ACTIVE EXOSKELETONS FOR UPPER-LIMB MOTION ASSIST

It may not an easy task for physically weak elderly, disabled and injured individuals to perform the day to day activities in their life. Therefore, many assistive devices have been developed in order to improve the quality of life of those people in which they may not depend on others. Especially upper-limb power-assist exoskeletons have been developed since the upper limb motions are very important for the daily activities. Electromyography (EMG) signals and/or force sensor based control methods have been identified as the promising methods to control such exoskeleton devices. However, if the user cannot generate sufficient muscle signals or movements, the EMG or force sensor based methods could not be useful to the user. On the other hand, electroencephalography (EEG) signals are also important biological signals to extract the user's motion intention. In this study, the user's elbow joint motion is estimated based on the EEG signals. The measured EEG signals are pre-processed and input to a time-embedded linear model, which is assumed to decode the elbow joint angular velocities. The genetic algorithm (GA) is used to train the model. A six fold cross validation process was performed for each motion segment of each subject. The experimental results suggest that EEG signals with the tested decoding model can be used to continuously decode the elbow joint velocity.

EEG signal is one of the biological signals that can be useful to control wearable robotic devices, according to the human motion intention. However, the real-time estimation of the user's motion intention from EEG signals is cumbersome. The user's motion intention might not be estimated when the user does not concentrate on the control of the robot, distracted by other things or disturbed by the outside interferences. In this paper, a neural network based real-time estimation method is proposed to detect human motion intention in terms of intended task, using EEG signals. The inputs of bandpower time series signals let the neural network identify the dynamic nature of the tasks performed. Experimental details, methodology and the prediction results are presented.

In order to assist the motion in the daily lives of physically weak persons such as elderly persons, many kinds of power-assist robots have been developed. In the case of some physically weak persons, the ability to perceive the environment is sometimes deteriorated also. A method of perception-assist has been proposed to assist not only the user's motion but also the user's interaction with an environment, by applying the modification force to the user's motion if it is necessary. In this paper, the perception-assist for a lower-limb power-assist exoskeleton robot is proposed. In the daily life, the walking is very important for persons to achieve desired tasks. Basically, the robot assists the user's muscle force according to the user's motion intention which is estimated based on EMG signals. If the robot has found problems which might lead the user to dangerous situation such as the falling down, the robot tries to modify the user's motion in addition to the ordinal power-assists to make the user walk properly. Since the user might fall down by the effect of the additional modification force of the perception-assist, the robot automatically prevents the user from falling down by considering ZMP (Zero Moment Point). The effectiveness of the proposed method has been evaluated by performing experiments.

A human motion intent estimation algorithm based on laser ranger finder and force sensors is designed for a walking-aid robot. By applying the proposed algorithm to the robot, the user can operate the walking-aid robot more fluently, comfortably and safely. A walking-aid or rehabilitation robot normally estimates the user's motion intention by measuring the interactive forces between human and robot. Whereas, the human motion should be represented as a coordination between the arms and the legs movements. This study therefore proposes a human motion intent estimation algorithm by fusing the motion intentions detected by the force sensors and the laser ranger finder, which are used to monitor the movements of arms and legs respectively. A special arrangement of push-pull force sensors is implemented for estimating the human motion intention I from user's operations on the handles. Meanwhile the human motion intention II is extracted by a laser ranger finder observing the user's movement of legs. A multi-sensor fusion algorithm is employed to fuse these two estimates. Experimental results are presented to show the validity of the proposed human motion intent estimation algorithm.

As one of the research hotspots in the field of rehabilitation robotics, the upper limb exoskeleton robot has been widely used in the field of rehabilitation. However, the existing methods cannot comprehensively and accurately reflect the motion state of patients, which may lead to overtraining and secondary injury of patients in the process of rehabilitation training. In this paper, an upper limb exoskeleton control method based on mixed perception model of motion intention and intensity is proposed, which is based on the 6 degree-of-freedom upper limb rehabilitation exoskeleton in the laboratory. First, the kinematic information and heart rate information in the rehabilitation process of patients are collected, corresponding to patients’ motion intention and motion intensity, and fused to obtain the mixed perception vector. Second, the motion perception model based on long short-term memory neural network is established to realize the prediction of upper limb motion trajectory of patients and compared with back-propagation neural network to prove its effectiveness. Finally, the control system is built, and both offline and online test of the control method proposed are implemented. The experimental results show that the method can achieve comprehensive motion state perception of patients, realize real-time and accurate prediction trajectory according to human motion intention and intensity. The average prediction accuracy is 95.3%, and predicted joint angle error is less than 5 degrees. Therefore, the control method based on mixed perception model has good robustness and universality, which provides a new method for the active control of upper limb exoskeleton.

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.

Prosthetics, exoskeletons, and rehabilitation devices that seamlessly respond to the user's motion intent are essential for improving the quality of life for individuals with physical disabilities. However, existing motion intent recognition systems based on Electromyography (EMG) often experience significant performance degradation over time and limb positions. To address this, this paper proposed a Hierarchical EMG-FMG Fusion (HEFF) framework that incorporates Force Myography (FMG) as a complementary modality to enhance robustness in long-term and multi-position motion intent recognition. The HEFF framework introduced three new strategies: (1) Complementary Pyramid Fusion, a neural network architecture that effectively integrates the complementary characteristics of EMG and FMG; (2) Position Compensation and Pairing, to increase the generalizability of the model by producing augmented training data mimicking the temporal and positional variability; and (3) Feedback-based Baseline Correction, to dynamically counteract FMG baseline drift. The robustness of the system was evaluated through 10 distinct motions performed in 3 different limb positions, retested after an interval of approximately 7 days. Notably, the training data were only collected in a single limb position on the first day. The real-time experiments with 15 subjects showed that the HEFF framework maintained 91.89% classification accuracy in the primary limb position and 85.39% across multiple unseen positions. According to offline analysis, HEFF improved classification accuracy by up to 34.99% across various limb positions compared to the conventional fusion method. These results highlight HEFF's robustness across various conditions, being well-suited for assistive technology applications.

The objectives of the study are to characterize paretic upper limb (UL) use in people with different levels of impairment 4weeks poststroke and to compare accelerometry and direct observational approaches. Twelve stroke inpatients (five mild, three moderate, and four severe UL impairment) were recruited from a rehabilitation hospital. UL use was measured using accelerometry (24hr) and direct observation (12hr of behavioural mapping). Accelerometry variables included duration of use, use ratio, magnitude ratio, bilateral magnitude, and variation ratio. Direct observation recorded the duration of use and type of UL movement (e.g., functional vs. non-functional). From accelerometry data, people with mild, moderate, and severe UL impairments used their paretic UL 59%, 45%, and 22% of a 24hr-day, respectively. People with severe UL impairment had the lowest paretic UL use duration (median 1.49hr/day), magnitude ratio, and variation ratio compared with people with mild and moderate UL impairment. From 12hr of observational data, people with mild impairment were using their UL for 37.8% of the observed time, whereas the people with moderate and severe impairment were using their UL 15.8% and 4.9%, respectively. UL movements for the mild cohort were mainly functional, whereas UL movements of the moderate and severe cohorts were mainly non-functional. UL movements were predominantly active for the mild and moderate cohorts but passive for the severe cohort. Duration of paretic UL use from accelerometry and observation data were highly correlated (ICC>0.8), but the absolute percentage error between methods ranged from 34.2% to 42.7%. Paretic UL use within the first 4weeks poststroke differs across levels of impairment in this exploratory study. Accelerometry and observation findings of paretic UL use were correlated and may be needed in different situations as they capture different information.

This article attempts to review papers on power assist rehabilitation robots, human motion intention, control laws, and estimation of power assist rehabilitation robots based on human motion intention in recent years. This paper presents the various ways in which human motion intention in rehabilitation can be estimated. This paper also elaborates on the control laws for the estimation of motion intention of the power assist rehabilitation robot. From the review, it has been found that the motion intention estimation method includes: Artificial Intelligence-based motion intention and Model-based motion intention estimation. The controllers include hybrid force/position control, EMG control, and adaptive control. Furthermore, Artificial Intelligence based motion intention estimation can be subdivided into Electromyography (EMG), Surface Electromyography (SEMG), Extreme Learning Machine (ELM), and Electromyography-based Admittance Control (EAC). Also, Model-based motion intention estimation can be subdivided into Impedance and Admittance control interaction. Having reviewed several papers, EAC and ELM are proposed for efficient motion intention estimation under artificial-based motion intention. In future works, Impedance and Admittance control methods are suggested under model-based motion intention for efficient estimation of motion intention of power assist rehabilitation robot. In addition, hybrid force/position control and adaptive control are suggested for the selection of control laws. The findings of this review paper can be used for developing an efficient power assist rehabilitation robot with motion intention to aid people with lower or upper limb impairment.

Current literature lacks a comparative analysis of different motion capture systems for tracking upper limb (UL) movement as individuals perform standard tasks. To better understand the performance of various motion capture systems in quantifying UL movement in the prosthesis user population, this study compares joint angles derived from three systems that vary in cost and motion capture mechanisms: a marker-based system (Vicon), an inertial measurement unit system (Xsens), and a markerless system (Kinect). Ten healthy participants (5F/5M; 29.6 ± 7.1 years) were trained with a TouchBionic i-Limb Ultra myoelectric terminal device mounted on a bypass prosthetic device. Participants were simultaneously recorded with all systems as they performed standardized tasks. Root mean square error and bias values for degrees of freedom in the right elbow, shoulder, neck, and torso were calculated. The IMU system yielded more accurate kinematics for shoulder, neck, and torso angles while the markerless system performed better for the elbow angles. By evaluating the ability of each system to capture kinematic changes of simulated upper limb prosthesis users during a variety of standardized tasks, this study provides insight into the advantages and limitations of using different motion capture technologies for upper limb functional assessment.

Although many gaming systems for early stroke rehabilitation capture kinematic data, studies on upper limb (UL) movement capacity analysis after virtual reality (VR)-based gaming interventions remain limited. This study is a secondary analysis of the EnteRtain randomized clinical trial and aims to examine the effects of gamified rehabilitation using a low-cost arm rehabilitation system compared to task-based training on UL movement capacity in individuals with acute or sub-acute stroke. This secondary analysis used data from a randomized, multicenter, single-blind clinical trial involving 120 participants (91 males) with unilateral stroke and an UL Brunnstrom motor recovery stage ≥ 1 to ≤ 5, recruited from four centers across India. Participants received either gamified training with the ArmAble™ device (experimental group; n = 64) or task-based training (control group; n = 56), alongside conventional therapy for two hours/day, six days/week, over two weeks, followed by four weeks of home-based UL rehabilitation. Movement capacity outcomes (reach distance, time, and movement velocity) were assessed by blinded evaluators at two and six weeks and analyzed using a linear mixed-effects regression model. At six weeks, the experimental group demonstrated significantly greater improvements compared to control group in movement velocity for reaches to both near [mean difference (95% CI): -2.8 (-5.0, -0.75); p = 0.008] and far targets [-2.7 (-4.9, -0.51); p = 0.016]. No significant differences were however observed at two weeks. Changes in reach distance and movement time were not statistically significant between the groups at any time point. Gamified rehabilitation with the ArmAble™ device enhanced UL movement velocity at six-week compared to task-based training in individuals recovering from acute/sub-acute stroke. These findings support the use of ArmAble™ both as an engaging therapeutic tool and as a quantitative assessment platform for evaluating UL function post-stroke.

In the last years there has been a noticeable progress in motor learning, neuroplasticity and functional recovery after the occurrence of brain lesion. Rehabilitation of motor function has been associated to motor learning that occurs during repetitive, frequent and intensive training. Neuro-rehabilitation is based on the assumption that motor learning principles can be applied to motor recovery after injury, and that training can lead to permanent improvements of motor functions in patients with muscle deficits. The emergent research field of Rehabilitation Engineering may provide promise technologies for neuro-rehabilitation therapies, exploiting the motor learning and neural plasticity concepts. Among those technologies, the FES-assisted systems could provide repetitive training-based therapies and have been developed to aid or control the upper and lower limbs movements in response to user’s intentionality. Surface electromyography (SEMG) reflects directly the human motion intention, so it can be used as input information to control an active FES-assisted system. The present work describes a neurorehabilitation platform at the upper-limb level, based on bilateral coordination training (i.e. mirror movements with the unaffected arm) using a close-loop active FES system controlled by user. In this way, this work presents a novel myoelectric controller for decoding movements of user to be employed in a neurorehabilitation platform. It was carried out a set of experiments to validate the myoelectric controller in classification of seven human upper-limb movements, obtaining an average classification error of 4.3%. The results suggest that the proposed myoelectric pattern recognition method may be applied to control close-loop FES system.

Voluntary residual motor efforts to the affected limb of patients after stroke have not been involved enough in most rehabilitation robot control strategies. In this paper, a natural integration between human and machine is proposed by using the surface electromyography (EMG) signals from six muscles which mainly contribute to the upper limb movement. A linear state space model is trained, which can estimate the real-time movement intention by using EMG signals, to calculate the movement needed forces and then provided by a cable-based upper limb rehabilitation robot. Ten healthy subjects are recruited to complete the tasks with and without robot assistances. The performances of the subjects with the assistances are compared to that of the subjects without assistances. Results show that the forces from the model were real-time continuously estimated and accurate. Furthermore, there is no significant difference in the group mean root mean square error (RMSE) and muscle activations between the task without assistance and with assistance. These results show that the robot using the state space model could provide physiologically appreciate assistance to the subject, and the robot could conduct the rehabilitation training combined with the voluntary residual motor efforts. Clinical test will be carried out to validate the feasibility of the robot-aided rehabilitation using myoelectrical control.

Chapter 11 - Smart walker's instrumentation and development with compliant optical fiber sensors

Use of the affected extremity during daily life is important if disuse atrophy is to be prevented after stroke. This study examined whether objectively measured real-world upper limb movement is associated with the amount of use of the affected upper limb, as assessed by a standardized assessment tool in older adults with acute or subacute stroke. This study also examined whether the real-world upper limb movement is associated with the extent of impairment of upper and lower extremities. The participants were 19 older adults with hemiparesis from acute or subacute stroke [17 (7) days after the stroke]. All the participants wore 3 accelerometers-1 on each wrist (bilateral accelerometry) and 1 on the waist-throughout a 24-hour period. They were interviewed about use of their upper limb in the real-world setting using a Motor Activity Log. Functions of the affected upper limb or upper and lower limbs were assessed using the National Institute of Health Stroke Scale, the Brunnstrom Recovery Stage (BRS), the Simple Test for Evaluating Hand Function, and the Functional Independence Measure tests. Movement counts measured with the wrist accelerometer on the affected upper limb (unilateral accelerometry) over 24 hours and during the 12-hour daytime period (08:00 to 20:00) were significantly correlated with scores for affected upper limb use (the Motor Activity Log) and functions (the upper extremity BRS and the affected side Simple Test for Evaluating Hand Function), upper and lower extremity functions (the National Institute of Health Stroke Scale, the lower extremity BRS, and the motor Functional Independence Measure), and step counts measured over the same period. To estimate the affected upper limb use or disuse, we subtracted the movement counts of the unaffected upper limb from those of the affected upper limb. As a result, the subtracted counts over 24 hours and during the 12-hour daytime period were only related to scores for affected upper limb use and functions. Bilateral accelerometry for monitoring upper limb movements in a real-world setting might be useful to clinicians for objective assessment of affected upper limb use or disuse and function among older adults with hemiparesis from acute or subacute stroke.