An fMRI study on the generalization of motor learning after brain actuated supernumerary robot training

Generalization is central to motor learning. However, few studies are on the learning generalization of BCI-actuated supernumerary robotic finger (BCI-SRF) for human-machine interaction training, and no studies have explored its longitudinal neuroplasticity mechanisms. Here, 20 healthy right-handed participants were recruited and randomly assigned to BCI-SRF group or inborn finger group (Finger) for 4-week training and measured by novel SRF-finger opposition sequences and multimodal MRI. After training, the BCI-SRF group showed 350% times compared to the Finger group in the improvement of sequence opposition accuracy before and after training, and accompanied by significant functional connectivity increases in the sensorimotor region and prefrontal cortex, as well as in the intra- and inter-hemisphere of the sensorimotor network. Moreover, Granger Causality Analysis identified causal effect main transfer within the sensorimotor cortex-cerebellar-thalamus loop and frontal-parietal loop. The findings suggest that BCI-SRF training enhances motor sequence learning ability by influencing the functional reorganization of sensorimotor network.

Active, Actuated, and Assistive: a Scoping Review of Exoskeletons for the Hands and Wrists

BACKGROUND

Assistive technology is often incorporated into rehabilitation and support for those impacted by upper limb impairments. When powered, these devices provide additional force to the joints of users with muscle weakness. Actuated devices allow dynamic movement compared to splints, therefore improving the ability to complete activities of daily living. However, these devices are not often prescribed and are underrepresented in research and clinical settings.

OBJECTIVE

This review examined the existing literature on devices developed to support hand and wrist functionality in daily activities. Focusing on active, powered, and actuated devices, to gain a clearer understanding of the current limitations in their design and prescription.

METHODOLOGY

The scoping review was conducted using the PRISMA-ScR guidelines. A systematic search was done on MEDLINE, EMBASE, Scopus, Web of Science, and NHS the Knowledge Network from inception to May 2023. Articles were included if the device was portable; supported the hands and wrist actively using an actuator; and could be used for assistive living during or post-rehabilitation period.

FINDINGS

A total of 135 studies were included in the analysis of which 34 were clinical trials. The design and control methods of 121 devices were analyzed. Electrical stimulation and direct mechanical transmission were popular actuation methods. Electromyography (EMG) and joint movement detection were highly used control methods to translate user intentions to device actuation. A total of 226 validation methods were reported, of which 44% were clinically validated. Studies were often not conducted in operational environments with 69% at technology readiness levels ≤ 6, indicating that further development and testing is required.

CONCLUSION

The existing literature on hand and wrist exoskeletons presents large variations in validation methods and technical requirements for user-specific characteristics. This suggests a need for well-defined testing protocols and refined reporting of device designs. This would improve the significance of clinical outcomes and new assistive technology.

Enhancement of Functional Connectivity in Frontal-Parietal Regions After BCI-Actuated Supernumerary Robotic Finger Training

The supernumerary robotic finger (SRF) can expand human hand abilities to achieve motor augmentation, and integrate with brain computer interface (BCI) to free the occupation of inherent body degrees of freedom. However, the neuro remodeling mechanisms of brain-actuated SRF training is not clear. In this study, a BCI-actuated SRF was used to investigate the concurrent changes in behavior and brain activity. After 4 weeks BCI-SRF training, the novel sequence operation accuracy rate enhanced by more than 350% compared with innate finger training (IFT). Task-based fMRI showed a significant increase in lateral activation of sensorimotor cortex and found a significant activation change in S1M1_L area. Moreover, BCI-SRF training significantly increase functional connectivity (FC) between S1M1_L and Frontal_Mid_L compared with IFT at post stage. And this FC increase in frontal-parietal is also significant at post vs pre in BCI-SRF group and significantly correlated with the improvement of motor sequence accuracy rate. Our findings provide useful insights into the enhanced human-machine interaction and this efficacy exhibited significant potential for clinical rehabilitation application.

A Probability Fusion Approach for Foot Placement Prediction in Complex Terrains

Prediction of foot placement presents great potential in better assisting the walking of people with lower-limb disability in daily terrains. Previous researches mainly focus on foot placement prediction in level ground walking, however these methods cannot be applied to daily complex terrains including ramps, stairs, and level ground with obstacles. To predict foot placement in complex terrains, this paper presents a probability fusion approach for foot placement prediction in complex terrains which consists of two parts: model training and foot placement prediction. In the first part, a deep learning model is trained on augmented data to predict the probability distribution of preliminary foot placement. In the second part, environmental information and human walking constraints are used to calculate the feasible area, and finally the feasible area is fused with the probability distribution of preliminary foot placement to predict the foot placement in complex terrains. The proposed method can predict the foot placement of next step in complex terrains when heel-off is detected. Experiments (including structured terrains experiments and complex terrains experiments) show that the root mean square error (RMSE) of prediction is 8.19 ± 1.20 cm, which is less than 8% of the average stride length, and the landing feasible area accuracy (LFAA) of prediction is 95.11 ± 3.09%. Comparing with existing foot placement prediction studies, the method proposed in this paper achieves faster and more accurate prediction in complex terrains.

An Upper-Limb Rehabilitation Exoskeleton System Controlled by MI Recognition Model With Deep Emphasized Informative Features in a VR Scene

The prevalence of stroke continues to increase with the global aging. Based on the motor imagery (MI) brain-computer interface (BCI) paradigm and virtual reality (VR) technology, we designed and developed an upper-limb rehabilitation exoskeleton system (VR-ULE) in the VR scenes for stroke patients. The VR-ULE system makes use of the MI electroencephalogram (EEG) recognition model with a convolutional neural network and squeeze-and-excitation (SE) blocks to obtain the patient's motion intentions and control the exoskeleton to move during rehabilitation training movement. Due to the individual differences in EEG, the frequency bands with optimal MI EEG features for each patient are different. Therefore, the weight of different feature channels is learned by combining SE blocks to emphasize the useful information frequency band features. The MI cues in the VR-based virtual scenes can improve the interhemispheric balance and the neuroplasticity of patients. It also makes up for the disadvantages of the current MI-BCIs, such as single usage scenarios, poor individual adaptability, and many interfering factors. We designed the offline training experiment to evaluate the feasibility of the EEG recognition strategy, and designed the online control experiment to verify the effectiveness of the VR-ULE system. The results showed that the MI classification method with MI cues in the VR scenes improved the accuracy of MI classification (86.49% ± 3.02%); all subjects performed two types of rehabilitation training tasks under their own models trained in the offline training experiment, with the highest average completion rates of 86.82% ± 4.66% and 88.48% ± 5.84%. The VR-ULE system can efficiently help stroke patients with hemiplegia complete upper-limb rehabilitation training tasks, and provide the new methods and strategies for BCI-based rehabilitation devices.

Human Operation Augmentation through Wearable Robotic Limb Integrated with Mixed Reality Device

Mixed reality technology can give humans an intuitive visual experience, and combined with the multi-source information of the human body, it can provide a comfortable human-robot interaction experience. This paper applies a mixed reality device (Hololens2) to provide interactive communication between the wearer and the wearable robotic limb (supernumerary robotic limb, SRL). Hololens2 can obtain human body information, including eye gaze, hand gestures, voice input, etc. It can also provide feedback information to the wearer through augmented reality and audio output, which is the communication bridge needed in human-robot interaction. Implementing a wearable robotic arm integrated with HoloLens2 is proposed to augment the wearer's capabilities. Taking two typical practical tasks of cable installation and electrical connector soldering in aircraft manufacturing as examples, the task models and interaction scheme are designed. Finally, human augmentation is evaluated in terms of task completion time statistics.

Impact of supplementary sensory feedback on the control and embodiment in human movement augmentation

In human movement augmentation, the number of controlled degrees of freedom could be enhanced by the simultaneous and independent use of supernumerary robotic limbs (SRL) and natural ones. However, this poses several challenges, that could be mitigated by encoding and relaying the SRL status. Here, we review the impact of supplementary sensory feedback on the control and embodiment of SRLs. We classify the main feedback features and analyse how they improve control performance. We report the feasibility of pushing body representation beyond natural human morphology and suggest that gradual SRL embodiment could make multisensory incongruencies less disruptive. We also highlight shared computational bases between SRL motor control and embodiment and suggest contextualizing them within the same theoretical framework. Finally, we argue that a shift towards long term experimental paradigms is necessary for successfully integrating motor control and embodiment.

Supernumerary robotic limbs are robotic devices providing additional limbs to the user. Mattia Pinardi and colleagues review the impact of supplementary sensory feedback on the control performance and embodiment of supernumerary robotic limbs.

Comparing end-effector position and joint angle feedback for online robotic limb tracking

Somatosensation greatly increases the ability to control our natural body. This suggests that supplementing vision with haptic sensory feedback would also be helpful when a user aims at controlling a robotic arm proficiently. However, whether the position of the robot and its continuous update should be coded in a extrinsic or intrinsic reference frame is not known. Here we compared two different supplementary feedback contents concerning the status of a robotic limb in 2-DoFs configuration: one encoding the Cartesian coordinates of the end-effector of the robotic arm (i.e., Task-space feedback) and another and encoding the robot joints angles (i.e., Joint-space feedback). Feedback was delivered to blindfolded participants through vibrotactile stimulation applied on participants' leg. After a 1.5-hour training with both feedbacks, participants were significantly more accurate with Task compared to Joint-space feedback, as shown by lower position and aiming errors, albeit not faster (i.e., similar onset delay). However, learning index during training was significantly higher in Joint space feedback compared to Task-space feedback. These results suggest that Task-space feedback is probably more intuitive and more suited for activities which require short training sessions, while Joint space feedback showed potential for long-term improvement. We speculate that the latter, despite performing worse in the present work, might be ultimately more suited for applications requiring long training, such as the control of supernumerary robotic limbs for surgical robotics, heavy industrial manufacturing, or more generally, in the context of human movement augmentation.

Wearable Supernumerary Robotic Limb System Using a Hybrid Control Approach Based on Motor Imagery and Object Detection

Motor disorder of upper limbs has seriously affected the daily life of the patients with hemiplegia after stroke. We developed a wearable supernumerary robotic limb (SRL) system using a hybrid control approach based on motor imagery (MI) and object detection for upper-limb motion assistance. SRL system included an SRL hardware subsystem and a hybrid control software subsystem. The system obtained the patient's motion intention through MI electroencephalogram (EEG) recognition method based on graph convolutional network (GCN) and gated recurrent unit network (GRU) to control the left and right movements of SRL, and the object detection technology was used together for a quick grasp of target objects to compensate for the disadvantages when using MI EEG alone like fewer control instructions and lower control efficiency. Offline training experiment was designed to obtain subjects' MI recognition models and evaluate the feasibility of the MI EEG recognition method; online control experiment was designed to verify the effectiveness of our wearable SRL system. The results showed that the proposed MI EEG recognition method (GCN+GRU) could effectively improve the MI classification accuracy (90.04% ± 2.36%) compared with traditional methods; all subjects were able to complete the target object grasping tasks within 23 seconds by controlling the SRL, and the highest average grasping success rate achieved 90.67% in bag grasping task. The SRL system can effectively assist people with upper-limb motor disorder to perform upper-limb tasks in daily life by natural human-robot interaction, and improve their ability of self-help and enhance their confidence of life.

Principles of human movement augmentation and the challenges in making it a reality

Augmenting the body with artificial limbs controlled concurrently to one's natural limbs has long appeared in science fiction, but recent technological and neuroscientific advances have begun to make this possible. By allowing individuals to achieve otherwise impossible actions, movement augmentation could revolutionize medical and industrial applications and profoundly change the way humans interact with the environment. Here, we construct a movement augmentation taxonomy through what is augmented and how it is achieved. With this framework, we analyze augmentation that extends the number of degrees-of-freedom, discuss critical features of effective augmentation such as physiological control signals, sensory feedback and learning as well as application scenarios, and propose a vision for the field.

EEG characteristic investigation of the sixth-finger motor imagery and optimal channel selection for classification

OBJECTIVE

Supernumerary Robotic Limbs (SRL) are body augmentation robotic devices by adding extra limbs or fingers to the human body different from the traditional wearable robotic devices such as prosthesis and exoskeleton. We proposed a novel MI (Motor imagery)-based BCI paradigm based on the sixth-finger which imagines controlling the extra finger movements. The goal of this work is to investigate the EEG characteristics and the application potential of MI-based BCI systems based on the new imagination paradigm (the sixth finger MI).

APPROACH

14 subjects participated in the experiment involving the sixth finger MI tasks and rest state. Event-related spectral perturbation (ERSP) was adopted to analyse EEG spatial features and key-channel time-frequency features. Common spatial patterns (CSP) were used for feature extraction and classification was implemented by support vector machine (SVM). A genetic algorithm (GA) was used to select combinations of EEG channels that maximized classification accuracy and verified EEG patterns based on the sixth finger MI. And we conducted a longitudinal 4-week EEG control experiment based on the new paradigm.

MAIN RESULTS

ERD (event-related desynchronization) was found in the supplementary motor area (SMA) and primary motor area (M1) with a faint contralateral dominance. Unlike traditional MI based on the human hand, ERD was also found in frontal lobe. GA results showed that the distribution of the optimal 8-channel is similar to EEG topographical distributions, nearing parietal and frontal lobe. And the classification accuracy based on the optimal 8-channel (the highest accuracy of 80% and mean accuracy of 70%) was significantly better than that based on the random 8-channel (p<0.01).

SIGNIFICANCE

This work provided a new paradigm for MI-based MI system and verified its feasibility, widened the control bandwidth of the BCI system.

Quantitative Investigation of Hand Grasp Functionality: Thumb Grasping Behavior Adapting to Different Object Shapes, Sizes, and Relative Positions

This paper is the first in the two-part series quantitatively modelling human grasp functionality and understanding the way human grasp objects. The aim is to investigate the thumb movement behavior influenced by object shapes, sizes, and relative positions. Ten subjects were requested to grasp six objects (3 shapes × 2 sizes) in 27 different relative positions (3 X deviation × 3 Y deviation × 3 Z deviation). Thumb postures were investigated to each specific joint. The relative position (X, Y, and Z deviation) significantly affects thumb opposition rotation (Rot) and flexion (interphalangeal (IP) and metacarpo-phalangeal (MCP)), while the object property (object shape and size) significantly affects thumb abduction/adduction (ABD) motion. Based on the F value, the Y deviation has the primary effects on thumb motion. When the Y deviation changing from proximal to distal, thumb opposition rotation (Rot) and flexion (IP and MCP joint) angles were increased and decreased, respectively. For principal component analysis (PCA) results, thumb grasp behavior can be accurately reconstructed by first two principal components (PCs) which variance explanation ratio reached 93.8% and described by the inverse and homodromous coordination movement between thumb opposition and IP flexion. This paper provides a more comprehensive understanding of thumb grasp behavior. The postural synergies can reproduce the anthropomorphic motion, reduce the robot hardware, and control dimensionality. All of these provide a more accurate and general basis for the design and control of the bionic thumb and novel wearable assistant robot, thumb function assessment, and rehabilitation.

Playing the piano with a robotic third thumb: assessing constraints of human augmentation

Contemporary robotics gives us mechatronic capabilities for augmenting human bodies with extra limbs. However, how our motor control capabilities pose limits on such augmentation is an open question. We developed a Supernumerary Robotic 3rd Thumbs (SR3T) with two degrees-of-freedom controlled by the user’s body to endow them with an extra contralateral thumb on the hand. We demonstrate that a pianist can learn to play the piano with 11 fingers within an hour. We then evaluate 6 naïve and 6 experienced piano players in their prior motor coordination and their capability in piano playing with the robotic augmentation. We show that individuals’ augmented performance with the SR3T could be explained by our new custom motor coordination assessment, the Human Augmentation Motor Coordination Assessment (HAMCA) performed pre-augmentation. Our work demonstrates how supernumerary robotics can augment humans in skilled tasks and that individual differences in their augmentation capability are explainable by their individual motor coordination abilities.

Human performance in three-hands tasks

The successful completion of complex tasks like hanging a picture or laparoscopic surgery requires coordinated motion of more than two limbs. User-controlled supernumerary robotic limbs (SL) have been proposed to bypass the need for coordination with a partner in such tasks. However, neither the capability to control multiple limbs alone relative to collaborative control with partners, nor how that capability varies across different tasks, is well understood. In this work, we present an investigation of tasks requiring three-hands where the foot was used as an additional source of motor commands. We considered: (1) how does simultaneous control of three hands compare to a cooperating dyad; (2) how this relative performance was altered by the existence of constraints emanating from real or virtual physical connections (mechanical constraints) or from cognitive limits (cognitive constraints). It was found that a cooperating dyad outperformed a single user in all scenarios in terms of task score, path efficiency and motion smoothness. However, while the participants were able to reach more targets with increasing mechanical constraints/decreasing number of simultaneous goals, the relative difference in performance between a dyad and a participant performing trimanual activities decreased, suggesting further potential for SLs in this class of scenario.

Inherent Haptic Feedback From Supernumerary Robotic Limbs

Supernumerary Robotics Limbs, or SuperLimbs for short, are wearable extra limbs for augmenting the wearer. SuperLimbs are attached directly to a human and, thereby, transmit a force from the environment to the human body. This inherent haptic feedback allows the human to perceive the interaction between the robot and the environment, monitor its actions, and effectively control the robot. This article addresses basic properties and the usefulness of the inherent haptic feedback from SuperLimbs in two exemplary cases. First, we show that the inherent haptic feedback allows the wearer to close the loop and manually regulate the force output of the SuperLimb. Second, we show that the inherent haptic feedback is sufficient for the wearer to supervise the autonomous actions of the SuperLimb. This ability is a critical requirement for safely and effectively performing multiple tasks simultaneously with the natural limbs and SuperLimbs. Together, these findings suggest the importance of designing SuperLimbs to take advantage of the inherent haptic feedback.

Laying the Groundwork for Intra-Robotic-Natural Limb Coordination

Supernumerary Robotic Limbs (SRLs) have been successfully applied in bracing and as an assistive technology for people with disabilities. These tasks only require perception internal to the SRL-human system. However, SRLs show promise in applications requiring external perception such as opening a door when one’s hands are full. One path toward developing SRLs that accomplish these tasks is to use human-in-the-loop control, thus leveraging the human’s superior perception system to help the SRLs. However, the effects on the user of controlling additional limbs are unclear. This article presents an experimental study where humans, wearing two single degree of freedom SRLs, were instructed to minimize the position error between the subject’s natural and robotic limbs and the corresponding targets, one for each limb. First, subjects performed worse with their natural limbs when asked to perform the task with two natural and two robotic limbs as opposed to with just their natural limbs, suggesting that shared control could help. Second, subjects moved their natural limbs together followed by moving their SRLs together. This informs both the choice of control scheme for the SRLs and the division of labor within a task. Third, subjects showed significant concurrent use of the natural and robotic limbs.

Grasp Posture Control of Wearable Extra Robotic Fingers with Flex Sensors Based on Neural Network

This study proposes a data-driven control method of extra robotic fingers to assist a user in bimanual object manipulation that requires two hands. The robotic system comprises two main parts, i.e., robotic thumb (RT) and robotic fingers (RF). The RT is attached next to the user’s thumb, while the RF is located next to the user’s little finger. The grasp postures of the RT and RF are driven by bending angle inputs of flex sensors, attached to the thumb and other fingers of the user. A modified glove sensor is developed by attaching three flex sensors to the thumb, index, and middle fingers of a wearer. Various hand gestures are then mapped using a neural network. The input data of the robotic system are the bending angles of thumb and index, read by flex sensors, and the outputs are commanded servo angles for the RF and RT. The third flex sensor is attached to the middle finger to hold the extra robotic finger’s posture. Two force-sensitive resistors (FSRs) are attached to the RF and RT for the haptic feedback when the robot is worn to take and grasp a fragile object, such as an egg. The trained neural network is embedded into the wearable extra robotic fingers to control the robotic motion and assist the human fingers in bimanual object manipulation tasks. The developed extra fingers are tested for their capacity to assist the human fingers and perform 10 different bimanual tasks, such as holding a large object, lifting and operate an eight-inch tablet, and lifting a bottle, and opening a bottle cap at the same time.

Lesion positioning method of a CT-guided surgical robotic system for minimally invasive percutaneous lung

BACKGROUND

Robot-assisted puncture has gradually attracted more attention and practical clinical application. The lesion positioning and the needle positioning are the basis to ensure the accuracy of puncture and the key techniques in insertion operation.

METHODS

A lesion positioning method is established which is realized only by the robot-CT system without using external positioning system, and an omnidirectional needle positioning method is also developed and realized by using VRCM, in order to make the puncture needle always keep pointing to the lesion point. A CT-guided surgical robotic system used for minimally invasive percutaneous lung is designed and the physical prototype is manufactured, to perform in-vitro experiments, thereby to validate the effectiveness of the lesion positioning method and the feasibility of omnidirectional needle positioning method.

RESULTS

The accuracy of established lesion positioning method based on three non-collinear markers is within 3 mm, which is similar to that of the least squares method based on the five non-coplanar markers, but the positioning efficiency can be improved by about 40%, and the non-collinearity of markers is easier to be satisfied than non-coplanarity in practical applications. The average calculation error of the established positioning method is 0.997 mm. Moreover, the omnidirectional positioning of the puncture needle under the designed surgical robot is feasible.

CONCLUSIONS

The designed surgical robot has good control accuracy and it can satisfy the requirements for use. The established lesion positioning method can provide a good precision basis for robot-assisted puncture surgery. The suitable insertion point and insertion posture can be determined by the developed omnidirectional needle positioning method. This study can provide theoretical reference for further study of path planning or autonomous positioning.

Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning

In emergency departments, the most common cause of death associated with suspected infected patients is sepsis. In this study, deep learning algorithms were used to predict the mortality of suspected infected patients in a hospital emergency department. During January 2007 and December 2013, 42,220 patients considered in this study were admitted to the emergency department due to suspected infection. In the present study, a deep learning structure for mortality prediction of septic patients was developed and compared with several machine learning methods as well as two sepsis screening tools: the systemic inflammatory response syndrome (SIRS) and quick sepsis-related organ failure assessment (qSOFA). The mortality predictions were explored for septic patients who died within 72 h and 28 days. Results demonstrated that the accuracy rate of deep learning methods, especially Convolutional Neural Network plus SoftMax (87.01% in 72 h and 81.59% in 28 d), exceeds that of the other machine learning methods, SIRS, and qSOFA. We expect that deep learning can effectively assist medical staff in early identification of critical patients.

Soft Poly-Limbs: Toward a New Paradigm of Mobile Manipulation for Daily Living Tasks

We present the design and development of the fluid-driven, wearable, Soft Poly-Limb (SPL), from the Greek word polys, meaning many. The SPL utilizes the numerous traits of soft robotics to enable a novel approach in providing safe and compliant mobile manipulation assistance to healthy and impaired users. This wearable system equips the user with a controllable additional limb that is capable of complex three-dimensional motion in space. Similar to an elephant trunk, the SPL is able to manipulate objects using a variety of end effectors, such as suction adhesion or a soft grasper, as well as its entire soft body to conform around an object, able to lift 2.35 times its own weight. To develop these highly articulated soft robotic limbs, we provide a novel set of systematic design rules, obtained through varying geometrical parameters of the SPL through experimentally verified finite element method models. We investigate performance of the limb by testing the lifetime of the new SPL actuators, evaluating its payload capacity, operational workspace, and capability of interacting close to a user through a spatial mobility test. Furthermore, we are able to demonstrate limb controllability through multiple user-intent detection modalities. Finally, we explore the limb's ability to assist in multitasking and pick and place scenarios with varying mounting locations of the SPL around the user's body. Our results highlight the SPL's ability to safely interact with the user while demonstrating promising performance in assisting with a wide variety of tasks, in both work and general living settings.

Toward wearable supernumerary robotic fingers to compensate missing grasping abilities in hemiparetic upper limb

This paper presents the design, analysis, fabrication, experimental characterization, and evaluation of two prototypes of robotic extra fingers that can be used as grasp compensatory devices for a hemiparetic upper limb. The devices are the results of experimental sessions with chronic stroke patients and consultations with clinical experts. Both devices share a common principle of work, which consists in opposing the device to the paretic hand or wrist so to restrain the motion of an object. They can be used by chronic stroke patients to compensate for grasping in several activities of daily living (ADLs) with a particular focus on bimanual tasks. The robotic extra fingers are designed to be extremely portable and wearable. They can be wrapped as bracelets when not being used, to further reduce the encumbrance. Both devices are intrinsically compliant and driven by a single actuator through a tendon system. The motion of the robotic devices can be controlled using an electromyography-based interface embedded in a cap. The interface allows the user to control the device motion by contracting the frontalis muscle. The performance characteristics of the devices have been measured experimentally and the shape adaptability has been confirmed by grasping various objects with different shapes. We tested the devices through qualitative experiments based on ADLs involving five chronic stroke patients. The prototypes successfully enabled the patients to complete various bimanual tasks. Results show that the proposed robotic devices improve the autonomy of patients in ADLs and allow them to complete tasks that were previously impossible to perform.