Predicting the occurrence of wrist tremor based on electromyography using a hidden Markov model and entropy based learning algorithm

Samaee, Saeedeh; Kobravi, Hamid Reza

doi:10.1016/j.bspc.2019.101739

Cited by 15 publications

(11 citation statements)

References 28 publications

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“…Indeed, robots can step up treatment, aid patient movement, and provide feedback. Systems dedicated to automated rehabilitation have benefited from numerous technological advances in robotics and information technologies [2][3][4][5][6]. In this context, we aim to take advantage of the progress made in mathematical modeling of human joints, the IoT-based system design, fuzzy logic-based decision-support systems to design a new wrist rehabilitation protocol based on a human wrist model.…”

Section: Introductionmentioning

confidence: 99%

“…As far as the market is concerned, rehabilitation systems are extremely large, bulky, immobile, and expensive. Due to the fact that the idea is intended to design small robots with a well-defined target that can be used at home, the proposed robotic solution is lightweight and portable as compared with earlier works [2][3][4][5]. Additionally, we propose a new design that reduces the number of motors in the robot.…”

Section: Introductionmentioning

confidence: 99%

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Design and Development of a Smart IoT-Based Robotic Solution for Wrist Rehabilitation

et al. 2022

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In this study, we present an IoT-based robot for wrist rehabilitation with a new protocol for determining the state of injured muscles as well as providing dynamic model parameters. In this model, the torque produced by the robot and the torque provided by the patient are determined and updated taking into consideration the constraints of fatigue. Indeed, in the proposed control architecture based on the EMG signal extraction, a fuzzy classifier was designed and implemented to estimate muscle fatigue. Based on this estimation, the patient’s torque is updated during the rehabilitation session. The first step of this protocol consists of calculating the subject-related parameters. This concerns axis offset, inertial parameters, passive stiffness, and passive damping. The second step is to determine the remaining component of the wrist model, including the interaction torque. The subject must perform the desired movements providing the torque necessary to move the robot in the desired direction. In this case, the robot applies a resistive torque to calculate the torque produced by the patient. After that, the protocol considers the patient and the robot as active and all exercises are performed accordingly. The developed robotics-based solution, including the proposed protocol, was tested on three subjects and showed promising results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Development of a Smart IoT-Based Robotic Solution for Wrist Rehabilitation

et al. 2022

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“…Mean Frequency [32], [33] MNF -Sample Entropy [26], [30] SampEn m = 2, r = 0.2 × σ Difference Absolute Standard Deviation Value [33], [34] DASDV -Difference of Maximum and Minimum Value [4], [35] DMMV -Energy [12], [26] ENE -Hjorth1 (Activity) or Variance [7], [36] Hjorth1 (or VAR) -Interquartile Range [7], [26] IQR [26] Kurt -Log Detector [27], [34] LD -Standard Deviation Value [5], [7] SD -Skewness [7], [18] Skew -Linear Prediction Coefficient 2 [16], [37] 2nd LPC -Linear Prediction Coefficient 3 [16], [37] 3rd LPC -Zero Crossing [7], [38] ZC threshold : 0.03 × σrest Slope Sign Change [7], [38] SSC threshold : 0.03 × σrest Spectral Entropy [26] SpEn -Simple Square Integral [27], [33] SSI -Waveform Length [18], [27], [28] WL -Auto-Regressive Coefficient 1 [39], [40] AR1 order : 4 Auto-Regressive Coefficient 2 [39], [40] AR2 order : 4 Auto-Regressive Coefficient 3 [39], [40] AR3 order : 4 Auto-Regressive Coefficient 4 [39], [40] AR4 order : 4 Maximum-to-M...…”

Section: Methodsmentioning

confidence: 99%

Automatic Upper-Limb Brunnstrom Recovery Stage Evaluation via Daily Activity Monitoring

Meng

Jiang

Qin

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Motor function assessment is crucial for post-stroke rehabilitation. Conventional evaluation methods are subjective, heavily depending on the experience of therapists. In light of the strong correlation between the stroke severity level and the performance of activities of daily living (ADLs), we explored the possibility of automatically evaluating the upperlimb Brunnstrom Recovery Stage (BRS) via three typical ADLs (tooth brushing, face washing and drinking). Multimodal data (acceleration, angular velocity, surface electromyography) were synchronously collected from 5 upper-limb-worn sensor modules. The performance of BRS evaluation system is known to be variable with different system parameters (e.g., number of sensor modules, feature types and classifiers). We systematically searched for the optimal parameters from different data segmentation strategies (five window lengths and four overlaps), 42 types of features, 12 feature optimization techniques and 9 classifiers with the leave-one-subject-out cross-validation. To achieve reliable and low-cost monitoring, we further explored whether it was possible to obtain a satisfactory result using a relatively small number of sensor modules. As a result, the proposed approach can correctly recognize the stages of all 27 participants using only three sensor modules with the optimized data segmentation parameters (window length: 7s, overlap: 50%), extracted features (simple square integral, slope sign change, modified mean absolute value 1 and modified mean absolute value 2), the feature optimization method (principal component analysis) and the logistic regression classifier. According to the literature, this is the first study to comprehensively optimize sensor configuration and parameters in each stage of the BRS

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“…The available rehabilitation systems are very large, complicated, immobile, unwieldy and very expensive. In contrast to the previously cited works [1][2][3]36,37], the proposed solution is light and portable since the objective was to design small robots with a well-defined target in order to be accessible for use at home. Compared to [14,[23][24][25][26], the developed system is a vision-based telemanipulation solution that avoids the inconveniences of the EMG-based solutions, such as signal sensitivity.…”

Section: Introductionmentioning

confidence: 99%

“…Robotics mainly focuses on the development of systems and control strategies to facilitate the recovery of lost motor skills. Systems dedicated to automated rehabilitation have benefited from numerous technological advances in robotics, such as sensors, actuators and control approaches [1][2][3][4][5][6][7]. In this context, we aimed to take advantage of the progress made in remote control systems, gestural control, IoT-based system design and fuzzy logic-based decision-support systems to design remote elbow rehabilitation solution.…”

Section: Introductionmentioning

confidence: 99%

Development of an IoT-Based Solution Incorporating Biofeedback and Fuzzy Logic Control for Elbow Rehabilitation

et al. 2020

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The last few years have seen significant advances in neuromotor rehabilitation technologies, such as robotics and virtual reality. Rehabilitation robotics primarily focuses on devices, control strategies, scenarios and protocols aimed at recovering sensory, motor and cognitive impairments often experienced by stroke victims. Remote rehabilitation can be adopted to relieve stress in healthcare facilities by limiting the movement of patients to clinics, mainly in the current COVID-19 pandemic. In this context, we have developed a remote controlled intelligent robot for elbow rehabilitation. The proposed system offers real-time monitoring and ultimately provides an electronic health record (EHR). Rehabilitation is an area of medical practice that treats patients with pain. However, this pain can prevent a person from positively interacting with therapy. To cope with this matter, the proposed solution incorporates a cascading fuzzy decision system to estimate patient pain. Indeed, as a safety measure, when the pain exceeds a certain threshold, the robot must stop the action even if the desired angle has not yet been reached. A fusion of sensors incorporating an electromyography (EMG) signal, feedback from the current sensor and feedback from the position encoder provides the fuzzy controller with the data needed to estimate pain. This measured pain is fed back into the control loop and processed to generate safe robot actions. The main contribution was to integrate vision-based gesture control, a cascade fuzzy logic-based decision system and IoT (Internet of Things) to help therapists remotely take care of patients efficiently and reliably. Tests carried out on three different subjects showed encouraging results.

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Predicting the occurrence of wrist tremor based on electromyography using a hidden Markov model and entropy based learning algorithm

Cited by 15 publications

References 28 publications

Design and Development of a Smart IoT-Based Robotic Solution for Wrist Rehabilitation

Design and Development of a Smart IoT-Based Robotic Solution for Wrist Rehabilitation

Automatic Upper-Limb Brunnstrom Recovery Stage Evaluation via Daily Activity Monitoring

Development of an IoT-Based Solution Incorporating Biofeedback and Fuzzy Logic Control for Elbow Rehabilitation

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