Kinematic Synergy of Multi-DoF Movement in Upper Limb and Its Application for Rehabilitation Exoskeleton Motion Planning

Long Short-Term Memory-Enabled Electromyography-Controlled Adaptive Wearable Robotic Exoskeleton for Upper Arm Rehabilitation

Restoring strength, function, and mobility following an illness, accident, or surgery is the primary goal of upper arm rehabilitation. Exoskeletons offer adaptable support, enhancing patient engagement and accelerating recovery. This work proposes an adjustable, wearable robotic exoskeleton powered by electromyography (EMG) data for upper arm rehabilitation. Three activation levels-low, medium, and high-were applied to the EMG data to forecast the Pulse Width Modulation (PWM) based on the range of motion (ROM) angle. Conventional machine learning (ML) models, including K-Nearest Neighbor Regression (K-NNR), Support Vector Regression (SVR), and Random Forest Regression (RFR), were compared with neural network approaches, including Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) to determine the best ML model for the ROM angle prediction. The LSTM model emerged as the best predictor with a high accuracy of 0.96. The system achieved 0.89 accuracy in exoskeleton control and 0.85 accuracy in signal categorization. Additionally, the proposed exoskeleton demonstrated a 0.97 performance in ROM correction compared to conventional methods (p = 0.097). These findings highlight the potential of EMG-based, LSTM-enabled exoskeleton systems to deliver accurate and adaptive upper arm rehabilitation, particularly for senior citizens, by providing personalized and effective support.

Spinal maps in phasic and tonic EMG: Investigating intra-subject and inter-subject variability

Reaching movements are essential for daily tasks and they have been widely investigated through kinematic, kinetic, and electromyographic (EMG) analyses. Recent studies have suggested that the central nervous system simplifies control of reaching movements by using muscle synergies. An alternative approach is to investigate how EMG activity reflects at theneural level with the representation of spinal maps that visualize the spatiotemporal activity of motoneuronal pools. Spinal maps have been rarely used and their investigation could be made by exploiting recent findings in EMG processing such as the separation of phasic (motion-related) and tonic components (anti-gravity). In this study, we aimed at characterizing spinal maps in the upper limb workspace. EMG data from 15 participants were recorded during repeated point-to-point movements toward target boards placed in five orientations. EMG waveforms were divided into total EMG envelope, tonic EMG, and phasic EMG. The multidimensional Pearson's correlation coefficient was used to assess thesimilarity of spinal maps among repetitions of movements within subjects (intra-subject variability) and among participants (inter-subject variability). Spinal maps of tonic and total EMG showed high intra- and inter-subject similarity in all planes, while phasic spinal maps were less repeatable and more subject-specific. These results may be useful as areference for rehabilitation, clinical, and neurological evaluations, especially for longitudinal assessments.

Comparison of inter-joint coordination strategies during activities of daily living with prosthetic and anatomical limbs

While healthy individuals have redundant degrees of freedom of the joints, they coordinate their multi-joint movements such that the redundancy is effectively reduced. Achieving high inter-joint coordination may be difficult for upper limb prosthesis users due to the lack of proprioceptive feedback and limited motion of the terminal device. This study compared inter-joint coordination between prosthesis users and individuals without limb loss during different upper limb activities of daily living (ADLs). Nine unilateral prosthesis users (five males) and nine age- and sex-matched controls without limb loss completed three unilateral and three bilateral ADLs. Principal component analysis was applied to the three-dimensional motion trajectories of the trunk and arms to identify coordinative patterns. For each ADL, we quantified the cumulative variance accounted for (VAF) of the first five principal components (pcs), which was the lowest number of pcs that could achieve 90% VAF in control limb movements across all ADLs (5 ≤ n ≤ 9). The VAF was lower for movements involving a prosthesis compared to those completed by controls across all ADLs (p < 0.001). The pc waveforms were similar between movements involving a prosthesis and movements completed by control participants for pc1 (r > 0.78, p < 0.001). The magnitude of the relationship for pc2 and pc3 differed between ADLs, with the strongest correlation for symmetric bilateral ADLs (0.67 ≤ r ≤ 0.97, p < 0.001). Collectively, this study demonstrates that activities of daily living were completed with distinct coordination strategies in prosthesis users compared to individuals without limb loss. Future work should explore how device features, such as the availability of sensory feedback or motorized wrist joints influence multi-joint coordination.

Kinematic synergies show good consistency when extracted with a low-cost markerless device and a marker-based motion tracking system

Recently, markerless tracking systems, such as RGB-Depth cameras, have spread to overcome some of the limitations of the gold standard marker-based tracking systems. Although these systems are valuable substitutes for human motion analysis, as they guarantee higher flexibility, faster setup time and lower costs, their tracking accuracy is lower with respect to marker-based systems. Many studies quantified the error made by markerless systems in terms of body segment length estimation, articular angles, and biomechanics, concluding that they are appropriate for many clinical applications related to motion analysis. We propose an innovative approach to compare a markerless tracking system (Kinect V2) with a gold standard marker-based system (Vicon), based on motor control assessment. We quantified kinematic synergies from the tracking data of fifteen participants performing multi-directional upper limb movements. Kinematic synergy analysis decomposes the kinematic data into a reduced set of motor primitives that describe how the central nervous system coordinates motion at spatial and temporal level. Synergies were extracted with the recently released mixed-matrix factorization algorithm. Four synergies were extracted from both marker-based and markerless datasets and synergies were grouped in 6 clusters for each dataset. Cosine similarity in each cluster was ⩾0.60 in both systems, showing good consistency of synergies. Good matching was found between synergies extracted from markerless and from marker-based data, with a cosine similarity between matched synergies ⩾0.60 in 5 out 6 synergies. These results showed that the markerless sensor can be feasible for kinematic synergy analysis for gross movements, as it correctly estimates the number of synergies and in most cases also their spatial and temporal organization.

Anthropomorphic motion planning for multi-degree-of-freedom arms

With the development of technology, the humanoid robot is no longer a concept, but a practical partner with the potential to assist people in industry, healthcare and other daily scenarios. The basis for the success of humanoid robots is not only their appearance, but more importantly their anthropomorphic behaviors, which is crucial for the human-robot interaction. Conventionally, robots are designed to follow meticulously calculated and planned trajectories, which typically rely on predefined algorithms and models, resulting in the inadaptability to unknown environments. Especially when faced with the increasing demand for personalized and customized services, predefined motion planning cannot be adapted in time to adapt to personal behavior. To solve this problem, anthropomorphic motion planning has become the focus of recent research with advances in biomechanics, neurophysiology, and exercise physiology which deepened the understanding of the body for generating and controlling movement. However, there is still no consensus on the criteria by which anthropomorphic motion is accurately generated and how to generate anthropomorphic motion. Although there are articles that provide an overview of anthropomorphic motion planning such as sampling-based, optimization-based, mimicry-based, and other methods, these methods differ only in the nature of the planning algorithms and have not yet been systematically discussed in terms of the basis for extracting upper limb motion characteristics. To better address the problem of anthropomorphic motion planning, the key milestones and most recent literature have been collated and summarized, and three crucial topics are proposed to achieve anthropomorphic motion, which are motion redundancy, motion variation, and motion coordination. The three characteristics are interrelated and interdependent, posing the challenge for anthropomorphic motion planning system. To provide some insights for the research on anthropomorphic motion planning, and improve the anthropomorphic motion ability, this article proposes a new taxonomy based on physiology, and a more complete system of anthropomorphic motion planning by providing a detailed overview of the existing methods and their contributions.

Arm muscle synergies enhance hand posture prediction in combination with forearm muscle synergies

Objective.We analyze and interpret arm and forearm muscle activity in relation with the kinematics of hand pre-shaping during reaching and grasping from the perspective of human synergistic motor control.Approach.Ten subjects performed six tasks involving reaching, grasping and object manipulation. We recorded electromyographic (EMG) signals from arm and forearm muscles with a mix of bipolar electrodes and high-density grids of electrodes. Motion capture was concurrently recorded to estimate hand kinematics. Muscle synergies were extracted separately for arm and forearm muscles, and postural synergies were extracted from hand joint angles. We assessed whether activation coefficients of postural synergies positively correlate with and can be regressed from activation coefficients of muscle synergies. Each type of synergies was clustered across subjects.Main results.We found consistency of the identified synergies across subjects, and we functionally evaluated synergy clusters computed across subjects to identify synergies representative of all subjects. We found a positive correlation between pairs of activation coefficients of muscle and postural synergies with important functional implications. We demonstrated a significant positive contribution in the combination between arm and forearm muscle synergies in estimating hand postural synergies with respect to estimation based on muscle synergies of only one body segment, either arm or forearm (p< 0.01). We found that dimensionality reduction of multi-muscle EMG root mean square (RMS) signals did not significantly affect hand posture estimation, as demonstrated by comparable results with regression of hand angles from EMG RMS signals.Significance.We demonstrated that hand posture prediction improves by combining activity of arm and forearm muscles and we evaluate, for the first time, correlation and regression between activation coefficients of arm muscle and hand postural synergies. Our findings can be beneficial for myoelectric control of hand prosthesis and upper-limb exoskeletons, and for biomarker evaluation during neurorehabilitation.

Intra-limb joint coordination measures of upper limb and hand movements: A systematic review

BACKGROUND

People with central nervous system disorders typically have difficulties in coordination of the upper limb and hand movements, which significantly impairs their activities of daily living. Laboratory-based measures can provide quantitative and objective information about intra-limb coordination to aid the rehabilitation process of this population. However, there is currently no comprehensive review of laboratory-based measures.

RESEARCH QUESTIONS

The aim of this review was to identify and summarize laboratory-based intra-limb coordination measures for different upper limb and hand movements.

METHODS

Searches were performed in the CINAHL, Embase, IEEE Xplore, MEDLINE, PubMed and Web of Science databases to identify studies published between 2013 and 2022. Two authors independently performed paper selection, data extraction and quality assessment.

RESULTS

21 papers were identified, and six types of coordination measures were classified. These included principal component analysis, continuous relative phase analysis, correlation analysis, regression analysis, uncontrolled manifold analysis, and uncorrelated surrogate data analysis, in descending order of occurrence. Regarding psychometric properties, all measures demonstrated good discriminative validity. However, only the principal component analysis approach and the continuous relative phase analysis approach were found to have good convergent validity and responsiveness, respectively. In terms of their practicality, these measures were primarily utilized for quantifying coordination in individuals with neurological disorders, with a greater emphasis on the coordination of upper limb movements rather than hand movements.

SIGNIFICANCE

This review summarized and critiqued the characteristics of six types of joint coordination measures. Researchers and clinicians should therefore select appropriate measures based on individual needs. Future research should continue on analysing coordination in individuals with pathological conditions and exploring the application of these measures in quantifying hand movement coordination, to advance current knowledge and inform rehabilitation practices.

Home-Based Rehabilitation of the Shoulder Using Auxiliary Systems and Artificial Intelligence: An Overview

Advancements in modern medicine have bolstered the usage of home-based rehabilitation services for patients, particularly those recovering from diseases or conditions that necessitate a structured rehabilitation process. Understanding the technological factors that can influence the efficacy of home-based rehabilitation is crucial for optimizing patient outcomes. As technologies continue to evolve rapidly, it is imperative to document the current state of the art and elucidate the key features of the hardware and software employed in these rehabilitation systems. This narrative review aims to provide a summary of the modern technological trends and advancements in home-based shoulder rehabilitation scenarios. It specifically focuses on wearable devices, robots, exoskeletons, machine learning, virtual and augmented reality, and serious games. Through an in-depth analysis of existing literature and research, this review presents the state of the art in home-based rehabilitation systems, highlighting their strengths and limitations. Furthermore, this review proposes hypotheses and potential directions for future upgrades and enhancements in these technologies. By exploring the integration of these technologies into home-based rehabilitation, this review aims to shed light on the current landscape and offer insights into the future possibilities for improving patient outcomes and optimizing the effectiveness of home-based rehabilitation programs.

Kinematic coordinations capture learning during human-exoskeleton interaction

Human-exoskeleton interactions have the potential to bring about changes in human behavior for physical rehabilitation or skill augmentation. Despite significant advances in the design and control of these robots, their application to human training remains limited. The key obstacles to the design of such training paradigms are the prediction of human-exoskeleton interaction effects and the selection of interaction control to affect human behavior. In this article, we present a method to elucidate behavioral changes in the human-exoskeleton system and identify expert behaviors correlated with a task goal. Specifically, we observe the joint coordinations of the robot, also referred to as kinematic coordination behaviors, that emerge from human-exoskeleton interaction during learning. We demonstrate the use of kinematic coordination behaviors with two task domains through a set of three human-subject studies. We find that participants (1) learn novel tasks within the exoskeleton environment, (2) demonstrate similarity of coordination during successful movements within participants, (3) learn to leverage these coordination behaviors to maximize success within participants, and (4) tend to converge to similar coordinations for a given task strategy across participants. At a high level, we identify task-specific joint coordinations that are used by different experts for a given task goal. These coordinations can be quantified by observing experts and the similarity to these coordinations can act as a measure of learning over the course of training for novices. The observed expert coordinations may further be used in the design of adaptive robot interactions aimed at teaching a participant the expert behaviors.

Technology Assisted Rehabilitation Patient Perception Questionnaire (TARPP-Q): development and implementation of an instrument to evaluate patients' perception during training

BACKGROUND

The introduction of technology-assisted rehabilitation (TAR) uncovers promising challenges for the treatment of motor disorders, particularly if combined with exergaming. Patients with neurological diseases have proved to benefit from TAR, improving their performance in several activities. However, the subjective perception of the device has never been fully addressed, being a conditioning factor for its use. The aims of the study were: (a) to develop a questionnaire on patients' personal experience with TAR and exergames in a real-world clinical setting; (b) to administer the questionnaire to a pilot group of neurologic patients to assess its feasibility and statistical properties.

METHODS

A self-administrable and close-ended questionnaire, Technology Assisted Rehabilitation Patient Perception Questionnaire (TARPP-Q), designed by a multidisciplinary team, was developed in Italian through a Delphi procedure. An English translation has been developed with consensus, for understandability purposes. The ultimate version of the questionnaire was constituted of 10 questions (5 with multiple answers), totalling 29 items, exploring the patient's performance and personal experience with TAR with Augmented Performance Feedback. TARPP-Q was then administered pre-post training in an observational, feasible, multi-centric study. The study involved in-patients aged between 18 and 85 with neurological diseases, admitted for rehabilitation with TAR (upper limb or gait). FIM scale was run to control functional performance.

RESULTS

Forty-four patients were included in the study. All patients answered the TARPP-Q autonomously. There were no unaccounted answers. Exploratory factor analyses identified 4 factors: Positive attitude, Usability, Hindrance perception, and Distress. Internal consistency was measured at T0. The values of Cronbach's alpha ranged from 0.72 (Distress) to 0.92 (Positive attitude). Functional Independence Measure (FIM®) scores and all TARPP-Q factors (Positive attitude, Usability, Hindrance perception, except for Distress (p = 0.11), significantly improved at the end of the treatment. A significant positive correlation between Positive attitude and Usability was also recorded.

CONCLUSIONS

The TARPP-Q highlights the importance of patients' personal experience with TAR and exergaming. Large-scale applications of this questionnaire may clarify the role of patients' perception of training effectiveness, helping to customize devices and interventions.

The Measure of Motion Similarity for Robotics Application

A new measure of motion similarity has been proposed. The formulation of this measure is presented and its logical basis is described. Unlike in most of other methods, the measure enables easy determination of the instantaneous synergies of the motion of body parts. To demonstrate how to use the measure, the data describing human movement is used. The movement is recorded using a professional motion capture system. Two different cases of non-periodic movements are discussed: stepping forward and backward, and returning to a stable posture after an unexpected thrust to the side (hands free or tied). This choice enables the identification of synergies in slow dynamics (stepping) and in fast dynamics (push recovery). The trajectories of motion similarity measures are obtained for point masses of the human body. The interpretation of these trajectories in relation to motion events is discussed. In addition, ordinary motion trajectories and footprints are shown in order to better illustrate the specificity of the discussed examples. The article ends with a discussion and conclusions.

Impacts of Motor Developmental Delay on the Inter-Joint Coordination Using Kinematic Synergies of Joint Angles During Infant Crawling

Motor developmental delay (MDD) usually affects the inter-joint coordination for limb movement. However, the mechanism between the abnormal inter-joint coordination and MDD is still unclear, which poses a challenge for clinical diagnosis and motor rehabilitation of MDD in infant's early life. This study aimed to explore whether the joint activities of limbs during infant crawling are represented with kinematic synergies of joint angles, and evaluate the impacts of MDD on the inter-joint coordination using those synergies. 20 typically developing infants, 16 infants at risk of developmental delay, 11 infants at high risk of developmental delay and 13 infants with confirmed developmental delay were recruited for self-paced crawling on hands and knees. A motion capture system was employed to trace infants' limbs in space, and angles of shoulder, elbow, hip and knee over time were computed. Kinematic synergies were derived from joint angles using principal component analysis. Sample entropy and Spearman's rank correlation coefficients were calculated among those synergies to evaluate the crawling complexity and the symmetry of bilateral limbs, respectively. We found that the first two synergies with different contributions to the crawling movements sufficiently represented the joint angular profiles of limbs. MDD further delayed the development of motor function for lower limbs and mainly increased the crawling complexity of joint flexion/extension to some extent, but did not obviously change the symmetry of bilateral limbs. These results suggest that the time-varying kinematic synergy of joint angles is a potential index for objectively evaluating the abnormal inter-joint coordination affected by MDD.

Restoration of complex movement in the paralyzed upper limb

OBJECTIVE

Functional electrical stimulation (FES) involves artificial activation of skeletal muscles to reinstate motor function in paralyzed individuals. While FES applied to the upper limb has improved the ability of tetraplegics to perform activities of daily living, there are key shortcomings impeding its widespread use. One major limitation is that the range of motor behaviors that can be generated is restricted to a small set of simple, preprogrammed movements. This limitation stems from the substantial difficulty in determining the patterns of stimulation across many muscles required to produce more complex movements. Therefore, the objective of this study was to use machine learning to flexibly identify patterns of muscle stimulation needed to evoke a wide array of multi-joint arm movements.

APPROACH

Arm kinematics and electromyographic activity from 29 muscles were recorded while a 'trainer' monkey made an extensive range of arm movements. Those data were used to train an artificial neural network that predicted patterns of muscle activity associated with a new set of movements. Those patterns were converted into trains of stimulus pulses that were delivered to upper limb muscles in two other temporarily paralyzed monkeys.

RESULTS

Machine-learning based prediction of EMG was good for within-subject predictions but appreciably poorer for across-subject predictions. Evoked responses matched the desired movements with good fidelity only in some cases. Means to mitigate errors associated with FES-evoked movements are discussed.

SIGNIFICANCE

Because the range of movements that can be produced with our approach is virtually unlimited, this system could greatly expand the repertoire of movements available to individuals with high level paralysis.

Angular Velocity Profiles of Upper Limb Joint Synergies in Reaching Movements: a pilot study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:6420-6423. [PMID: 34892581 DOI: 10.1109/embc46164.2021.9630305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

The spatiotemporal kinematic synergy, a coupling of multiple degrees of freedom (DoF), runs through human activities of daily living (ADL). And it is an entry point for exploring the central nervous system's (CNS) control process of musculoskeletal system by analyzing the time-varying kinematic synergy. The aim of this study was to find more physiological properties from the angular velocity profiles of synergy. Ten healthy right-handed subjects were asked to reach target button at different locations. During reaching movement, the motion data of five right upper limb joints were recorded, and the synergistic patterns were extracted by PCA algorithm. Our results showed that the combinations of the first four synergies were sufficient to explain raw data. As far as possible to exclude the effects of individual and information differences, we found shoulder flexion/extension and elbow flexion/extension made distinct contribution in a period of time to the control procedure performed by CNS after targets were confirmed. Our preliminary results implied that reaching movements required comparatively constant scheduling of shoulder horizontal abduction/adduction, shoulder internal/external rotation and wrist ulnar/radial deviation by CNS, while scheduling of SFE and EFE depends on the objectives.Clinical relevance- The findings of this paper may provide a novel dynamic control evidence based on CNS for realizing near-natural control of assistive devices in motor rehabilitation area.

Path Planning and Impedance Control of a Soft Modular Exoskeleton for Coordinated Upper Limb Rehabilitation

The coordinated rehabilitation of the upper limb is important for the recovery of the daily living abilities of stroke patients. However, the guidance of the joint coordination model is generally lacking in the current robot-assisted rehabilitation. Modular robots with soft joints can assist patients to perform coordinated training with safety and compliance. In this study, a novel coordinated path planning and impedance control method is proposed for the modular exoskeleton elbow-wrist rehabilitation robot driven by pneumatic artificial muscles (PAMs). A convolutional neural network-long short-term memory (CNN-LSTM) model is established to describe the coordination relationship of the upper limb joints, so as to generate adaptive trajectories conformed to the coordination laws. Guided by the planned trajectory, an impedance adjustment strategy is proposed to realize active training within a virtual coordinated tunnel to achieve the robot-assisted upper limb coordinated training. The experimental results showed that the CNN-LSTM hybrid neural network can effectively quantify the coordinated relationship between the upper limb joints, and the impedance control method ensures that the robotic assistance path is always in the virtual coordination tunnel, which can improve the movement coordination of the patient and enhance the rehabilitation effectiveness.

Portable, open-source solutions for estimating wrist position during reaching in people with stroke

Arm movement kinematics may provide a more sensitive way to assess neurorehabilitation outcomes than existing metrics. However, measuring arm kinematics in people with stroke can be challenging for traditional optical tracking systems due to non-ideal environments, expense, and difficulty performing required calibration. Here, we present two open-source methods, one using inertial measurement units (IMUs) and another using virtual reality (Vive) sensors, for accurate measurements of wrist position with respect to the shoulder during reaching movements in people with stroke. We assessed the accuracy of each method during a 3D reaching task. We also demonstrated each method's ability to track two metrics derived from kinematics-sweep area and smoothness-in people with chronic stroke. We computed correlation coefficients between the kinematics estimated by each method when appropriate. Compared to a traditional optical tracking system, both methods accurately tracked the wrist during reaching, with mean signed errors of 0.09 ± 1.81 cm and 0.48 ± 1.58 cm for the IMUs and Vive, respectively. Furthermore, both methods' estimated kinematics were highly correlated with each other (p < 0.01). By using relatively inexpensive wearable sensors, these methods may be useful for developing kinematic metrics to evaluate stroke rehabilitation outcomes in both laboratory and clinical environments.

Intra-Subject and Inter-Subject Movement Variability Quantified with Muscle Synergies in Upper-Limb Reaching Movements

Quantifying movement variability is a crucial aspect for clinical and laboratory investigations in several contexts. However, very few studies have assessed, in detail, the intra-subject variability across movements and the inter-subject variability. Muscle synergies are a valuable method that can be used to assess such variability. In this study, we assess, in detail, intra-subject and inter-subject variability in a scenario based on a comprehensive dataset, including multiple repetitions of multi-directional reaching movements. The results show that muscle synergies are a valuable tool for quantifying variability at the muscle level and reveal that intra-subject variability is lower than inter-subject variability in synergy modules and related temporal coefficients, and both intra-subject and inter-subject similarity are higher than random synergy matching, confirming shared underlying control structures. The study deepens the available knowledge on muscle synergy-based motor function assessment and rehabilitation applications, discussing their applicability to real scenarios.

Preliminary Assessment of a Postural Synergy-Based Exoskeleton for Post-Stroke Upper Limb Rehabilitation

Upper limb exoskeletons have drawn significant attention in neurorehabilitation because of the anthropomorphic mechanical structure analogous to human anatomy. Whereas, the training movements are typically unorganized because most exoskeletons ignore the natural movement characteristic of human upper limbs, particularly inter-joint postural synergy. This paper introduces a newly developed exoskeleton (Armule) for upper limb rehabilitation with a postural synergy design concept, which can reproduce activities of daily living (ADL) motion with the characteristics of human natural movements. The semitransparent active control strategy with the interactive force guidance and visual feedback ensured the active participation of users. Eight participants with hemiplegia due to a first-ever, unilateral stroke were recruited and included. They participated in exoskeleton therapy sessions for 4 weeks, with passive/active training under trajectories and postures with the characteristics of human natural movements. The primary outcome was the Fugl-Meyer Assessment for Upper Extremities (FMA-UE). The secondary outcomes were the Action Research Arm Test(ARAT), modified Barthel Index (mBI), and metric measured with the exoskeleton After the 4-weeks intervention, all subjects showed significant improvements in the following clinical measures: the FMA-UE (difference, 11.50 points, p = 0.002), the ARAT (difference, 7.75 points ), and the mBI (difference, 17.50 points, p = 0.003 ) score. Besides, all subjects showed significant improvements in kinematic and interaction force metrics measured with the exoskeleton. These preliminary results demonstrate that the Armule exoskeleton could improve individuals' motor control and ADL function after stroke, which might be associated with kinematic and interaction force optimization and postural synergy modification during functional tasks.

[Mirror-type rehabilitation training with dynamic adjustment and assistance for shoulder joint]

The real physical image of the affected limb, which is difficult to move in the traditional mirror training, can be realized easily by the rehabilitation robots. During this training, the affected limb is often in a passive state. However, with the gradual recovery of the movement ability, active mirror training becomes a better choice. Consequently, this paper took the self-developed shoulder joint rehabilitation robot with an adjustable structure as an experimental platform, and proposed a mirror training system completed by next four parts. First, the motion trajectory of the healthy limb was obtained by the Inertial Measurement Units (IMU). Then the variable universe fuzzy adaptive proportion differentiation (PD) control was adopted for inner loop, meanwhile, the muscle strength of the affected limb was estimated by the surface electromyography (sEMG). The compensation force for an assisted limb of outer loop was calculated. According to the experimental results, the control system can provide real-time assistance compensation according to the recovery of the affected limb, fully exert the training initiative of the affected limb, and make the affected limb achieve better rehabilitation training effect.

Motion Planning of Upper-Limb Exoskeleton Robots: A Review

(1) Background: Motion planning is an important part of exoskeleton control that improves the wearer’s safety and comfort. However, its usage introduces the problem of trajectory planning. The objective of trajectory planning is to generate the reference input for the motion-control system. This review explores the methods of trajectory planning for exoskeleton control. In order to reduce the number of surveyed papers, this review focuses on the upper limbs, which require refined three-dimensional motion planning. (2) Methods: A systematic search covering the last 20 years was conducted in Ei Compendex, Inspect-IET, Web of Science, PubMed, ProQuest, and Science-Direct. The search strategy was to use and combine terms “trajectory planning”, “upper limb”, and ”exoskeleton” as high-level keywords. “Trajectory planning” and “motion planning” were also combined with the following keywords: “rehabilitation”, “humanlike motion“, “upper extremity“, “inverse kinematic“, and “learning machine “. (3) Results: A total of 67 relevant papers were discovered. Results were then classified into two main categories of methods to plan trajectory: (i) Approaches based on Cartesian motion planning, and inverse kinematics using polynomial-interpolation or optimization-based methods such as minimum-jerk, minimum-torque-change, and inertia-like models; and (ii) approaches based on “learning by demonstration” using machine-learning techniques such as supervised learning based on neural networks, and learning methods based on hidden Markov models, Gaussian mixture models, and dynamic motion primitives. (4) Conclusions: Various methods have been proposed to plan the trajectories for upper-limb exoskeleton robots, but most of them plan the trajectory offline. The review approach is general and could be extended to lower limbs. Trajectory planning has the advantage of extending the applicability of therapy robots to home usage (assistive exoskeletons); it also makes it possible to mitigate the shortages of medical caregivers and therapists, and therapy costs. In this paper, we also discuss challenges associated with trajectory planning: kinematic redundancy and incompatibility, and the trajectory-optimization problem. Commonly, methods based on the computation of swivel angles and other methods rely on the relationship (e.g., coordinated or synergistic) between the degrees of freedom used to resolve kinematic redundancy for exoskeletons. Moreover, two general solutions, namely, the self-tracing configuration of the joint axis and the alignment-free configuration of the joint axis, which add the appropriate number of extra degrees of freedom to the mechanism, were employed to improve the kinematic incompatibility between human and exoskeleton. Future work will focus on online trajectory planning and optimal control. This will be done because very few online methods were found in the scope of this study.

Effect of Motor Development Levels on Kinematic Synergies During Two-Hand Catching in Children

The ability to coordinate different body parts under different constraints that are imposed by organism, environment, and tasks during motor development might be different in children. The aim of this study was to examine whether children with different motor development levels are different with regard to multijoint coordination during two-hand catching. Eighty-four children (age: 6.05 ±0.67 years) who were assessed on object control skills were recruited voluntarily. The biomechanical model was defined from 20 movements of seven segments (shoulders, elbows, wrists, and torso), and the principal component analysis was used to quantify the multijoint coordination and kinematic synergies during catching. The results showed that the redundancy of joints in two-hand catching is controlled by three kinematic synergies that defined the majority of the variance. The participants who were grouped based on their development levels did not show differences in the number and strength of synergies; however, they were different in the utilization of the kinematic synergies for successful catching. In conclusion, the number and the strength of the kinematic synergies during two-hand catching are not affected by the developmental levels and are related to the nature of the task.

Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients

All people experience aging, and the related physical and health changes, including changes in memory and brain function. These changes may become debilitating leading to an increase in dependence as people get older. Many external aids and tools have been developed to allow older adults and elderly patients to continue to live normal and comfortable lives. This mini-review describes some of the recent studies on cognitive decline and motor control impairment with the goal of advancing non-invasive brain computer interface (BCI) technologies to improve health and wellness of older adults and elderly patients. First, we describe the state of the art in cognitive prosthetics for psychiatric diseases. Then, we describe the state of the art of possible assistive BCI applications for controlling an exoskeleton, a wheelchair and smart home for elderly people with motor control impairments. The basic age-related brain and body changes, the effects of age on cognitive and motor abilities, and several BCI paradigms with typical tasks and outcomes are thoroughly described. We also discuss likely future trends and technologies to assist healthy older adults and elderly patients using innovative BCI applications with minimal technical oversight.