Fig 6 - uploaded by Anand Asokan
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Rectified EMG waveform during the initial rest phase. The µ rest and σ rest is obtained from this data. The threshold is calculated from equation (1).
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![Fig. 2: CLEVERarm. Source: [10]](profile/Anand-Asokan-2/publication/334752133/figure/fig1/AS:848817045897216@1579385162298/CLEVERarm-Source-10_Q320.jpg)
![Fig. 5: Electrode placement on Upper arm. Source: [14]](profile/Anand-Asokan-2/publication/334752133/figure/fig4/AS:848817045897227@1579385162818/Electrode-placement-on-Upper-arm-Source-14_Q320.jpg)
Rehabilitation of patients who have recovered from a stroke is a tedious and difficult process, involving a very long recovery time for the patient. Therapists guide patients to do a series of exercises or tasks, for short but intensive sessions. Passive exoskeletons are often used to keep track of the patient’s progress digitally, along with thera...
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Aims: To explore younger adults' experiences of stroke rehabilitation to inform practice, education and future health policy.
Design: Qualitative analysis of digital and other media sources on public platforms.
Methods: Between March and June 2020, the experiences of younger adult stroke survivors aged 18 to 45 at the time of the stroke were coll...
BACKGROUND: Proprioception is the awareness of the position, movement, and muscular force generated by the body and its musculoskeletal parts, and is an important somatosensory impairment to assess in people with stroke. We know that rehabilitation clinicians self-report to assess proprioception in approximately two-thirds of people with stroke. Wh...
Despite rehabilitation, stroke patients continue to have impaired function and reduced health-related quality of life (HRQoL) even in a chronic stage. However, no clear information is available on long-term variations in HRQoL. In this study, we aimed to report the short- and long-term changes in HRQoL in a subacute stroke sample that was enrolled...
Bicycles are one of the sports equipment as well as a mode of transportation that is environmentally friendly, when using a bicycle, all members of the body perform movements. In this study, a tricycle with a delta concept was made, that is 2 wheels behind and 1 wheel in front. This bike is designed to help stroke sufferers perform physical rehabil...
Post-stroke is a stage a patient undergoes if the patient has had a previous stroke. Stroke is a big and serious problem. As the second most common cause of disability of people at age of over 60 years. For patients having experienced a stroke, rehabilitation is a way to make them able to do activities of daily living as before. Stroke Rehabilitati...
Citations
... In order to improve the rehabilitation outcome, different neurotechnologies have been proposed in the last decade [13][14][15][16]. The use of robotics, brain-computer interfaces, and noninvasive stimulation has showed promising results and needs to be further explored to translate them to clinical practice [1,16,17]. ...
Stroke is a leading cause of disability and death worldwide, with a prevalence of 200 millions of cases worldwide. Motor disability is presented in 80% of patients. In this context, physical rehabilitation plays a fundamental role for gradually recovery of mobility. In this work, we designed a robotic hand exoskeleton to support rehabilitation of patients after a stroke episode. The system acquires electromyographic (EMG) signals in the forearm, and automatically estimates the movement intention for five gestures. Subsequently, we developed a predictive adaptive control of the exoskeleton to compensate for three different levels of muscle fatigue during the rehabilitation therapy exercises. The proposed system could be used to assist the rehabilitation therapy of the patients by providing a repetitive, intense, and adaptive assistance.
... В этот период конструкция и функциональность экзоскелетов верхних конечностей уходит в биомедицинскую сторону, помогая пожилым людям или инвалидам, которые с помощью датчиков, искусственного интеллекта и виртуальной или дополненной проекции могут стать настоящими реабилитационными медсёстрами [5]. Эти степени свободы помогают нам классифицировать системы в совокупности с количеством элементов системы, что приводит к определённому протоколу управления. ...
В этой статье представлен обзор активных технологий экзоскелета руки за последнее десятилетие для реабилитации, помощи и усиления. Экзоскелеты рук по-прежнему являются активной областью исследований из-за проблем, с которыми сталкиваются инженеры. У каждого экзоскелета руки есть определённые требования, которые необходимо выполнить для достижения своих целей. Эти требования были извлечены и разбиты на две части: общие и специальные, чтобы создать общую платформу для разработки будущих устройств. Поскольку это все ещё развивающаяся область, требования также формируются в соответствии с достижениями в этой области. Технические проблемы, такие как требования к размеру, весу, эргономике, реабилитации, приводам и датчикам, связаны со сложной анатомией и биомеханикой руки. Таким образом, эта статья обобщает технологию в систематическом подходе и рассматривает современное состояние активных экзоскелетов рук с упором на реабилитационные и вспомогательные устройства.
... However, the use of mechanical sensors to detect changes in arm movement has a slow response. On the other hand, active rehabilitation involves muscle contractions that are attempted by the user to move the exoskeleton device [27], [28] [21], [26], [29]- [37]. On the other hand, the EMG signal generated by the muscles when the arm performs flexion and extension movements is non-linear so it requires pre-processing the EMG signal to produce linear angle predictions. ...
Post-stroke can cause partial or complete paralysis of the human limb. Delayed rehabilitation steps in post-stroke patients can cause muscle atrophy and limb stiffness. Post-stroke patients require an upper limb exoskeleton device for the rehabilitation process. Several previous studies used more than one electrode lead to control the exoskeleton. The use of many electrode leads can lead to an increase in complexity in terms of hardware and software. Therefore, this research aims to develop single lead EMG pattern recognition to control an upper limb exoskeleton. The main contribution of this research is that the robotic upper limb exoskeleton device can be controlled using a single lead EMG. EMG signals were tapped at the biceps point with a sampling frequency of 2000 Hz. A Raspberry Pi 3B+ was used to embed the data acquisition, feature extraction, classification and motor control by using multithread algorithm. The exoskeleton arm frame is made using 3D printing technology using a high torque servo motor drive. The control process is carried out by extracting EMG signals using EMG features (mean absolute value, root mean square, variance) further extraction results will be trained on machine learning (decision tree (DT), linear regression (LR), polynomial regression (PR), and random forest (RF)). The results show that machine learning decision tree and random forest produce the highest accuracy compared to other classifiers. The accuracy of DT and RF are of 96.36±0.54% and 95.67±0.76%, respectively. Combining the EMG features, shows that there is no significant difference in accuracy (p-value 0.05). A single lead EMG electrode can control the upper limb exoskeleton robot device well.
... However, the use of mechanical sensors to detect changes in arm movement has a slow response. On the other hand, active rehabilitation involves muscle contractions that are attempted by the user to move the exoskeleton device [27], [28] [21], [26], [29]- [37]. On the other hand, the EMG signal generated by the muscles when the arm performs flexion and extension movements is non-linear so it requires pre-processing the EMG signal to produce linear angle predictions. ...
Post-stroke can cause partial or complete paralysis of the human limb. Delayed rehabilitation steps in post-stroke patients can cause muscle atrophy and limb stiffness. Post-stroke patients require an upper limb exoskeleton device for the rehabilitation process. Several previous studies used more than one electrode lead to control the exoskeleton. The use of many electrode leads can lead to an increase in complexity in terms of hardware and software. Therefore, this research aims to develop single lead EMG pattern recognition to control an upper limb exoskeleton. The main contribution of this research is that the robotic upper limb exoskeleton device can be controlled using a single lead EMG. EMG signals were tapped at the biceps point with a sampling frequency of 2000 Hz. A Raspberry Pi 3B+ was used to embed the data acquisition, feature extraction, classification and motor control by using multithread algorithm. The exoskeleton arm frame is made using 3D printing technology using a high torque servo motor drive. The control process is carried out by extracting EMG signals using EMG features (mean absolute value, root mean square, variance) further extraction results will be trained on machine learning (decision tree (DT), linear regression (LR), polynomial regression (PR), and random forest (RF)). The results show that machine learning decision tree and random forest produce the highest accuracy compared to other classifiers. The accuracy of DT and RF are of 96.36±0.54% and 95.67±0.76%, respectively. Combining the EMG features, shows that there is no significant difference in accuracy (p-value >0.05). A single lead EMG electrode can control the upper limb exoskeleton robot device well.
... That is why within the abstractions that have been made in the domain, the function called 'activities' has been added, which tries to abstract the common functionalities that industrial robots can use in Arduino so that it can reuse them. The above is not a novel concept in robotic arms used in industry [40], but it is in those in the Arduino domain. ...
Software reuse has potential for educational purposes since it uses decomposition and abstraction, two necessary skills to learn programming. Software reuse techniques require abstractions that are not obvious to students or even to professionals. Taking advantage of these techniques, students can learn computer programming in a productive and organized way. This paper proposes to use the Software Product Line (SPL) reuse technique as a strategy for learning to program industrial robots with the Arduino platform. First, the paper explains SPL construction and application with first-year university students. The SPL proposes abstractions close to the industrial robots domain with a simplified variability. The paper uses the case study method to show the feasibility of using the SPL approach in a learning environment. In this evaluation, students reused 38% to 43% of the total code needed to program the robot. It represents an improvement in the time it takes students to program industrial robotics solutions facilitating their learning. In addition, the paper unveils some limitations related to usability, specific knowledge, and some exploitable technologies.
... This exoskeleton is proposed as a compact system with one Degree-of-Freedom (1-DoF) in the elbow joint, allowing both flexion and extension movements. The implementation of 1-DoF allows for a functional proof of concept focusing on the ergonomic and practical design for rehabilitation processes without neglecting the clinical importance, developing a suitable device for patients with different pathologies that cause partial or complete disability of the upper limbs [29,30]. ...
Neuromotor rehabilitation and recovery of upper limb functions are essential to improve the life quality of patients who have suffered injuries or have pathological sequels, where it is desirable to enhance the development of activities of daily living (ADLs). Modern approaches such as robotic-assisted rehabilitation provide decisive factors for effective motor recovery, such as objective assessment of the progress of the patient and the potential for the implementation of personalized training plans. This paper focuses on the design, development, and preliminary testing of a wearable robotic exoskeleton prototype with autonomous Artificial Intelligence-based control, processing, and safety algorithms that are fully embedded in the device. The proposed exoskeleton is a 1-DoF system that allows flexion-extension at the elbow joint, where the chosen materials render it compact. Different operation modes are supported by a hierarchical control strategy, allowing operation in autonomous mode, remote control mode, or in a leader-follower mode. Laboratory tests validate the proper operation of the integrated technologies, highlighting a low latency and reasonable accuracy. The experimental result shows that the device can be suitable for use in providing support for diagnostic and rehabilitation processes of neuromotor functions, although optimizations and rigorous clinical validation are required beforehand.
... Complex and wearable or mobile structures are shown in [24,31,41,55]. These devices were noteworthy for maintaining compact properties of usability and strength, despite being bulkier structures. ...
... The most frequently developed type of robotic exoskeleton is the hard exoskeleton, corresponding to 84% of the reviewed literature (see Figure 5b). Rigid exoskeletons are systems designed mechanically to support the full weight of the limbs [31,36,40,45,52,53] and their structure usually offers high resistance to tension or deformation [23,24,41,48]. Hard exoskeletons can be quite compact and provide some mechanical flexibility, which facilitates limb mobilisation tasks [22,29,32,33,37,42,46]. ...
... It is also common to find structures formed by a wide variety of metals (27%), which provided rigidity to the structures [27,36,41,45,52]. For instance, in [31] after numerous tests it was concluded that the most appropriate material to be used is aluminium alloy 6061-T6. ...
Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.
The precise gait phase detection with lightweight equipment under variable conditions is crucial for low limb exoskeleton robots. Therefore, the kinematics and dynamics information are investigated. In this paper, a novel radius-margin-based support vector machine (SVM) model with particle swarm optimization (PSO) in feature space called PSO-FSVM is proposed for gait phase detection. The proposed method addresses the dual objectives of maximizing margin while minimizing radius, employing PSO to fine-tune the parameters of the FSVM. This enhancement significantly bolsters the classification accuracy of the SVM. For the measurement of gait features with a lightweight sensor system, the plantar pressure insoles equipped with flexible and elastic sensors are designed. To evaluate the effectiveness of our method, we conducted comparative experiments, pitting the proposed PSO-FSVM against other support vector machine variants, across four treadmill speeds. The experimental results indicate that the proposed method achieves an accuracy of over 98% at four different speeds indoors. Furthermore, the proposed method is compared with other algorithms (SVM, k-nearest neighbor (KNN), adaptive boosting (AdaBoost), and quadratic discriminant analysis (QDA)) under outdoor experiments. The experimental results demonstrate that the average recognition accuracy of this method reaches 96.13% under variable speed conditions, with an average accuracy of 98.06% under slow walking conditions, surpassing the performance of the above four algorithms.
Exoskeleton rehabilitation robots have been widely used in the rehabilitation treatment of stroke patients. Clinical studies confirmed that rehabilitation training with active movement intentions could improve the effectiveness of rehabilitation treatment significantly. This research proposes a real-time control method for an upper limb exoskeleton based on the active torque prediction model. To fulfill the goal of individualized and precise rehabilitation, this method has an adjustable parameter assist ratio that can change the strength of the assist torque under the same conditions. In this study, upper limb muscles’ EMG signals and elbow angle were chosen as the sources of control signals. The active torque prediction model was then trained using a BP neural network after appropriately extracting features. The model exhibited good accuracy on PC and embedded systems, according to the experimental results. In the embedded system, the RMSE of this model was 0.1956 N·m and 94.98%. In addition, the proposed real-time control system also had an extremely low delay of only 40 ms, which would significantly increase the adaptability of human–computer interactions.
