I. Belli
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Biomechanics-aware control for robot-assisted physiotherapy
A novel approach to treating shoulder injuries
Musculoskeletal injuries are among the leading causes of pain, disability, and loss of independence worldwide. They affect millions of people, with prevalence rising steeply with age. One of the most common musculoskeletal injuries is tears to the shoulder rotator cuff. As these muscle-tendon tissues are anatomically constricted in a very narrow space between the shoulder bones, they are frequently subject to trauma or wear. Treatment of these injuries is both medically and socially pressing: they impair daily activities, limit the ability to work and engage in sports, and generate high personal and healthcare costs. Rehabilitation is essential to recovery, but it is often lengthy and labor-intensive for both physiotherapists (PTs) and patients. Moreover, it is prone to setbacks such as re-injury, since PTs lack quantitative tools to monitor the evolution of complex musculoskeletal structures during therapy.
In this context, the adoption of robotic devices offers opportunities to support manual manipulation of patients and provide sophisticated sensors to monitor them. Yet, despite advances in robot design and control, current systems remain unaware of the patient’s underlying biomechanics, and therefore cannot monitor or prevent harmful loading of healing tissues.
This thesis addresses such critical lack of knowledge by embedding state-of-the-art musculoskeletal models into the control of rehabilitation robots. Through the development of novel algorithms, it enables real-time estimation of deep muscle activity and tendon strain in the shoulder during physical human-robot interaction. By spanning from improved biomechanical simulations to their integration in robotic therapy execution, this work significantly advances the current state of the art to form a cohesive framework for biomechanics-aware robotic physiotherapy. ...
In this context, the adoption of robotic devices offers opportunities to support manual manipulation of patients and provide sophisticated sensors to monitor them. Yet, despite advances in robot design and control, current systems remain unaware of the patient’s underlying biomechanics, and therefore cannot monitor or prevent harmful loading of healing tissues.
This thesis addresses such critical lack of knowledge by embedding state-of-the-art musculoskeletal models into the control of rehabilitation robots. Through the development of novel algorithms, it enables real-time estimation of deep muscle activity and tendon strain in the shoulder during physical human-robot interaction. By spanning from improved biomechanical simulations to their integration in robotic therapy execution, this work significantly advances the current state of the art to form a cohesive framework for biomechanics-aware robotic physiotherapy. ...
Musculoskeletal injuries are among the leading causes of pain, disability, and loss of independence worldwide. They affect millions of people, with prevalence rising steeply with age. One of the most common musculoskeletal injuries is tears to the shoulder rotator cuff. As these muscle-tendon tissues are anatomically constricted in a very narrow space between the shoulder bones, they are frequently subject to trauma or wear. Treatment of these injuries is both medically and socially pressing: they impair daily activities, limit the ability to work and engage in sports, and generate high personal and healthcare costs. Rehabilitation is essential to recovery, but it is often lengthy and labor-intensive for both physiotherapists (PTs) and patients. Moreover, it is prone to setbacks such as re-injury, since PTs lack quantitative tools to monitor the evolution of complex musculoskeletal structures during therapy.
In this context, the adoption of robotic devices offers opportunities to support manual manipulation of patients and provide sophisticated sensors to monitor them. Yet, despite advances in robot design and control, current systems remain unaware of the patient’s underlying biomechanics, and therefore cannot monitor or prevent harmful loading of healing tissues.
This thesis addresses such critical lack of knowledge by embedding state-of-the-art musculoskeletal models into the control of rehabilitation robots. Through the development of novel algorithms, it enables real-time estimation of deep muscle activity and tendon strain in the shoulder during physical human-robot interaction. By spanning from improved biomechanical simulations to their integration in robotic therapy execution, this work significantly advances the current state of the art to form a cohesive framework for biomechanics-aware robotic physiotherapy.
In this context, the adoption of robotic devices offers opportunities to support manual manipulation of patients and provide sophisticated sensors to monitor them. Yet, despite advances in robot design and control, current systems remain unaware of the patient’s underlying biomechanics, and therefore cannot monitor or prevent harmful loading of healing tissues.
This thesis addresses such critical lack of knowledge by embedding state-of-the-art musculoskeletal models into the control of rehabilitation robots. Through the development of novel algorithms, it enables real-time estimation of deep muscle activity and tendon strain in the shoulder during physical human-robot interaction. By spanning from improved biomechanical simulations to their integration in robotic therapy execution, this work significantly advances the current state of the art to form a cohesive framework for biomechanics-aware robotic physiotherapy.
Conference paper
(2025)
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Italo Belli, Yuxuan Hu, J. Micah Prendergast, David Abbink, Ajay Seth, Luka Peternel
Combining biomechanical modeling with robotic physiotherapy is a promising direction to provide real-time insights during the rehabilitation of patients with musculoskeletal injuries, such as rotator-cuff tears. One aspect is to prevent re-injuries caused by high strain in the injured tissues while allowing patients to perform the required rehabilitation exercises. In this paper, we propose a novel shared control method for robots to limit unsafe patient movements, through physical guidance based on a strain-space representation of the human rotator cuff. The method provides motion corrections through two complementary predictive modules. The first module exerts a lower degree of intervention and is analogous to rumble strips or speed bumps for cars on the road. In this case, an impedance controller induces variable damping to slow down the patient's movement when a danger zone is approached. The second module produces a higher degree of intervention and is analogous to lane-assist in cars. In this case, the robot plans an optimal deflection trajectory and temporarily takes over control of the movement to avoid an unsafe situation. We performed experiments with a healthy participant acting as a patient and evaluated the effect of different human-robot interaction modalities on the resulting human movement in terms of avoidance of high-strain areas of the rotator-cuff tendons and contact forces exchanged.
...
Combining biomechanical modeling with robotic physiotherapy is a promising direction to provide real-time insights during the rehabilitation of patients with musculoskeletal injuries, such as rotator-cuff tears. One aspect is to prevent re-injuries caused by high strain in the injured tissues while allowing patients to perform the required rehabilitation exercises. In this paper, we propose a novel shared control method for robots to limit unsafe patient movements, through physical guidance based on a strain-space representation of the human rotator cuff. The method provides motion corrections through two complementary predictive modules. The first module exerts a lower degree of intervention and is analogous to rumble strips or speed bumps for cars on the road. In this case, an impedance controller induces variable damping to slow down the patient's movement when a danger zone is approached. The second module produces a higher degree of intervention and is analogous to lane-assist in cars. In this case, the robot plans an optimal deflection trajectory and temporarily takes over control of the movement to avoid an unsafe situation. We performed experiments with a healthy participant acting as a patient and evaluated the effect of different human-robot interaction modalities on the resulting human movement in terms of avoidance of high-strain areas of the rotator-cuff tendons and contact forces exchanged.
Modeling glenohumeral stability in musculoskeletal simulations
A validation study with in vivo contact forces
Journal article
(2025)
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Ibrahim Mohammed I. Hasan, Italo Belli, Ajay Seth, Elena M. Gutierrez-Farewik
Common optimization approaches for solving the muscle redundancy problem in musculoskeletal simulations can predict shoulder contact forces that either violate or barely satisfy joint stability requirements, with force directions falling outside or near the perimeter of the glenoid cavity. In this study, several glenohumeral stability formulations were tested against in vivo measurements of glenohumeral contact forces from the Orthoload dataset on one participant data in lateral, posterior, and anterior dumbbell raises. The investigated formulations either constrained the contact force direction to remain within different shapes of a stability perimeter, or added a penalty term that discouraged contact force directions from deviating from the glenoid cavity center. All stability formulations predicted contact force magnitudes that agreed relatively well to the in vivo measured forces except for the strictest formulation that constrained the joint contact force directly to the glenoid cavity center. Constraint and conditional penalty models estimated force vectors that largely lay along the perimeters. Continuous penalty models estimated relatively more accurate contact force directions within the glenoid cavity than constraint models. Our findings support the proposed penalty formulations as more reasonable and accurate than other investigated existing glenohumeral stability formulations.
...
Common optimization approaches for solving the muscle redundancy problem in musculoskeletal simulations can predict shoulder contact forces that either violate or barely satisfy joint stability requirements, with force directions falling outside or near the perimeter of the glenoid cavity. In this study, several glenohumeral stability formulations were tested against in vivo measurements of glenohumeral contact forces from the Orthoload dataset on one participant data in lateral, posterior, and anterior dumbbell raises. The investigated formulations either constrained the contact force direction to remain within different shapes of a stability perimeter, or added a penalty term that discouraged contact force directions from deviating from the glenoid cavity center. All stability formulations predicted contact force magnitudes that agreed relatively well to the in vivo measured forces except for the strictest formulation that constrained the joint contact force directly to the glenoid cavity center. Constraint and conditional penalty models estimated force vectors that largely lay along the perimeters. Continuous penalty models estimated relatively more accurate contact force directions within the glenoid cavity than constraint models. Our findings support the proposed penalty formulations as more reasonable and accurate than other investigated existing glenohumeral stability formulations.
Journal article
(2024)
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L. Noteboom, I. Belli, Marco J.M. Hoozemans, A. Seth, H.E.J. Veeger, F.C.T. van der Helm
While shoulder injuries resulting from the bench press exercise are commonly reported, no biomechanical evidence for lowering injury risk is currently available. Therefore, the aim of the present study was to compare musculoskeletal shoulder loads and potential injury risk during several bench press variations. Ten experienced strength athletes performed 21 technical variations of the barbell bench press, including variations in grip width of 1,1.5 and 2 bi-acromial widths (BAW), shoulder abduction angles of 45°, 70° and 90°, and scapula poses including neutral, retracted, and released conditions. Motions and forces were recorded by an opto-electronic measurement system and an instrumented barbell. An OpenSim musculoskeletal shoulder model was employed to estimate joint reaction forces in the glenohumeral and acromioclavicular joints. Time-series of joint reaction forces were compared between techniques by statistical non-parametric mapping. Results showed that narrower grip widths of <1.5 BAW decreased acromioclavicular compression (p < 0.05), which may decrease the risk for distal clavicular osteolysis. Moreover, scapula retraction, as well as a grip width of <1.5 BAW (p < 0.05), decreased glenohumeral posterior shear force components and rotator cuff activity and may decrease the risk for glenohumeral instability and rotator cuff injuries. Furthermore, results showed that mediolaterally exerted barbell force components varied considerably between athletes and largely affected shoulder reaction forces. It can be concluded that the grip width, scapula pose and mediolateral exerted barbell forces during the bench press influence musculoskeletal shoulder loads and the potential injury risk. Results of this study can contribute to safer bench press training guidelines.
...
While shoulder injuries resulting from the bench press exercise are commonly reported, no biomechanical evidence for lowering injury risk is currently available. Therefore, the aim of the present study was to compare musculoskeletal shoulder loads and potential injury risk during several bench press variations. Ten experienced strength athletes performed 21 technical variations of the barbell bench press, including variations in grip width of 1,1.5 and 2 bi-acromial widths (BAW), shoulder abduction angles of 45°, 70° and 90°, and scapula poses including neutral, retracted, and released conditions. Motions and forces were recorded by an opto-electronic measurement system and an instrumented barbell. An OpenSim musculoskeletal shoulder model was employed to estimate joint reaction forces in the glenohumeral and acromioclavicular joints. Time-series of joint reaction forces were compared between techniques by statistical non-parametric mapping. Results showed that narrower grip widths of <1.5 BAW decreased acromioclavicular compression (p < 0.05), which may decrease the risk for distal clavicular osteolysis. Moreover, scapula retraction, as well as a grip width of <1.5 BAW (p < 0.05), decreased glenohumeral posterior shear force components and rotator cuff activity and may decrease the risk for glenohumeral instability and rotator cuff injuries. Furthermore, results showed that mediolaterally exerted barbell force components varied considerably between athletes and largely affected shoulder reaction forces. It can be concluded that the grip width, scapula pose and mediolateral exerted barbell forces during the bench press influence musculoskeletal shoulder loads and the potential injury risk. Results of this study can contribute to safer bench press training guidelines.
In this work, we propose a method of capturing the patient’s discomfort during robotic shoulder physiotherapy, creating "discomfort maps". These maps depict the personalized distribution of discomfort that each patient perceived across their shoulder range of motion, facilitating both robotic devices and human therapists to account for patient-specific characteristics during the therapeutic process. Our system enables a patient to communicate and map discomfort in space and time during movement via a handheld push-button device, while interacting with a robotic physical therapy device capable of moving the patient and estimating their pose. We validated our method through human factors experiments simulating shoulder physiotherapy sessions with 10 healthy participants. To avoid the risk of injury to the participants and to allow for ground truth map information, we emulate perceived discomfort via an auditory signal. Our experimental apparatus enabled participants to reconstruct synthetic discomfort maps, demonstrating the feasibility of automatically capturing and storing patient discomfort during robotic physiotherapy.
...
In this work, we propose a method of capturing the patient’s discomfort during robotic shoulder physiotherapy, creating "discomfort maps". These maps depict the personalized distribution of discomfort that each patient perceived across their shoulder range of motion, facilitating both robotic devices and human therapists to account for patient-specific characteristics during the therapeutic process. Our system enables a patient to communicate and map discomfort in space and time during movement via a handheld push-button device, while interacting with a robotic physical therapy device capable of moving the patient and estimating their pose. We validated our method through human factors experiments simulating shoulder physiotherapy sessions with 10 healthy participants. To avoid the risk of injury to the participants and to allow for ground truth map information, we emulate perceived discomfort via an auditory signal. Our experimental apparatus enabled participants to reconstruct synthetic discomfort maps, demonstrating the feasibility of automatically capturing and storing patient discomfort during robotic physiotherapy.
Does enforcing glenohumeral joint stability matter?
A new rapid muscle redundancy solver highlights the importance of non-superficial shoulder muscles
Journal article
(2023)
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I. Belli, S.D. Joshi, J.M. Prendergast, I.L.Y. Beck, C. Della Santina, L. Peternel, A. Seth
The complexity of the human shoulder girdle enables the large mobility of the upper extremity, but also introduces instability of the glenohumeral (GH) joint. Shoulder movements are generated by coordinating large superficial and deeper stabilizing muscles spanning numerous degrees-of-freedom. How shoulder muscles are coordinated to stabilize the movement of the GH joint remains widely unknown. Musculoskeletal simulations are powerful tools to gain insights into the actions of individual muscles and particularly of those that are difficult to measure. In this study, we analyze how enforcement of GH joint stability in a musculoskeletal model affects the estimates of individual muscle activity during shoulder movements. To estimate both muscle activity and GH stability from recorded shoulder movements, we developed a Rapid Muscle Redundancy (RMR) solver to include constraints on joint reaction forces (JRFs) from a musculoskeletal model. The RMR solver yields muscle activations and joint forces by minimizing the weighted sum of squared-activations, while matching experimental motion. We implemented three new features: first, computed muscle forces include active and passive fiber contributions; second, muscle activation rates are enforced to be physiological, and third, JRFs are efficiently formulated as linear functions of activations. Muscle activity from the RMR solver without GH stability was not different from the computed muscle control (CMC) algorithm and electromyography of superficial muscles. The efficiency of the solver enabled us to test over 3600 trials sampled within the uncertainty of the experimental movements to test the differences in muscle activity with and without GH joint stability enforced. We found that enforcing GH stability significantly increases the estimated activity of the rotator cuff muscles but not of most superficial muscles. Therefore, a comparison of shoulder model muscle activity to EMG measurements of superficial muscles alone is insufficient to validate the activity of rotator cuff muscles estimated from musculoskeletal models.
...
The complexity of the human shoulder girdle enables the large mobility of the upper extremity, but also introduces instability of the glenohumeral (GH) joint. Shoulder movements are generated by coordinating large superficial and deeper stabilizing muscles spanning numerous degrees-of-freedom. How shoulder muscles are coordinated to stabilize the movement of the GH joint remains widely unknown. Musculoskeletal simulations are powerful tools to gain insights into the actions of individual muscles and particularly of those that are difficult to measure. In this study, we analyze how enforcement of GH joint stability in a musculoskeletal model affects the estimates of individual muscle activity during shoulder movements. To estimate both muscle activity and GH stability from recorded shoulder movements, we developed a Rapid Muscle Redundancy (RMR) solver to include constraints on joint reaction forces (JRFs) from a musculoskeletal model. The RMR solver yields muscle activations and joint forces by minimizing the weighted sum of squared-activations, while matching experimental motion. We implemented three new features: first, computed muscle forces include active and passive fiber contributions; second, muscle activation rates are enforced to be physiological, and third, JRFs are efficiently formulated as linear functions of activations. Muscle activity from the RMR solver without GH stability was not different from the computed muscle control (CMC) algorithm and electromyography of superficial muscles. The efficiency of the solver enabled us to test over 3600 trials sampled within the uncertainty of the experimental movements to test the differences in muscle activity with and without GH joint stability enforced. We found that enforcing GH stability significantly increases the estimated activity of the rotator cuff muscles but not of most superficial muscles. Therefore, a comparison of shoulder model muscle activity to EMG measurements of superficial muscles alone is insufficient to validate the activity of rotator cuff muscles estimated from musculoskeletal models.
In this research, we propose a novel method to estimate and monitor rotator cuff tendon strains during active robotic-assisted rehabilitation. This is a significant step forward from our previous work which estimated these tendon strains during passive exercises (i.e., no muscle activity). Physiotherapists adopt a cautious approach to rehabilitation treatment to prevent (re-) injury given the limited available information about the shoulder's internal condition. By leveraging a robotic device and a musculoskeletal model, our approach provides quantitative information on the risk of re-injury by monitoring the strains of the rotator cuff tendons during shoulder movements with the application of external loads. Muscle strains depend on the shoulder kinematic state and muscle activations, which makes it crucial to obtain physiologically realistic joint kinematics to estimate real-time muscle function. To obtain the strains, we utilize our muscle redundancy solver that incorporates constraints on model accelerations, the glenohumeral joint reaction forces, and active muscle dynamics. Using this algorithm along with force and pose data from a collaborative robotic arm, we demonstrate the ability to update our tendon strain estimates based on muscle activation during robotic-assisted rehabilitation exercises. The findings of our research pave the way for establishing improved therapy that considers the risk of injury to individual muscles and explores a broader and more personalized range of motion. By providing physiotherapists with valuable quantitative information on rotator cuff tendon strains, our method empowers them to optimize rehabilitation protocols and deliver more personalized and effective care. In addition, the system and method presented here comprise a tool capable of offering new insights into the relationship between the rotator cuff muscles, external forces, and shoulder kinematics.
...
In this research, we propose a novel method to estimate and monitor rotator cuff tendon strains during active robotic-assisted rehabilitation. This is a significant step forward from our previous work which estimated these tendon strains during passive exercises (i.e., no muscle activity). Physiotherapists adopt a cautious approach to rehabilitation treatment to prevent (re-) injury given the limited available information about the shoulder's internal condition. By leveraging a robotic device and a musculoskeletal model, our approach provides quantitative information on the risk of re-injury by monitoring the strains of the rotator cuff tendons during shoulder movements with the application of external loads. Muscle strains depend on the shoulder kinematic state and muscle activations, which makes it crucial to obtain physiologically realistic joint kinematics to estimate real-time muscle function. To obtain the strains, we utilize our muscle redundancy solver that incorporates constraints on model accelerations, the glenohumeral joint reaction forces, and active muscle dynamics. Using this algorithm along with force and pose data from a collaborative robotic arm, we demonstrate the ability to update our tendon strain estimates based on muscle activation during robotic-assisted rehabilitation exercises. The findings of our research pave the way for establishing improved therapy that considers the risk of injury to individual muscles and explores a broader and more personalized range of motion. By providing physiotherapists with valuable quantitative information on rotator cuff tendon strains, our method empowers them to optimize rehabilitation protocols and deliver more personalized and effective care. In addition, the system and method presented here comprise a tool capable of offering new insights into the relationship between the rotator cuff muscles, external forces, and shoulder kinematics.
In this work, we propose a method for monitoring and managing rotator-cuff (RC) tendon strains in human-robot collaborative physical therapy for shoulder rehabilitation. We integrate a high-resolution biomechanical model with a collaborative industrial robot arm and an impedance controller to provide feedback to a human subject, therapist or both, which prevents the subject from entering unsafe poses during rehabilitation. The biomechanical model estimates RC tendon strain as a function of human shoulder configuration, muscle activation and applied external forces. Subject- and injury-specific data are model estimates of strain that compose strain maps, which capture the relationship between the RC strains and movement of the shoulder degrees of freedom (DoF). High-strain regions of the strain map are identified as unsafe zones by clustering and ellipse fitting to smoothly demarcate these zones. These unsafe areas, which reflect increased risks of (re-)injury, are used to define parameters of an impedance controller and reference pose for real-time biomechanical safety control. Using strain maps we demonstrate both safe patient-led movements and teleoperated movements that prevent the subject from entering unsafe zones. In the teleoperated case, the physical therapist leads the patient remotely using a haptic device. The proposed method has the potential to improve the safety, range of motion, and volume of activity that a patient receives through robot-mediated physical therapy. We validated our approach using three experiments that demonstrate shoulder joint torques of less than 1 Nm during free motion with larger torques occurring only when the subject was asked to actively push into the unsafe boundary or, in the case of teleoperation, to resist the physical therapist.
...
In this work, we propose a method for monitoring and managing rotator-cuff (RC) tendon strains in human-robot collaborative physical therapy for shoulder rehabilitation. We integrate a high-resolution biomechanical model with a collaborative industrial robot arm and an impedance controller to provide feedback to a human subject, therapist or both, which prevents the subject from entering unsafe poses during rehabilitation. The biomechanical model estimates RC tendon strain as a function of human shoulder configuration, muscle activation and applied external forces. Subject- and injury-specific data are model estimates of strain that compose strain maps, which capture the relationship between the RC strains and movement of the shoulder degrees of freedom (DoF). High-strain regions of the strain map are identified as unsafe zones by clustering and ellipse fitting to smoothly demarcate these zones. These unsafe areas, which reflect increased risks of (re-)injury, are used to define parameters of an impedance controller and reference pose for real-time biomechanical safety control. Using strain maps we demonstrate both safe patient-led movements and teleoperated movements that prevent the subject from entering unsafe zones. In the teleoperated case, the physical therapist leads the patient remotely using a haptic device. The proposed method has the potential to improve the safety, range of motion, and volume of activity that a patient receives through robot-mediated physical therapy. We validated our approach using three experiments that demonstrate shoulder joint torques of less than 1 Nm during free motion with larger torques occurring only when the subject was asked to actively push into the unsafe boundary or, in the case of teleoperation, to resist the physical therapist.