments and support informed decision-making in the realm of lower-limb exoskeleton and AI applications.

show abstract

“…However, it relies on specialized equipment (optoelectronic motion capture system and markers), which may not be universally accessible, and contains the potential lack of comprehensive coverage for all clinical scenarios and hand variations. Tamantini et al (2023) introduced a psychophysiological-aware control strategy for upper limb robot-aided orthopedic rehabilitation, allowing adaptable treatment, guiding patients’ movements, and adjusting control parameters based on patient performance and psychophysiological state. However, it involved a relatively small sample of eight orthopedic patients, which may limit the generalizability of the findings to a larger population.…”

Section: Introductionmentioning

confidence: 99%

Modeling multi-contact point physical interaction between the anthropomorphic finger and soft robotic exo-digit for wearable rehabilitation robotics applications

Alam,

Shedd,

Kirkland

et al. 2023

Front. Robot. AI

View full text Add to dashboard Cite

Introduction: Effective control of rehabilitation robots requires considering the distributed and multi-contact point physical human–robot interaction and users’ biomechanical variation. This paper presents a quasi-static model for the motion of a soft robotic exo-digit while physically interacting with an anthropomorphic finger model for physical therapy.Methods: Quasi-static analytical models were developed for modeling the motion of the soft robot, the anthropomorphic finger, and their coupled physical interaction. An intertwining of kinematics and quasi-static motion was studied to model the distributed (multiple contact points) interaction between the robot and a human finger model. The anthropomorphic finger was modeled as an articulated multi-rigid body structure with multi-contact point interaction. The soft robot was modeled as an articulated hybrid soft-and-rigid model with a constant bending curvature and a constant length for each soft segment. A hyperelastic constitute model based on Yeoh’s 3rdorder material model was used for modeling the soft elastomer. The developed models were experimentally evaluated for 1) free motion of individual soft actuators and 2) constrained motion of the soft robotic exo-digit and anthropomorphic finger model.Results and Discussion: Simulation and experimental results were compared for performance evaluations. The theoretical and experimental results were in agreement for free motion, and the deviation from the constrained motion was in the range of the experimental errors. The outcomes also provided an insight into the importance of considering lengthening for the soft actuators.

show abstract

Tailoring Upper-Limb Robot-Aided Orthopedic Rehabilitation on Patients’ Psychophysiological State

Cited by 16 publications

References 34 publications

Design of a Lower Limb Exoskeleton: Robust Control, Simulation and Experimental Results

Design of a Lower Limb Exoskeleton: Robust Control, Simulation and Experimental Results

AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review

Modeling multi-contact point physical interaction between the anthropomorphic finger and soft robotic exo-digit for wearable rehabilitation robotics applications

Contact Info

Product

Resources

About