Hybrid brain/neural interface and autonomous vision-guided whole-arm exoskeleton control to perform activities of daily living (ADLs).

BACKGROUND: The aging of the population and the progressive increase of life expectancy in developed countries is leading to a high incidence of age-related cerebrovascular diseases, which affect people's motor and cognitive capabilities and might result in the loss of arm and hand functions. Such conditions have a detrimental impact on people's quality of life. Assistive robots have been developed to help people with motor or cognitive disabilities to perform activities of daily living (ADLs) independently. Most of the robotic systems for assisting on ADLs proposed in the state of the art are mainly external manipulators and exoskeletal devices. The main objective of this study is to compare the performance of an hybrid EEG/EOG interface to perform ADLs when the user is controlling an exoskeleton rather than using an external manipulator. METHODS: Ten impaired participants (5 males and 5 females, mean age 52 ± 16 years) were instructed to use both systems to perform a drinking task and a pouring task comprising multiple subtasks. For each device, two modes of operation were studied: synchronous mode (the user received a visual cue indicating the sub-tasks to be performed at each time) and asynchronous mode (the user started and finished each of the sub-tasks independently). Fluent control was assumed when the time for successful initializations ranged below 3 s and a reliable control in case it remained below 5 s. NASA-TLX questionnaire was used to evaluate the task workload. For the trials involving the use of the exoskeleton, a custom Likert-Scale questionnaire was used to evaluate the user's experience in terms of perceived comfort, safety, and reliability. RESULTS: All participants were able to control both systems fluently and reliably. However, results suggest better performances of the exoskeleton over the external manipulator (75% successful initializations remain below 3 s in case of the exoskeleton and bellow 5s in case of the external manipulator). CONCLUSIONS: Although the results of our study in terms of fluency and reliability of EEG control suggest better performances of the exoskeleton over the external manipulator, such results cannot be considered conclusive, due to the heterogeneity of the population under test and the relatively limited number of participants.

A Classification Method for Workers' Physical Risk.

In Industry 4.0 scenarios, wearable sensing allows the development of monitoring solutions for workers' risk prevention. Current approaches aim to identify the presence of a risky event, such as falls, when it has already occurred. However, there is a need to develop methods capable of identifying the presence of a risk condition in order to prevent the occurrence of the damage itself. The measurement of vital and non-vital physiological parameters enables the worker's complex state estimation to identify risk conditions preventing falls, slips and fainting, as a result of physical overexertion and heat stress exposure. This paper aims at investigating classification approaches to identify risk conditions with respect to normal physical activity by exploiting physiological measurements in different conditions of physical exertion and heat stress. Moreover, the role played in the risk identification by specific sensors and features was investigated. The obtained results evidenced that k-Nearest Neighbors is the best performing algorithm in all the experimental conditions exploiting only information coming from cardiorespiratory monitoring (mean accuracy 88.7±7.3% for the model trained with max(HR), std(RR) and std(HR)).

Manipulation Tasks in Hazardous Environments Using a Teleoperated Robot: A Case Study at CERN.

Remote robotic systems are employed in the CERN accelerator complex to perform different tasks, such as the safe handling of cables and their connectors. Without dedicated control, these kinds of actions are difficult and require the operators' intervention, which is subjected to dangerous external agents. In this paper, two novel modules of the CERNTAURO framework are presented to provide a safe and usable solution for managing optical fibres and their connectors. The first module is used to detect touch and slippage, while the second one is used to regulate the grasping force and contrast slippage. The force reference was obtained with a combination of object recognition and a look-up table method. The proposed strategy was validated with tests in the CERN laboratory, and the preliminary experimental results demonstrated statistically significant increases in time-based efficiency and in the overall relative efficiency of the tasks.

Treatment of the Paretic Hand with a Robotic Glove Combined with Physiotherapy in a Patient Suffering from Traumatic Tetraparesis: A Case Report.

BACKGROUND: cervical spinal cord injury leads to loss of upper limb functionality, which causes a decrease in autonomy to perform activities of daily living. The use of robotic technologies in rehabilitation could contribute to improving upper limb functionality and treatment quality. This case report aims to describe the potential of robotic hand treatment with Gloreha Sinfonia, in combination with conventional rehabilitation, in a tetraparetic patient. MATERIAL: fifteen rehabilitative sessions were performed. Evaluations were conducted pre-treatment (T0), post-treatment (T1), and at two-months follow-up (T2) based on: the upper-limb range of motion and force assessment, the FMA-UE, the 9-Hole Peg Test (9HPT), and the DASH questionnaire. A virtual reality game-based rating system was used to evaluate the force control and modulation ability. RESULTS: the patient reported greater ability to use hands with less compensation at T1 and T2 assessments. Improvements in clinical scales were reported in both hands at T1, however, at T2 only did the dominant hand show further improvement. Improved grip strength control and modulation ability were reported for T1. However a worsening was found in both hands at T2, significant only for the non-dominant hand. The maximum force exerted increased from T0 to T2 in both hands. CONCLUSION: hand treatment combining physical therapy and Gloreha Sinfonia seems to have benefits in functionality and dexterity in tetraparetic patient in the short term. Further studies are needed to confirm these findings, to verify long-term results, and to identify the most appropriate modalities of robotic rehabilitation.

A parallel classification strategy to simultaneous control elbow, wrist, and hand movements.

BACKGROUND: In the field of myoelectric control systems, pattern recognition (PR) algorithms have become always more interesting for predicting complex electromyography patterns involving movements with more than 2 Degrees of Freedom (DoFs). The majority of classification strategies, used for the prosthetic control, are based on single, hierarchical and parallel linear discriminant analysis (LDA) classifiers able to discriminate up to 19 wrist/hand gestures (in the 3-DoFs case), considering both combined and discrete motions. However, these strategies were introduced to simultaneously classify only 2 DoFs and their use is limited by the lack of online performance measures. This study introduces a novel classification strategy based on the Logistic Regression (LR) algorithm with regularization parameter to provide simultaneous classification of 3 DoFs motion classes. METHODS: The parallel PR-based strategy was tested on 15 healthy subjects, by using only six surface EMG sensors. Twenty-seven discrete and complex elbow, hand and wrist motions were classified by keeping the number of electromyographic (EMG) electrodes to a bare minimum and the classification error rate under 10 %. To this purpose, the parallel classification strategy was implemented by using three classifiers one for each DoF: the "Elbow classifier", the "Wrist classifier", and the "Hand classifier" provided the simultaneous control of the elbow, hand, and wrist joints, respectively. RESULTS: Both the offline and real-time performance metrics were evaluated and compared with the LDA parallel classification results. The real-time recognition results were statistically better with the LR classifier with respect to the LDA classifier, for all motion classes (elbow, hand and wrist). CONCLUSIONS: In this paper, a novel parallel PR-based strategy was proposed for classifying up to 3 DoFs: three joint classifiers were employed simultaneously for classifying 27 motion classes related to the elbow, wrist, and hand and promising results were obtained.

Hierarchical Human-Inspired Control Strategies for Prosthetic Hands.

The abilities of the human hand have always fascinated people, and many studies have been devoted to describing and understanding a mechanism so perfect and important for human activities. Hand loss can significantly affect the level of autonomy and the capability of performing the activities of daily life. Although the technological improvements have led to the development of mechanically advanced commercial prostheses, the control strategies are rather simple (proportional or on/off control). The use of these commercial systems is unnatural and not intuitive, and therefore frequently abandoned by amputees. The components of an active prosthetic hand are the mechatronic device, the decoding system of human biological signals into gestures and the control law that translates all the inputs into desired movements. The real challenge is the development of a control law replacing human hand functions. This paper presents a literature review of the control strategies of prosthetics hands with a multiple-layer or hierarchical structure, and points out the main critical aspects of the current solutions, in terms of human's functions replicated with the prosthetic device. The paper finally provides several suggestions for designing a control strategy able to mimic the functions of the human hand.

A Soft Zwitterionic Hydrogel as Potential Coating on a Polyimide Surface to Reduce Foreign Body Reaction to Intraneural Electrodes.

Invasive intraneural electrodes can control advanced neural-interfaced prostheses in human amputees. Nevertheless, in chronic implants, the progressive formation of a fibrotic capsule can gradually isolate the electrode surface from the surrounding tissue leading to loss of functionality. This is due to a nonspecific inflammatory response called foreign-body reaction (FBR). The commonly used poly(ethylene glycol) (PEG)-based low-fouling coatings of implantable devices can be easily encapsulated and are susceptible to oxidative damage in long-term in vivo applications. Recently, sulfobetaine-based zwitterionic hydrogels have emerged as an important class of robust ultra-low fouling biomaterials, holding great potential to mitigate FBR. The aim of this proof-of-principle in vitro work was to assess whether the organic zwitterionic-poly(sulfobetaine methacrylate) [poly(SBMA)]-hydrogel could be a suitable coating for Polyimide (PI)-based intraneural electrodes to reduce FBR. We first synthesized and analyzed the hydrogel through a mechanical characterization (i.e., Young's modulus). Then, we demonstrated reduced adhesion and activation of fibrogenic and pro-inflammatory cells (i.e., human myofibroblasts and macrophages) on the hydrogel compared with PEG-coated and polystyrene surfaces using cell viability assays, confocal fluorescence microscopy and high-content analysis of oxidative stress production. Interestingly, we successfully coated PI surfaces with a thin film of the hydrogel through covalent bond and demonstrated its high hydrophilicity via water contact angle measurement. Importantly, we showed the long-term release of an anti-fibrotic drug (i.e., Everolimus) from the hydrogel. Because of the low stiffness, biocompatibility, high hydration and ultra-low fouling characteristics, our zwitterionic hydrogel could be envisioned as long-term diffusion-based delivery system for slow and controlled anti-inflammatory and anti-fibrotic drug release in vivo.

The WGD-A Dataset of Assembly Line Working Gestures for Ergonomic Analysis and Work-Related Injuries Prevention.

This paper wants to stress the importance of human movement monitoring to prevent musculoskeletal disorders by proposing the WGD-Working Gesture Dataset, a publicly available dataset of assembly line working gestures that aims to be used for worker's kinematic analysis. It contains kinematic data acquired from healthy subjects performing assembly line working activities using an optoelectronic motion capture system. The acquired data were used to extract quantitative indicators to assess how the working tasks were performed and to detect useful information to estimate the exposure to the factors that may contribute to the onset of musculoskeletal disorders. The obtained results demonstrate that the proposed indicators can be exploited to early detect incorrect gestures and postures and, consequently to prevent work-related disorders. The approach is general and independent on the adopted motion analysis system. It wants to provide indications for safely performing working activities. For example, the proposed WGD can also be used to evaluate the kinematics of workers in real working environments thanks to the adoption of unobtrusive measuring systems, such as wearable sensors through the extracted indicators and thresholds.

Control Strategies and Performance Assessment of Upper-Limb TMR Prostheses: A Review.

The evolution of technological and surgical techniques has made it possible to obtain an even more intuitive control of multiple joints using advanced prosthetic systems. Targeted Muscle Reinnervation (TMR) is considered to be an innovative and relevant surgical technique for improving the prosthetic control for people with different amputation levels of the limb. Indeed, TMR surgery makes it possible to obtain reinnervated areas that act as biological amplifiers of the motor control. On the technological side, a great deal of research has been conducted in order to evaluate various types of myoelectric prosthetic control strategies, whether direct control or pattern recognition-based control. In the literature, different control performance metrics, which have been evaluated on TMR subjects, have been introduced, but no accepted reference standard defines the better strategy for evaluating the prosthetic control. Indeed, the presence of several evaluation tests that are based on different metrics makes it difficult the definition of standard guidelines for comprehending the potentiality of the proposed control systems. Additionally, there is a lack of evidence about the comparison of different evaluation approaches or the presence of guidelines on the most suitable test to proceed for a TMR patients case study. Thus, this review aims at identifying these limitations by examining the several studies in the literature on TMR subjects, with different amputation levels, and proposing a standard method for evaluating the control performance metrics.

Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses.

BACKGROUND: The usability of dexterous hand prostheses is still hampered by the lack of natural and effective control strategies. A decoding strategy based on the processing of descending efferent neural signals recorded using peripheral neural interfaces could be a solution to such limitation. Unfortunately, this choice is still restrained by the reduced knowledge of the dynamics of human efferent signals recorded from the nerves and associated to hand movements. FINDINGS: To address this issue, in this work we acquired neural efferent activities from healthy subjects performing hand-related tasks using ultrasound-guided microneurography, a minimally invasive technique, which employs needles, inserted percutaneously, to record from nerve fibers. These signals allowed us to identify neural features correlated with force and velocity of finger movements that were used to decode motor intentions. We developed computational models, which confirmed the potential translatability of these results showing how these neural features hold in absence of feedback and when implantable intrafascicular recording, rather than microneurography, is performed. CONCLUSIONS: Our results are a proof of principle that microneurography could be used as a useful tool to assist the development of more effective hand prostheses.

Physiological Responses During Hybrid BNCI Control of an Upper-Limb Exoskeleton.

When combined with assistive robotic devices, such as wearable robotics, brain/neural-computer interfaces (BNCI) have the potential to restore the capabilities of handicapped people to carry out activities of daily living. To improve applicability of such systems, workload and stress should be reduced to a minimal level. Here, we investigated the user's physiological reactions during the exhaustive use of the interfaces of a hybrid control interface. Eleven BNCI-naive healthy volunteers participated in the experiments. All participants sat in a comfortable chair in front of a desk and wore a whole-arm exoskeleton as well as wearable devices for monitoring physiological, electroencephalographic (EEG) and electrooculographic (EoG) signals. The experimental protocol consisted of three phases: (i) Set-up, calibration and BNCI training; (ii) Familiarization phase; and (iii) Experimental phase during which each subject had to perform EEG and EoG tasks. After completing each task, the NASA-TLX questionnaire and self-assessment manikin (SAM) were completed by the user. We found significant differences (p-value < 0.05) in heart rate variability (HRV) and skin conductance level (SCL) between participants during the use of the two different biosignal modalities (EEG, EoG) of the BNCI. This indicates that EEG control is associated with a higher level of stress (associated with a decrease in HRV) and mental work load (associated with a higher level of SCL) when compared to EoG control. In addition, HRV and SCL modulations correlated with the subject's workload perception and emotional responses assessed through NASA-TLX questionnaires and SAM.

Human arm joints reconstruction algorithm in rehabilitation therapies assisted by end-effector robotic devices.

BACKGROUND: End-effector robots are commonly used in robot-assisted neuro-rehabilitation therapies for upper limbs where the patient's hand can be easily attached to a splint. Nevertheless, they are not able to estimate and control the kinematic configuration of the upper limb during the therapy. However, the Range of Motion (ROM) together with the clinical assessment scales offers a comprehensive assessment to the therapist. Our aim is to present a robust and stable kinematic reconstruction algorithm to accurately measure the upper limb joints using only an accelerometer placed onto the upper arm. METHODS: The proposed algorithm is based on the inverse of the augmented Jaciobian as the algorithm (Papaleo, et al., Med Biol Eng Comput 53(9):815-28, 2015). However, the estimation of the elbow joint location is performed through the computation of the rotation measured by the accelerometer during the arm movement, making the algorithm more robust against shoulder movements. Furthermore, we present a method to compute the initial configuration of the upper limb necessary to start the integration method, a protocol to manually measure the upper arm and forearm lengths, and a shoulder position estimation. An optoelectronic system was used to test the accuracy of the proposed algorithm whilst healthy subjects were performing upper limb movements holding the end effector of the seven Degrees of Freedom (DoF) robot. In addition, the previous and the proposed algorithms were studied during a neuro-rehabilitation therapy assisted by the 'PUPArm' planar robot with three post-stroke patients. RESULTS: The proposed algorithm reports a Root Mean Square Error (RMSE) of 2.13cm in the elbow joint location and 1.89cm in the wrist joint location with high correlation. These errors lead to a RMSE about 3.5 degrees (mean of the seven joints) with high correlation in all the joints with respect to the real upper limb acquired through the optoelectronic system. Then, the estimation of the upper limb joints through both algorithms reveal an instability on the previous when shoulder movement appear due to the inevitable trunk compensation in post-stroke patients. CONCLUSIONS: The proposed algorithm is able to accurately estimate the human upper limb joints during a neuro-rehabilitation therapy assisted by end-effector robots. In addition, the implemented protocol can be followed in a clinical environment without optoelectronic systems using only one accelerometer attached in the upper arm. Thus, the ROM can be perfectly determined and could become an objective assessment parameter for a comprehensive assessment.

A Motion Analysis Protocol for Kinematic Assessment of Poly-Articulated Prosthetic Hands With Cosmetic Gloves.

To provide upper-limb amputees with devices that best fit their needs and to test innovative solutions, it is necessary to quantitatively appraise a device performance with rigorous measurement methods. The aim of this work was to define an optimal motion analysis protocol, suitable for optoelectronic systems, to measure the kinematics of poly-articulated hands even when covered by a cosmetic glove. This is a fundamental aspect, because gloves can decrease device speed and range of motion and, ultimately, patients' acceptance of the artificial limb. In this work, different mathematical models of the joints and marker-sets for motion analysis were conceived. A regression model to choose a reduced marker-set for studying the hand performance with different cosmetic glove models was developed. The proposed approaches for finger motion analysis were experimentally tested on the index finger of the i-Limb, a commercial myoelectric poly-articulated prosthetic hand, but the results can be easily extended to the whole hand and to other poly-articulated prosthetic hands. The methods proposed for the performance analysis of prosthetic hands points out that the cosmetic gloves imply a reduction of the finger flexion/extension (F/E) angles and of the motion velocity. This draws attention to the need for performing independent cyclic tests on commercial products with various cosmetic solutions to better guide component selection.

Transcutaneous Vagus Nerve Stimulation Combined with Robotic Rehabilitation Improves Upper Limb Function after Stroke.

The efficacy of standard rehabilitative therapy for improving upper limb functions after stroke is limited; thus, alternative strategies are needed. Vagus nerve stimulation (VNS) paired with rehabilitation is a promising approach, but the invasiveness of this technique limits its clinical application. Recently, a noninvasive method to stimulate vagus nerve has been developed. The aim of the present study was to explore whether noninvasive VNS combined with robotic rehabilitation can enhance upper limb functionality in chronic stroke. Safety and efficacy of this combination have been assessed within a proof-of-principle, double-blind, semirandomized, sham-controlled trial. Fourteen patients with either ischemic or haemorrhagic chronic stroke were randomized to robot-assisted therapy associated with real or sham VNS, delivered for 10 working days. Efficacy was evaluated by change in upper extremity Fugl-Meyer score. After intervention, there were no adverse events and Fugl-Meyer scores were significantly better in the real group compared to the sham group. Our pilot study confirms that VNS is feasible in stroke patients and can produce a slight clinical improvement in association to robotic rehabilitation. Compared to traditional stimulation, noninvasive VNS seems to be safer and more tolerable. Further studies are needed to confirm the efficacy of this innovative approach.

NLR, MLP, SVM, and LDA: a comparative analysis on EMG data from people with trans-radial amputation.

BACKGROUND: Currently, the typically adopted hand prosthesis surface electromyography (sEMG) control strategies do not provide the users with a natural control feeling and do not exploit all the potential of commercially available multi-fingered hand prostheses. Pattern recognition and machine learning techniques applied to sEMG can be effective for a natural control based on the residual muscles contraction of amputated people corresponding to phantom limb movements. As the researches has reached an advanced grade accuracy, these algorithms have been proved and the embedding is necessary for the realization of prosthetic devices. The aim of this work is to provide engineering tools and indications on how to choose the most suitable classifier, and its specific internal settings for an embedded control of multigrip hand prostheses. METHODS: By means of an innovative statistical analysis, we compare 4 different classifiers: Nonlinear Logistic Regression, Multi-Layer Perceptron, Support Vector Machine and Linear Discriminant Analysis, which was considered as ground truth. Experimental tests have been performed on sEMG data collected from 30 people with trans-radial amputation, in which the algorithms were evaluated for both performance and computational burden, then the statistical analysis has been based on the Wilcoxon Signed-Rank test and statistical significance was considered at p < 0.05. RESULTS: The comparative analysis among NLR, MLP and SVM shows that, for either classification performance and for the number of classification parameters, SVM attains the highest values followed by MLP, and then by NLR. However, using as unique constraint to evaluate the maximum acceptable complexity of each classifier one of the typically available memory of a high performance microcontroller, the comparison pointed out that for people with trans-radial amputation the algorithm that produces the best compromise is NLR closely followed by MLP. This result was also confirmed by the comparison with LDA with time domain features, which provided not significant differences of performance and computational burden between NLR and LDA. CONCLUSIONS: The proposed analysis would provide innovative engineering tools and indications on how to choose the most suitable classifier based on the application and the desired results for prostheses control.

Slippage Detection with Piezoresistive Tactile Sensors.

One of the crucial actions to be performed during a grasping task is to avoid slippage. The human hand can rapidly correct applied forces and prevent a grasped object from falling, thanks to its advanced tactile sensing. The same capability is hard to reproduce in artificial systems, such as robotic or prosthetic hands, where sensory motor coordination for force and slippage control is very limited. In this paper, a novel algorithm for slippage detection is presented. Based on fast, easy-to-perform processing, the proposed algorithm generates an ON/OFF signal relating to the presence/absence of slippage. The method can be applied either on the raw output of a force sensor or to its calibrated force signal, and yields comparable results if applied to both normal or tangential components. A biomimetic fingertip that integrates piezoresistive MEMS sensors was employed for evaluating the method performance. Each sensor had four units, thus providing 16 mono-axial signals for the analysis. A mechatronic platform was used to produce relative movement between the finger and the test surfaces (tactile stimuli). Three surfaces with submillimetric periods were adopted for the method evaluation, and 10 experimental trials were performed per each surface. Results are illustrated in terms of slippage events detection and of latency between the slippage itself and its onset.

Evaluation of Pressure Capacitive Sensors for Application in Grasping and Manipulation Analysis.

The analysis of the human grasping and manipulation capabilities is paramount for investigating human sensory-motor control and developing prosthetic and robotic hands resembling the human ones. A viable solution to perform this analysis is to develop instrumented objects measuring the interaction forces with the hand. In this context, the performance of the sensors embedded in the objects is crucial. This paper focuses on the experimental characterization of a class of capacitive pressure sensors suitable for biomechanical analysis. The analysis was performed in three loading conditions (Distributed load, 9 Tips load, and Wave-shaped load, thanks to three different inter-elements) via a traction/compression testing machine. Sensor assessment was also carried out under human- like grasping condition by placing a silicon material with the same properties of prosthetic cosmetic gloves in between the sensor and the inter-element in order to simulate the human skin. Data show that the input-output relationship of the analyzed, sensor is strongly influenced by both the loading condition (i.e., type of inter-element) and the grasping condition (with or without the silicon material). This needs to be taken into account to avoid significant measurement error. To go over this hurdle, the sensors have to be calibrated under each specific condition in order to apply suitable corrections to the sensor output and significantly improve the measurement accuracy.

Shoulder motor performance assessment in the sagittal plane in children with hemiplegia during single joint pointing tasks.

BACKGROUND: Pointing is a motor task extensively used during daily life activities and it requires complex visuo-motor transformation to select the appropriate movement strategy. The study of invariant characteristics of human movements has led to several theories on how the brain solves the redundancy problem, but the application of these theories on children affected by hemiplegia is limited. This study aims at giving a quantitative assessment of the shoulder motor behaviour in children with hemiplegia during pointing tasks. METHODS: Eight children with hemiplegia were involved in the study and were asked to perform movements on the sagittal plane with both arms, at low and high speed. Subject movements were recorded using an optoelectronic system; a 4-DOF model of children arm has been developed to calculate kinematic and dynamic variables. A set of evaluation indexes has been extracted in order to quantitatively assess whether and how children modify their motor control strategies when perform movements with the more affected or less affected arm. RESULTS: In low speed movements, no differences can be seen in terms of movement duration and peak velocity between the More Affected arm (MA) and the Less Affected arm (LA), as well as in the main characteristics of movement kinematics and dynamics. As regards fast movements, remarkable differences in terms of strategies of motor control can be observed: while movements with LA did not show any significant difference in Dimensionless Jerk Index (JI) and Dimensionless Torque-change Cost index (TC) between the elevation and lowering phases, suggesting that motor control optimization is similar for movements performed with or against gravity, movements with MA showed a statistically significant increase of both JI and TC during lowering phase. CONCLUSIONS: Results suggest the presence of a different control strategy for fast movements in particular during lowering phase. Results suggest that motor control is not able to optimize Jerk and Torque-change cost functions in the same way when controls the two arms, suggesting that children with hemiplegia do not actively control MA lowering fast movements, in order to take advantage of the passive inertial body properties, rather than to attempt its optimal control.

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

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.

A marker-based approach to determine the centers of rotation of finger joints.

BACKGROUND AND OBJECTIVE: The methods proposed in literature to estimate the position of hand joints Centers of Rotation (CoRs) typically require computationally non-trivial optimization routines and exploit a high number of markers to calculate CoRs positions from surface marker trajectories. Moreover, most of the existing works evaluated the accuracy only in simulation. This work proposes a new procedure, based on the Pratt circle fit, to estimate joints CoRs position in 2D through marker-based acquisitions. METHODS: The advantage of the Pratt circle fit lies in its simplicity and computational speed, and in the possibility of exploiting a reduced markerset for calculating CoRs. By applying simplifying assumptions regarding the movement of the fingers (i.e., planar and decoupled flexion-extension movements of each joint occurring in the same flexion plane for all the joints of the finger), it is possible to determine the position of the CoR of each joint in 2D. For this reason, the estimation of the Carpo-MetaCarpal joint of the thumb was not included in this work, as it exhibits a more complex movement associated to the combination of a flexion-extension and adduction-abduction degree of freedom. The errors in estimating CoRs were evaluated by conducting experimental acquisitions on an anthropomorphic robotic hand and comparing the position of the estimated CoR with the real position of the CoR. The repeatability of the method and its capability to estimate anatomically plausible CoRs were evaluated through experimental acquisitions conducted on five healthy volunteers. RESULTS: Errors in estimating finger joints CoRs were in the order of 0.70 mm and 0.18 mm respectively along the finger longitudinal direction (i.e., x coordinate) and thickness (i.e., y coordinate). Standard Deviations of CoRs positions were comparable to the ones obtained in literature (i.e., below 2 mm and 1 mm respectively for the x and y coordinates), thus demonstrating the repeatability of the method. The Anatomical Plausibility Rate of the proposed approach was between 80% and 100%. CONCLUSIONS: The performance of the Pratt-based CoRs estimation procedure proposed in this work was comparable to other existing methods, with the advantage of exploiting a simple fitting algorithm and a reduced markerset with respect to the state-of-the-art techniques.