Posts Tagged Rehabilitation robotics

[ARTICLE] Upper Limb Rehabilitation Robot System Based on Internet of Things Remote Control – Full Text


Modern technology has been improving, as is medical technology. Over the years, rehabilitation medicine is developing and growing. The use of rehabilitation robots to achieve the upper limb motor function of patients with hemiplegia has also become a popular research in academia. Under this background, this paper proposes an upper limb robot rehabilitation system based on Internet of Things remote control. The upper limb robotic rehabilitation system based on the Internet of Things in this paper is composed of upper computer and lower computer. Information is collected by pressure sensor. The transmission process is realized by STM32 controller, which is first transmitted to the upper computer, and then the information needs to be processed After processing, it sends control commands to the lower computer controller to control the motor drive of the rehabilitation robot, so as to realize the rehabilitation training of the patient. In order to verify the reliability of the system in this paper, this paper conducted a motion test and system dynamic performance test. The research results of this paper show that the passive motion accuracy of the system in this paper has reached more than 97%, and the active motion accuracy has reached more than 98%. In addition, the maximum speed response time of the upper limb rehabilitation robot system based on the remote control of the Internet of Things in this paper is 5.7ms. The amount of adjustment is 5.32%, and the dynamic performance is good. The research results of this paper show that the upper limb rehabilitation robot system based on the Internet of Things remote control in this paper has excellent performance, which can provide a certain reference value for the research of rehabilitation robot.


Science and technology and people’s living standards are gradually improving, whether it is China or other countries in the world, and these changes will bring about an aging population problem. In recent years, due to the impact of cardiovascular and cerebrovascular diseases, there have been some changes in middle-aged and elderly patients with hemiplegia. The number of patients has increased and the trend of becoming younger. At the same time, on the other hand, due to the rapid growth of the number of transportation vehicles, more and more The more people suffer from nervous system injuries or limb injuries due to traffic accidents [1]. Strictly speaking, according to medical theory and clinical medicine, in addition to early surgical treatment and necessary medical care, correct and scientific rehabilitation education is also very important for the recovery and improvement of limb motor ability, but these patients have exercise Obstacles, can’t do rehabilitation training alone, and someone needs help, but in view of the fact that there are not enough medical staff in our country, these patients will be in an embarrassing situation. In this respect, the development of a remotely controlled upper limb rehabilitation robot is of great significance for solving the problem of unattended patients with hemiplegia.

Sanja Vukićević once designed a robust controller of a two-degree-of-freedom upper limb rehabilitation robot for the motion characteristics of rehabilitation training and the inherent properties of the robot, so that the robot can drive the precise trajectory of hemiplegic patients according to the given trajectory, ensuring Under the system dynamics model with zero error, the modeling error bounded error remains consistent and bounded, and the tracking error is zero. The simulation results of Sanja Vukićević show that the robust control strategy can make the system tracking error tend to under certain conditions Zero, has a good control effect, although Sanja Vukićević’s method improves the robustness of rehabilitation training robots, but the reliability has decreased [2][3]. Dobkin BH used the hemiplegic rehabilitation theory and upper limb physiological structure as the basis, combined with biological science, mechanical engineering, automatic control and other disciplines to design the upper limb functional rehabilitation robot. The control system of impedance control, and Simulink software was used to establish the simulation model of the control system, and the influence of the control parameters based on position impedance on the upper limb function control of rehabilitation robot was analyzed. The results of Dobkin B H show that the rehabilitation robot’s control effect on the upper limb function changes with the change of movement speed. The upper limb rehabilitation robot designed by Dobkin B H has good stability but its accuracy is lacking, and it needs to be improved [4][5]. Naranjo-Hernández David once proposed a new upper limb rehabilitation robot system based on virtual reality, which fully utilizes many advantages of robots participating in stroke upper limb rehabilitation. The system has the advantages of small size, light weight and rehabilitation interaction. Naranjo-Hernández David’s system is mainly composed of a haptic device called Phantom Premium, Upper Extremity Exoskeleton Rehabilitation Device (ULERD) and virtual reality environment. It has been experimentally proved that Naranjo-Hernández David’s method is accurate and convenient during the rehabilitation process However, the economy is not strong and needs to be strengthened [6][7].

This article adopts the Internet of Things remote control technology and designs the upper limb rehabilitation robot system. In this paper, the relevant theory of the remote control of the Internet of Things is first elaborated, then from the perspective of human kinematics, the motion model of the upper limb rehabilitation robot is constructed, and finally, the upper limb rehabilitation robot system based on the Internet of Things remote control is designed and set The corresponding experiment was carried out to test the system. The test results show that the system in this paper has good accuracy and dynamic performance.SECTION II.

Internet of Things Remote Control

The so-called remote control technology refers to the technology that the Internet controls and manages remote devices to control and manage signals based on signals. Its software usually includes client-side and server-side programs. As the Internet of Things becomes more and more popular, remote control technology is also popularized. It can achieve the effect of unconventional remote control through IoT media [8][9].

A. Internet of Things

The Internet of Things realizes the mutual exchange, mutual knowledge, and interactive information exchange between “machines and machines”. It can also be understood that through a variety of communication technologies, the Internet of Things is a very complex and diverse system technology.. According to the principles of information generation, transmission, processing and application, the Internet of Things can be divided into four levels: perception recognition layer, network construction layer, management service layer and integrated application layer [10][11].

1) Perception Recognition Layer

What is the core technology of the Internet of Things? It is perception and recognition, so the perception recognition layer is very important for the Internet of Things. So let’s take a look at what the perceptual recognition layer includes. The level of perceptual recognition includes radio frequency identification, wireless sensors and automatic information production equipment. Not only that, but also includes a variety of intelligent information used to artificially produce electronic products. It can be said that as an emerging technology, wireless sensor networks mainly use different types of sensors to obtain large-scale, long-term, real-time information on environmental status and behavior patterns [12].

2) Network Building Layer

The main function of this layer is to connect lower-level data (perceived recognition-level data) to higher levels such as the Internet for its use. The Internet and next-generation Internet (including IPv6 and other technologies) are the core networks of the Internet of Things. Various wireless networks on the edge can provide network access services anytime and anywhere. The existing WIMAX technology is included in the scope of the wireless metropolitan area network, and its role is to provide high-speed data transmission services in the metro area (about 100 km). On the other hand, the wireless local area network also includes the WIFI that almost every household is currently trying. The use of WIFI is very wide. The main function is to provide network access services for users in a certain area (family, campus, restaurant, airport, etc.). Not only that, the wireless personal area network also includes Bluetooth, ZigBee and other communication protocols. These several things have a common feature, that is, low power consumption, low transmission rate, short distance, generally used for personal electronic product interconnection, industrial equipment Control and other fields. The various types of wireless networks listed above are suitable for different environments and work together to provide convenient network access so that the Internet of Things can be achieved [13].

3) Management Service Layer

By supporting high-performance computer technology and large-capacity storage, the management service level can efficiently and reliably organize large-scale data and provide an intelligent support platform for high-level industry applications. Storage is the first step in information processing. The database system and various mass storage technologies developed later, even including network storage (such as data centers), have now been widely used in information technology, finance, telecommunications, automation, etc. These industries. Faced with massive amounts of information, how to organize and search for effective data is a key issue. Therefore, the main feature of the management service layer is “wisdom”. Through rich and detailed data, mechanical learning, data mining, expert systems and other means, it serves the management ’s The function is increasingly powerful [14].

4) Comprehensive Application Layer

What was the original role of the Internet? It is used to achieve computer-to-computer communication, and then developed into a connection between users and people as the main body, and the times are changing. Now, it is moving towards the goal of connecting things-things-people. Not only that, along with this process, network applications have also undergone tremendous changes, from the initial transmission of files and emails with basic functions of data services to user-centric applications. In addition, the layers of the Internet of Things are relatively independent but closely connected. Below the integrated application layer, different technologies at the same layer are complementary and suitable for different environments, forming a complete set of response strategies for this level of technology, and at different levels, providing different technical compositions and combinations to Create a complete solution according to the requirements of the implementation [15].

The network topology diagrams of the mobile communication network and the wireless sensor network are shown in Figure 1 and Figure 2, respectively.

FIGURE 1. - Mobile communication network topology.


Mobile communication network topology.

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FIGURE 2. - Wireless sensor network topology.


Wireless sensor network topology.

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From Figure 1 and Figure 2 we can see the network topology of the mobile communication network and wireless sensor network. The sensor is the first basic link to realize the automatic monitoring function of the system.It is generally composed of sensitive components, conversion originals, conversion circuits and auxiliary power sources.It can convert the sensed information into electrical signals or other output forms according to certain rules. So as to transmit and process information [16].

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[Abstract] Myoelectric Sensing for Intent Detection and Assessment in Upper-Limb Robotic Rehabilitation – Thesis


This thesis explores how surface electromyography (EMG) — the measurement of muscle force through voltage changes at the skin surface – can be of use to the field of upper-limb robotic rehabilitation. We focus on two main aspects: detecting human intention from measured muscle activity and assessing human motor coordination through synchronous muscle activations known as muscle synergies – each examples of the bidirectional communication found in tightly integrated human-robot interaction. EMG-based intent detection presents an opportunity to examine and promote human engagement at the neuromuscular level, enabling new protocols for intervention that could be combined with robotic rehabilitation, particularly for the most impaired of users. Meanwhile, the latest research in motor control proposes that natural, healthy human movement can be characterized by the presence of certain muscle synergies, and that the alteration of these synergies indicates a disruption, from neurological impairment or some other physical constraints, in natural movement. Wearable robotic devices are capable of altering muscle synergies, and though the mechanisms are not yet understood, a focus on altering muscle synergies is a promising new approach to neurorehabilitation. This thesis employs a robotic exoskeleton for the elbow and wrist joints designed for research in robotic rehabilitation of individuals with neurological impairments and now integrated with a myoelectric control interface. We first demonstrate the ability of a myoelectric interface to discern the user’s intended direction of motion in single-degree-of-freedom (DoF) and multi-DoF control modes with 10 able-bodied participants and 4 participants with incomplete cervical spinal cord injury (SCI). Predictive accuracy was high for able-bodied participants (averages over 99% for single-DoF and near 90% for multi-DoF), and performance in the SCI group was promising (averages ranging from 85% to 95% for single-DoF, and variable multi-DoF performance averaging around 60%), which is encouraging for the future use of myoelectric interfaces in robotic rehabilitation for SCI. Second, we explore the identification of synchronous muscle synergies in the muscles controlling the elbow and wrist, and the possible effects of robot-imposed task constraints on the neural constrains represented by synergy patterns. Our results indicate that constraining the unused degrees of freedom during a single-DoF movement inside the exoskeleton does not have a significant effect on the underlying muscle synergies in the task, and that methodological choices in muscle synergy analysis also do not have a large effect on the outcome. With all of these findings, we have achieved a deeper understanding of the value myoelectric sensing can bring to upper-limb robotic rehabilitation, and how much potential it has to advance the field toward greater accessibility to individuals of all levels of impairment.

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[REVIEW ARTICLE ] Robot-Assisted Therapy in Upper Extremity Hemiparesis: Overview of an Evidence-Based Approach – Full Text

Robot-mediated therapy is an innovative form of rehabilitation that enables highly repetitive, intensive, adaptive, and quantifiable physical training. It has been increasingly used to restore loss of motor function, mainly in stroke survivors suffering from an upper limb paresis. Multiple studies collated in a growing number of review articles showed the positive effects on motor impairment, less clearly on functional limitations. After describing the current status of robotic therapy after upper limb paresis due to stroke, this overview addresses basic principles related to robotic therapy applied to upper limb paresis. We demonstrate how this innovation is an evidence-based approach in that it meets both the improved clinical and more fundamental knowledge-base about regaining effective motor function after stroke and the need of more objective, flexible and controlled therapeutic paradigms.


Robot-mediated rehabilitation is an innovative exercise-based therapy using robotic devices that enable the implementation of highly repetitive, intensive, adaptive, and quantifiable physical training. Since the first clinical studies with the MIT-Manus robot (1), robotic applications have been increasingly used to restore loss of motor function, mainly in stroke survivors suffering from an upper limb paresis but also in cerebral palsy (2), multiple sclerosis (3), spinal cord injury (4), and other disease types. Thus, multiple studies suggested that robot-assisted training, integrated into a multidisciplinary program, resulted in an additional reduction of motor impairments in comparison to usual care alone in different stages of stroke recovery: namely, acute (57), subacute (18), and chronic phases after the stroke onset (911). Typically, patients engaged in the robotic therapy showed an impairment reduction of 5 points or more in the Fugl-Meyer assessment as compared to usual care. Of notice, rehabilitation studies conducted during the chronic stroke phase suggest that a 5-point differential represents the minimum clinically important difference (MCID), i.e., the magnitude of change that is necessary to produce real-world benefits for patients (12). These results were collated in multiple review articles and meta-analyses (1317). In contrast, the advantage of robotic training over usual care in terms of functional benefit is less clear, but there are recent results that suggest how best to organize training to achieve superior results in terms of both impairment and function (18). Indeed, the use of the robotic tool has allowed us the parse and study the ingredients that should form an efficacious and efficient rehabilitation program. The aim of this paper is to provide a general overview of the current state of robotic training in upper limb rehabilitation after stroke, to analyze the rationale behind its use, and to discuss our working model on how to more effectively employ robotics to promote motor recovery after stroke.

Upper Extremity Robotic Therapy: Current Status

Robotic systems used in the field of neurorehabilitation can be organized under two basic categories: exoskeleton and end-effector type robots. Exoskeleton robotic systems allow us to accurately determine the kinematic configuration of human joints, while end-effector type robots exert forces only in the most distal part of the affected limb. A growing number of commercial robotic devices have been developed employing either configuration. Examples of exoskeleton type include the Armeo®Spring, Armeo®Power, and Myomo® and of end-effector type include the InMotion™, Burt®, Kinarm™ and REAplan®. Both categories enable the implementation of intensive training and there are many other devices in different stages of development or commercialization (1920).

The last decade has seen an exponential growth in both the number of devices as well as clinical trials. The results coalesced in a set of systematic reviews, meta-analyses (1317) and guidelines such as those published by the American Heart Association and the Veterans Administration (AHA and VA) (21). There is a clear consensus that upper limb therapy using robotic devices over 30–60-min sessions, is safe despite the larger number of movement repetitions (14).

This technic is feasible and showed a high rate of eligibility; in the VA ROBOTICS (911) study, nearly two thirds of interviewed stroke survivors were enrolled in the study. As a comparison the EXCITE cohort of constraint-induced movement therapy enrolled only 6% of the screened patients participated (22). On that issue, it is relevant to notice the admission criteria of both chronic stroke studies. ROBOTICS enrolled subjects with Fugl-Meyer assessment (FMA) of 38 or lower (out of 66) while EXCITE typically enrolled subjects with an FMA of 42 or higher. Duret and colleagues demonstrated that the target population, based on motor impairments, seems to be broader in the robotic intervention which includes patients with severe motor impairments, a group that typically has not seen much benefit from usual care (23). Indeed, Duret found that more severely impaired patients benefited more from robot-assisted training and that co-factors such as age, aphasia, and neglect had no impact on the amount of repetitive movements performed and were not contraindicated. Furthermore, all patients enrolled in robotic training were satisfied with the intervention. This result is consistent with the literature (24).

The main outcome result is that robotic therapy led to significantly more improvement in impairment as compared to conventional usual care, but only slightly more on motor function of the limb segments targeted by the robotic device (16). For example, Bertani et al. (15) and Zhang et al. (17) found that robotic training was more effective in reducing motor impairment than conventional usual care therapy in patients with chronic stroke, and further meta-analyses suggested that using robotic therapy as an adjunct to conventional usual care treatment is more effective than robotic training alone (1317). Other examples of disproven beliefs: many rehabilitation professionals mistakenly expected significant increase of muscle hyperactivity and shoulder pain due to the intensive training. Most studies showed just the opposite, i.e., that intensive robotic training was associated with tone reduction as compared to the usual care groups (92526). These results are shattering the resistance to the widespread adoption of robotic therapy as a therapeutic modality post-stroke.

That said, not all is rosy. Superior changes in functional outcomes were more controversial until the very last years as most studies and reviews concluded that robotic therapy did not improve activities of daily living beyond traditional care. One first step was reached in 2015 with Mehrholz et al. (14), who found that robotic therapy can provide more functional benefits when compared to other interventions however with a quality of evidence low to very low. 2018 may have seen a decisive step in favor of robotic as the latest meta-analysis conducted by Mehrholz et al. (27) concluded that robot-assisted arm training may improve activities of daily living in the acute phase after stroke with a high quality of evidence However, the results must be interpreted with caution because of the high variability in trial designs as evidenced by the multicenter study (28) in which robotic rehabilitation using the Armeo®Spring, a non-motorized device, was compared to self-management with negative results on motor impairments and potential functional benefits in the robotic group.

The Robot Assisted Training for the Upper Limb after Stroke (RATULS) study (29) might clarify things and put everyone in agreement on the topic. Of notice, RATULS goes beyond the Veterans Administration ROBOTICS with chronic stroke or the French REM_AVC study with subacute stroke. RATULS included 770 stroke patients and covered all stroke phases, from acute to chronic, and it included a positive meaningful control in addition to usual care.[…]


Continue —-> Frontiers | Robot-Assisted Therapy in Upper Extremity Hemiparesis: Overview of an Evidence-Based Approach | Neurology

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[Abstract] An Omnidirectional Assistive Platform Integrated With Functional Electrical Stimulation for Gait Rehabilitation: A Case Study


This paper presents a novel omnidirectional platform for gait rehabilitation of people with hemiparesis after stroke. The mobile platform, henceforth the “walker”, allows unobstructed pelvic motion during walking, helps the user maintain balance and prevents falls. The system aids mobility actively by combining three types of therapeutic intervention: forward propulsion of the pelvis, controlled body weight support, and functional electrical stimulation (FES) for compensation of deficits in angular motion of the joints. FES is controlled using gait data extracted from a set of inertial measurement units (IMUs) worn by the user. The resulting closed-loop FES system synchronizes stimulation with the gait cycle phases and automatically adapts to the variations in muscle activation caused by changes in residual muscle activity and spasticity. A pilot study was conducted to determine the potential outcomes of the different interventions. One chronic stroke survivor underwent five sessions of gait training, each one involving a total of 30 minutes using the walker and FES system. The patient initially exhibited severe anomalies in joint angle trajectories on both the paretic and the non-paretic side. With training, the patient showed progressive increase in cadence and self-selected gait speed, along with consistent decrease in double-support time. FES helped correct the paretic foot angle during swing phase, and likely was a factor in observed improvements in temporal gait symmetry. Although the experiments showed favorable changes in the paretic trajectories, they also highlighted the need for intervention on the non-paretic side.

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[ARTICLE] Human arm weight compensation in rehabilitation robotics: efficacy of three distinct methods – Full Text



Arm weight compensation with rehabilitation robots for stroke patients has been successfully used to increase the active range of motion and reduce the effects of pathological muscle synergies. However, the differences in structure, performance, and control algorithms among the existing robotic platforms make it hard to effectively assess and compare human arm weight relief. In this paper, we introduce criteria for ideal arm weight compensation, and furthermore, we propose and analyze three distinct arm weight compensation methods (AverageFullEquilibrium) in the arm rehabilitation exoskeleton ’ARMin’. The effect of the best performing method was validated in chronic stroke subjects to increase the active range of motion in three dimensional space.


All three methods are based on arm models that are generalizable for use in different robotic devices and allow individualized adaptation to the subject by model parameters. The first method Average uses anthropometric tables to determine subject-specific parameters. The parameters for the second method Full are estimated based on force sensor data in predefined resting poses. The third method Equilibrium estimates parameters by optimizing an equilibrium of force/torque equations in a predefined resting pose. The parameters for all three methods were first determined and optimized for temporal and spatial estimation sensitivity. Then, the three methods were compared in a randomized single-center study with respect to the remaining electromyography (EMG) activity of 31 healthy participants who performed five arm poses covering the full range of motion with the exoskeleton robot. The best method was chosen for feasibility tests with three stroke patients. In detail, the influence of arm weight compensation on the three dimensional workspace was assessed by measuring of the horizontal workspace at three different height levels in stroke patients.


All three arm weight compensation methods reduced the mean EMG activity of healthy subjects to at least 49% compared with the no compensation reference. The Equilibrium method outperformed the Average and the Full methods with a highly significant reduction in mean EMG activity by 19% and 28% respectively. However, upon direct comparison, each method has its own individual advantages such as in set-up time, cost, or required technology. The horizontal workspace assessment in poststroke patients with the Equilibrium method revealed potential workspace size-dependence of arm height, while weight compensation helped maximize the workspace as much as possible.


Different arm weight compensation methods were developed according to initially defined criteria. The methods were then analyzed with respect to their sensitivity and required technology. In general, weight compensation performance improved with the level of technology, but increased cost and calibration efforts. This study reports a systematic way to analyze the efficacy of different weight compensation methods using EMG. Additionally, the feasibility of the best method, Equilibrium, was shown by testing with three stroke patients. In this test, a height dependence of the workspace size also seemed to be present, which further highlights the importance of patient-specific weight compensation, particularly for training at different arm heights.

Trial registration,NCT02720341. Registered 25 March 2016


Human arm weight compensation in robotic rehabilitation

In the acute phase post-stroke, approximately 66%−80% of patients experience reduced arm function due to paresis and subsequent arm weakness [12]. In chronic stroke patients abnormal synergies restrict the patient’s movement [3] and workspace [4] as a loss of independent joint control. In both acute and chronic patients, arm weight compensation can extend the patient’s workspace and, therefore, allow training of tasks that have higher relevance for activities of daily living [46]. The training of these tasks is according to the known “use it and improve it, or lose it” principle of neurorehabilitation, which is considered a key factor for successful rehabilitation [7]. Therefore, many rehabilitation robots with arm weight compensation functions have been developed. The common robot types that provide system-dependent arm weight compensation can be divided in gantry-based robots [810], passive exoskeletons [1112], actuated exoskeletons [4111314], and actuated end-effector robots [561518].

For actuated rehabilitation robots, assist-as-needed control strategies are also commonly used. These strategies not only support the patients along the movement direction, but also against the gravity in an indirect way [1920]. However, in this paper, we focus only on the arm weight compensation as an independent assistance dimension, since the support along movement direction may not always be desirable. Furthermore, there are adaptive control strategies that readjust assistance over time [21]. However, since arm weight is constant over time, we aim to estimate the arm weight parameters once in the beginning of the therapy session and not to adapt them during the therapy. From a literature review [822] and our own experience, the following four requirements of ideal, generalizable weight compensation for robot-assisted training of activities of daily living were deduced: Freedom of movement, no additional disturbances, scalability, and applicability to other systems [13].

Freedom of movement

The degrees of freedom of the human arm joints should not be restricted by the robot. From the shoulder to the wrist joint, the human arm can be approximated by five degrees of freedom (shoulder horizontal abduction/adduction, shoulder elevation, shoulder internal/external rotation, elbow flexion/extension, forearm pronation/supination). Arm weight compensation should be provided in any pose without restricting or hindering any possible degree of freedom. However, most end-effector robots can restrict the user’s freedom of movement while providing arm weight compensation due to mechanical limitations and missing human joint angle information, e.g., [561518].

No additional disturbances

With the exception of arm weight compensation, each robot should behave mechanically transparent during physical human-robot interactions [23]. Ideally, weight is a static force, and thus, the robot torques providing arm weight compensation should only depend on pose, arm weight, and arm length. Generally, end-effector robots support arm weight only at the end-effector without knowledge of the user’s arm joint angles, which can lead to over- or under-compensation of arm weight. In particular, systematic disturbances due to spring properties are present in passive exoskeletons [12], and unwanted horizontal forces due to the vertical sling attachments are present in gantry-based robots [8].


A progressive reduction in arm weight compensation during rehabilitation therapy can lead to an increased active range of motion [24]. Furthermore, scalability of arm weight compensation can be used for assessment of arm weight-induced impairments [25]. Independent upper and lower arm weight scalability could allow more individualized assessments and therefore, tailored rehabilitation therapies for arm weight-induced impairments.

Applicability to other systems

The applicability of arm weight compensation methods to other systems is robot type-dependent. Exemplarily, a passive exoskeleton can entail mechanical spring-based arm weight compensation [12], which needs to be mechanically adapted for applications in different types of robots. Ideally, the method should be a software solution, that can be easily applied to a variety of rehabilitation robots with actuation.

Evaluation of arm weight compensation efficacy

The highest arm weight compensation efficacy is reached through correctly compensating for gravity contributing to arm weight in every possible arm pose, i.e., overcompensation or undercompensation of arm weight leads to a lower arm weight compensation efficacy. Arm weight compensation has been proven to be an important factor for enabling patients to train for tasks that require longer reaching distances [5] and an increased workspace [62427]. Furthermore, improvements in arm weight compensation efficacy are expected to lead to an even greater increase in workspace for stroke patients [6]. However, most studies of arm weight compensation by rehabilitation robots have focused on the gains that stroke patients achieve in clinical scores rather than the provided arm weight compensation [28]. While gains in clinical scores are good indicators of performance development in general, it is difficult to assess the contribution of the provided weight compensation to these gains. Namely, the efficacy of weight compensation per se was not evaluated in parallel, i.e., how much unloading is effectively applied for a certain unloading condition and arm pose. Therefore, the results of clinical studies that evaluate weight compensation for one particular rehabilitation device are difficult to generalize to other devices, as the weight compensation performance might differ among devices. In summary, the efficacy of arm weight compensation in rehabilitation robotics has been rarely investigated [29]. However, several papers have investigated the efficacy of arm weight compensation through additional electromyography (EMG) measurements of relevant muscles [2933]. For example, arm weight compensation decreased EMG activity during static holding [32] and reaching tasks [2931], and the transfer of this effect to stroke patients was also shown [29]. However, static holding was performed in only one pose [32], not over the whole workspace to analyze pose-dependent effects of arm weight compensation. Furthermore, previous EMG signal analyses have mainly focused on individual muscle evaluations [2931] instead of a combined evaluation of all relevant muscles with respect to their passive reference measurements as a score. Additional active EMG reference measurements to adapt to a subject’s specific behavior and physiology have not been a main focus [13]. Finally, the arm weight relief efficacies of different arm weight compensation methods have never been compared, even for use of the same device.

In this paper, we present three different arm weight compensation methods AverageFull, and Equilibrium. These arm weight compensation methods are based on arm models that are developed to address the presented four critical requirements. Each method has advantages and disadvantages regarding the technology used, hardware costs, and calibration effort/time. All three compensation methods are consecutively implemented and compared using the rehabilitation robot ARMin. This paper sequentially presents the following studies: First, the estimation results of the arm weight compensation methods are analyzed for spatial and temporal sensitivity. The efficacies of all three methods are subsequently tested with EMG measurements in healthy subjects. Finally, the most successful method is tested in stroke patients. During the assessment with stroke patients, the horizontal workspace is assessed at three different height levels to determine if there is a height-dependence of arm weight compensation over the workspace.[…]


Continue —->  Human arm weight compensation in rehabilitation robotics: efficacy of three distinct methods | SpringerLink


Fig. 2

The five EMG measurement poses (P1,…,P5) for the efficacy comparison study: The poses represent the ADL usage of a stroke subject: P1) Mouth and head reaching (-45,90,90,100,0), P2) Ipsilateral reaching on shoulder level (-45,70,5,60,0), P3) Contralateral reaching (10,70,30,40,60), P4) Medial reaching at shoulder level (-15,65,15,50,0), P5) Ipsilateral reaching on abdomen level (-50,55,30,40,0). Pose coordinates of the axes (1,2,3,4,5) in degree are according to anatomical axes definitions [<a id="ref-link-section-d60942e3491" title="Holzbaur KRS, Murray WM, Delp SL. A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control. Ann Biomed Eng. 2005; 33(6):829–40.
.” href=”; data-track=”click” data-track-action=”reference anchor” data-track-label=”link” data-test=”citation-ref” aria-label=”Reference 39″>39]. Axis 1 (horizontal shoulder abduction/adduction), axis 2 (shoulder elevation), axis 3 (internal /external shoulder rotation), axis 4 (elbow flexion/extension), and axis 5 (forearm pronation/supination). P1 and P2 are the chosen estimation poses (14,20) of the sensitivity analysis and P3P4 and P5 complement the workspace for stroke patients in ARMin. All coordinates of the poses of the sensitivity analysis and efficacy comparison study are shown in the Additional file 2


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[ARTICLE] Assist-as-needed control strategy for upper-limb rehabilitation based on subject’s functional ability – Full Text

The assist-as-needed technique in robotic rehabilitation is a popular technique that encourages patients’ active participation to promote motor recovery. It has been proven beneficial for patients with functional motor disability. However, its application in robotic therapy has been hindered by a poor estimation method of subjects’ functional or movement ability which is required for setting the appropriate robotic assistance. Moreover, there is also the need for consistency and repeatability of the functional ability estimation in line with the clinical procedure to facilitate a wider clinical adoption. In this study, we propose a new technique of estimation of subject’s functional ability based on the Wolf Motor Function Test. We called this estimation the functional ability index. The functional ability index enables the modulation of robotic assistance and gives a more consistent indication of subjects’ upper-limb movement ability during therapy session. Our baseline controller is an adaptive inertia-related controller, which is integrated with the functional ability index algorithm to provide movement assistance as when needed. Experimental studies are conducted on three hemiplegic patients with different levels of upper-limb impairments. Each patient is requested to perform reaching task of lifting a can from table-to-mouth according to the guidelines stipulated in the Wolf Motor Function Test. Data were collected using two inertial measurement unit sensors installed at the flexion/extension joints, and the functional ability score of each patient was rated by an experienced therapist. Results showed that the proposed functional ability index algorithm can estimate patients’ functional ability level consistently with clinical procedure and can modify generated robotic assistance in accordance with patients’ functional movement ability.

The assist-as-needed (AAN) robotic strategy is a popular strategy for encouraging patients’ active participation in robot-assisted rehabilitation therapy. Numerous clinical outcomes have suggested the effectiveness of the AAN scheme to induce neuroplasticity in patients with neurological impairment.1 The AAN strategy focuses on providing the minimal amount of robotic assistance necessary for a patient to complete a movement,2 thus a significant effort is required from the patient. If the patient can perform the task flawlessly, robotic assistance is withdrawn. However, if the patient cannot complete the given task, assistance is offered only as much as it is needed.3

Deploying robotic assistance in accordance with the AAN strategy still come with many shortcomings.3,4 One major issue is how to appropriately estimate patients’ functional ability to set the correct level of robotic assistance. Another issue is the consistency of the estimated subject’s functional ability with clinical data and the repeatability across a wide range of subjects. An appropriate estimation of subject’s functional ability consistent with clinical data can give a realistic basis for deploying robotic assistance, since it gives a measure of subject’s actual disability level or recovery progress.5,6

A few strategies of AAN have been proposed recently which have attempted to address the challenges in the scheme. Wolbrecht et al.7 proposed a model-based robotic assistance strategy which can enable a robot to learn the patients’ ability in real time based on a radial-basis function (RBF). The RBF is applied under an adaptive control framework.

Another AAN strategy was proposed by Pehlivan et al.3 The authors introduced a minimal assist-as-needed (mAAN) strategy which uses a Kalman filter to estimate subjects’ functional inputs instead of the RBF technique that is a sensor-less force estimation strategy. Under the scheme, the ANN strategy is achieved in the following two ways: (1) by updating the derivative feedback gain to modify the bounds of allowable error on the desired trajectory and (2) by decaying a feed-forward disturbance rejection term which reduces the constraint on allowable quick movements. The combined effect could vary the robotic assistance according to the subjects’ capability.8 The potential limitation of this approach is the reliance on the robot model for the estimation of subject’s capability. It is well known that model errors always exist and can significantly excite the disturbance term making it difficult to accurately estimate the subject’s input. There is the implication that different robot models would produce different functional ability estimates which will hinder an appropriate standardization or deployment of robotic assistance for clinical purpose.9,10

Pérez-Rodríguez et al.11 also introduced an AAN strategy called anticipatory assistance-as-needed control algorithm capable of ensuring that the deviation from a patients’ desired trajectory is restored by giving an anticipated force assistance. This way, robotic assistance is always given as a restoring force to maintain the subject on the reference (desired) trajectory. With regards to the validity of this strategy, there are however no experimental studies till date.

Other noteworthy AAN strategies include the rule-based assistive strategy proposed by Wang et al.,12 which is applied in Physiotherabot; the hybrid impedance control for wrist and forearm rehabilitation proposed by Akdoğan and Adli,13 which is applied on a 3-degree-of-freedom (3-DOF) upper-limb rehabilitation robot; and the visual error augmentation-based AAN proposed by Akdoğan et al.,14 which can provide robotic assistance as needed by amplifying tracking error to heighten the participant’s motivation.

Efforts in developing an appropriate estimation strategy for AAN robotic assistance are still on course;15 however, there has been less focus on developing appropriate estimation techniques of subject’s functional ability that are consistent with the clinical procedure and that can be integrated in the control loop to provide robotic assistance.15,16

In this paper, we propose an ANN strategy to direct robotic assistance based on a novel functional ability index (FAI). The main originality of this work is the derivation of the new FAI estimation algorithm in accordance with the clinical procedure for the estimation of subject’s motor ability in movement task. As a preliminary investigation, we derive our FAI following the Wolf Motor Function Test (WMFT), a popular motor function test with consistency over a wide range of neurologically impaired patients. The FAI serves as input to a decay algorithm under the adaptive control law which consequently varies the robotic assistance according to the subject’s functional ability. The FAI is independent on the robot model or controller adaptation law and thus it is unaffected by modelling uncertainties.

The rest of the paper is organized as follows: section ‘System dynamic and control’ presents the dynamics for the robotic rehabilitation system, the proposed FAI, and the proposed control algorithm. Section ‘Experimental study’ presents the data collection and simulation study; section ‘Results’ describes the results; section ‘Discussion’ presents the discussion; and section ‘Conclusion’ concludes the paper.

The mechanical system

The proposed prototype of the exoskeleton system is shown in Figure 1. The system is an upper-limb rehabilitation robotic device with two active degrees of freedom (DOFs) at the shoulder and elbow joint, respectively. If actively controlled, the exoskeleton can permit abduction/adduction (AA) movement of the shoulder joint and flexion/extension (FE) movement of the elbow, thus allowing the possibility of performing the table to mouth reaching task.


Figure 1. Exoskeleton device is of 2 degrees of freedom (DOFs).

Continue —> Assist-as-needed control strategy for upper-limb rehabilitation based on subject’s functional ability – Shawgi Younis Ahmed Mounis, Norsinnira Zainul Azlan, Fatai Sado,

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[Abstract] Exoskeleton design and adaptive compliance control for hand rehabilitation

An adaptive robotic system has been developed to be used for hand rehabilitation. Previously developed exoskeletons are either very complex in terms of mechanism, hardware and software, or simple but have limited functionality only for a specific rehabilitation task. Some of these studies use simple position controllers considering only to improve the trajectory tracking performance of the exoskeleton which is inadequate in terms of safety and health of the patient. Some of them focus only on either passive or active rehabilitation, but not both together. Some others use EMG signals to assist the patient, but this time active rehabilitation is impossible unless different designs and control strategies are not developed. The proposed mechanical structure is extremely simple. The middle and the proximal phalanxes are used as a link of consecutively connected two 4-bar mechanisms, respectively. The PIP and MCP joints are actuated by a single electro mechanical cylinder to produce complex flexion and extension movements. It is simpler than similar ones from aspect with the mechanical structure and the biodynamic fit of the hand, making it practicable in terms of production and personal usage. Simple design lets to implement adaptive compliance controller for all active and passive rehabilitation tasks, instead of developing complex and different strategies for different rehabilitation tasks. Furthermore, using the Luenberger observer for unmeasured velocity state variable, an on-line estimation method is used to estimate the dynamic parameters of the system. This makes possible to estimate the force exerted by the patient as well, without a force sensor.


via Exoskeleton design and adaptive compliance control for hand rehabilitation – Gazi Akgun, Ahmet Emre Cetin, Erkan Kaplanoglu,

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[Abstract] An Elbow Exoskeleton for Upper Limb Rehabilitation With Series Elastic Actuator and Cable-Driven Differential


Movement impairments resulting from neurologic injuries, such as stroke, can be treated with robotic exoskeletons that assist with movement retraining. Exoskeleton designs benefit from low impedance and accurate torque control. We designed a two-degrees-of-freedom tethered exoskeleton that can provide independent torque control on elbow flexion/extension and forearm supination/pronation. Two identical series elastic actuators (SEAs) are used to actuate the exoskeleton. The two SEAs are coupled through a novel cable-driven differential. The exoskeleton is compact and lightweight, with a mass of 0.9 kg. Applied rms torque errors were less than 0.19 Nm. Benchtop tests demonstrated a torque rise time of approximately 0.1 s, a torque control bandwidth of 3.7 Hz, and an impedance of less than 0.03 Nm/° at 1 Hz. The controller can simulate a stable maximum wall stiffness of 0.45 Nm/°. The overall performance is adequate for robotic therapy applications and the novelty of the design is discussed.

via An Elbow Exoskeleton for Upper Limb Rehabilitation With Series Elastic Actuator and Cable-Driven Differential – IEEE Journals & Magazine

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[Abstract + References] Design and Kinematics Analysis of a Bionic Finger Hand Rehabilitation Robot Mechanism


The rehabilitation process of human fingers is a coupling movement of wearable hand rehabilitation equipment and human fingers, and its design must be based on the kinematics of human fingers. In this paper, the forward kinematics and inverse kinematics models are established for the index finger. Kinematics analysis is carried out. Then a bionic finger rehabilitation robot is designed according to the movement characteristics of the finger, A parallelogram linkage mechanism is proposed to make the joint independent drive, realize the flexion/extension movement, and perform positive kinematics and inverse kinematics analysis on the mechanism. The results show that it conforms to the kinematics of the index finger and can be used as the mechanism model of the finger rehabilitation robot.
1. Ibrahim Yildiz, “A Low-Cost and Lightweight Alternative to Rehabilitation Robots: Omnidirectional Interactive Mobile Robot for Arm Rehabilitation” in Arabian Journal for Science & Engineering, Springer Science & Business Media B.V., vol. 43, no. 3, pp. 1053-1059, 2018.

2. Bai Shaoping, Gurvinder S. Virk, Thomas G. Sugar, Wearable Exoskeleton Systems: Design control and applications[M], Institution of Engineering and Technology Control, pp. 1-406, 2018.

3. Kai Zhang, Xiaofeng Chen et al., “System Framework of Robotics in Upper Limb Rehabilitation on Poststroke Motor Recovery”, Behavioural Neurology, vol. 12, pp. 1-14, 2018.

4. Yang Haile, Zhu Huiying, Lin Xingyu, “Review of Exoskeleton Wearable Rehabilitation System[J]”, Metrology and testing technology, vol. 46, no. 03, pp. 40-44, 2019.

5. Xiang Shichuan, Meng Qiaoling, Yu Hongliu, Meng Qingyun, “Research status of compliant exoskeleton rehabilitation manipulator [J]”, Chinese Journal of Rehabilitation Medicine, vol. 33, no. 04, pp. 461-465+474, 2018.

6. Wu Hongjian, Li Lina, Li Long, Liu Tian, Jue Wang, “Review of comprehensive intervention by hand rehabilitation robot after stroke [J]”, Journal of biomedical engineering, vol. 36, no. 01, pp. 151-156, 2019.

7. Yu Junwei, Xu Hongbin, Xu Taojin, Zhang Chengjie, Lu Shiqing, “Structure Design and Finite Element Analysis of a Rope Traction Upper Limb Rehabilitation Robot [J]”, Mechanical transmission, vol. 42, no. 12, pp. 93-97, 2018.

8. Chang Ying, Meng Qingyun, Yu Hongliu, “Research progress on the development of hand rehabilitation robot [J]”, Beijing Biomedical Engineering, vol. 37, no. 06, pp. 650-656, 2018.

9. N A I M Rosli, M A A Rahman, S A Mazlan et al., “Electrocardiographic (ECG) and Electromyographic (EMG) signals fusion for physiological device in rehab application[C]”, IEEE Student Conference on Research and Development, pp. 1-5, 2015.

10. K O Thielbar, K M Triandafilou, H C Fischer et al., “Benefits of using a voice and EMG- Driven actuated glove to support occupational therapy for stroke survivors”, IEEE Trans Neural Syst Rehabil Eng, vol. 25, no. 3, pp. 297-305, 2017.


via Design and Kinematics Analysis of a Bionic Finger Hand Rehabilitation Robot Mechanism – IEEE Conference Publication

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[Abstract] Remote Upper Limb Exoskeleton Rehabilitation Training System Based on Virtual Reality


According to the present situation that the treatment means for apoplectic patients is lagging and weak, a set of long-distance exoskeleton rehabilitation training system with 5 DOF for upper limb was developed. First, the mechanical structure and control system of the training system were designed. Then a new kind of building method for virtual environment was proposed. The method created a complex model effectively with good portability. The new building method was used to design the virtual training scenes for patients’ rehabilitation in which the virtual human model can move following the trainer on real time, which can reflect the movement condition of arm of patient factually and increase the interest of rehabilitation training. Finally, the network communication technology was applied into the training system to realize the remote communication between the client-side of doctor and training system of patient, which makes it possible to product rehabilitation training at home.

via Remote Upper Limb Exoskeleton Rehabilitation Training System Based on Virtual Reality – IEEE Conference Publication

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