Pathological Tremor Research Articles

An OpenSim-Based Closed-Loop Biomechanical Wrist Model for Subject-Specific Pathological Tremor Simulation.

A pathological tremor (PT) is an involuntary rhythmic movement of varying frequency and amplitude that affects voluntary motion, thus compromising individuals' independence. A comprehensive model incorporating PT's physiological and biomechanical aspects can enhance our understanding of the disorder and provide valuable insights for therapeutic approaches. This study aims to build a biomechanical model of pathological tremors using OpenSim's realistic musculoskeletal representation of the human wrist with two degrees of freedom. We implemented a Matlab/OpenSim interface for a forward dynamics simulation, which allows for the modeling, simulation, and design of a physiological H∞ closed-loop control. This system replicates pathological tremors similar to those observed in patients when their arm is extended forward, the wrist is pronated, and the hand is subject to gravity forces. The model was individually tuned to five subjects (four Parkinson's disease patients and one diagnosed with essential tremor), each exhibiting distinct tremor characteristics measured by an inertial sensor and surface EMG electrodes. Simulation agreement with the experiments for EMGs, central frequency, joint angles, and angular velocities were evaluated by Jensen-Shannon divergence, histogram centroid error, and histogram intersection. The model emulated individual tremor statistical characteristics, including muscle activations, frequency, variability, and wrist kinematics, with greater accuracy for the four Parkinson's patients than the essential tremor. The proposed model replicated the main statistical features of subject-specific wrist tremor kinematics. Our methodology may facilitate the design of patient-specific rehabilitation devices for tremor suppression, such as neural prostheses and electromechanical orthoses.

Tremor Suppression Using Functional Electrical Stimulation.

Parkinson's disease (PD) and essential tremor are two major causes of pathological tremor among people over 60 years old. Due to the side effects and complications of traditional tremor management methods such as medication and deep brain surgery, non invasive tremor suppression methods have become more popular in recent years. Functional electrical stimulation (FES) is one of the methods used to reduce tremor in several studies. However, the effect of different FES parameters on tremor suppression and discomfort level, including amplitude, the number of pulses in each stimulation burst, frequency, and pulse width is yet to be studied for longer stimulation durations. Therefore, in this work, experiments were performed on 14 participants with PD to evaluate the effect of thirty seconds of out-of-phase electrical stimulation on wrist tremor at rest. Trials were conducted by varying the stimulation amplitude and the number of pulses while keeping the frequency and pulse width constant. Each test was repeated three times for each participant. The results showed an overall tremor suppression for 11 out of 14 participants and no average positive effects for three participants. It is concluded that despite the effectiveness of FES in tremor suppression, each set of FES parameters showed different suppression levels among participants due to the variability of tremor over time. Thus, for this method to be effective, an adaptive control system would be required to tune FES parameters in real time according to changes in tremor during extended stimulation periods.

Towards a Modular Pathological Tremor Simulation System Based on the Stewart Platform.

Wearable technologies have aided in reducing pathological tremor symptoms through non-intrusive solutions that aim to identify patterns in involuntary movements and suppress them using actuators positioned at specific joints. However, during the development of these devices, tests were primarily conducted on patients due to the difficulty of faithfully simulating tremors using simulation equipment. Based on studies characterizing tremors in Parkinson's disease, the development of a robotic manipulator based on the Stewart platform was initiated, with the goal of satisfactorily simulating resting tremor movements in the hands. In this work, a simulator was implemented in a computational environment using the multibody dynamics method. The platform structure was designed in a virtual environment using SOLIDWORKS® v2017 software and later exported to Matlab® R17a software using the Simulink environment and Simscape multibody library. The workspace was evaluated, and the Kalman filter was used to merge acceleration and angular velocity data and convert them into data related to the inclination and rotation of real patients' wrists, which were subsequently executed in the simulator. The results show a high correlation and low dispersion between real and simulated signals, demonstrating that the simulated mechanism has the capacity to represent Parkinson's disease resting tremors in all wrist movements. The system could contribute to conducting tremor tests in suppression devices without the need for the presence of the patient and aid in comparing suppression techniques, benefiting the development of new wearable devices.

Analysis of Adaptive Algorithms Based on Least Mean Square Applied to Hand Tremor Suppression Control

The increase in life expectancy, according to the World Health Organization, is a fact, and with it rises the incidence of age-related neurodegenerative diseases. The most recurrent symptoms are those associated with tremors resulting from Parkinson’s disease (PD) or essential tremors (ETs). The main alternatives for the treatment of these patients are medication and surgical intervention, which sometimes have restrictions and side effects. Through computer simulations in Matlab software, this work investigates the performance of adaptive algorithms based on least mean squares (LMS) to suppress tremors in upper limbs, especially in the hands. The signals resulting from pathological hand tremors, related to PD, present components at frequencies that vary between 3 Hz and 6 Hz, with the more significant energy present in the fundamental and second harmonics, while physiological hand tremors, referred to ET, vary between 4 Hz and 12 Hz. We simulated and used these signals as reference signals in adaptive algorithms, filtered-x least mean square (Fx-LMS), filtered-x normalized least mean square (Fx-NLMS), and a hybrid Fx-LMS–NLMS purpose. Our results showed that the vibration control provided by the Fx-LMS–LMS algorithm is the most suitable for physiological tremors. For pathological tremors, we used a proposed algorithm with a filtered sinusoidal input signal, Fsinx-LMS, which presented the best results in this specific case.

Bilateral transcranial direct current stimulation may be a feasible treatment of Parkinsonian tremor.

Parkinsonian tremor is a common pathological tremor that affects over 6 million people worldwide. It lowers patients' quality of life and threatens their career development, especially when patients' occupation requires dexterous manipulation. In spite of current available treatments in clinics, there is a lack of low-cost, low side-effect, effective solutions for Parkinsonian tremor. Transcranial direct current stimulation (tDCS) may be an alternative treatment. In this research, we explored the immediate effect of tDCS with a novel bilateral electrode setup over Parkinsonian tremor. In such a bilateral setup, the cathode was placed over the primary cortex contralateral to the more affected side of Parkinsonian tremor while the anode symmetrically over the other hemisphere. It was designed as a modification to the traditional cathodal setup. The performance of this bilateral setup was compared with three other setups including anodal setup, cathodal setup, and sham (control). A randomized, sham-controlled, double-blind, crossover experiment was carried out over 13 qualified patients diagnosed with idiopathic Parkinson's disease (PD). Before and after the stimulus of each tDCS setup, subjects were tested before and after tDCS with four measures, including the Unified Parkinson's Disease Rating Scale (UPDRS), Fahn-Tolosa-Marin Tremor Rating Scale (FTMTRS), Purdue Pegboard Test (PPT) and a self-design Continuous Tremor Signal Assessment (CTSA). Tremor intensity calculated from CTSA data were regarded as the primary outcome of the experiment. Statistical results of CTSA, FTMTRS and PPT showed both bilateral tDCS and cathodal tDCS effectively suppressed Parkinsonian tremor. A quantitative comparison of the effect in tremor suppression indicated the optimal suppressive effect was obtained with bilateral tDCS. Based on the results of UPDRS, anodal tDCS was found to benefit subjects' overall performance the most, however, it had little effect in improving Parkinsonian tremor, as revealed by the results of other evaluations. Our study suggests a beneficial immediate effect of bilateral tDCS in Parkinsonian tremor suppression. In addition, we assume there may be an underlying interhemispheric unbalance of cortical excitability which contributes to Parkinsonian tremor genesis. Identifier: ChiCTR2100054804.

Design and efficiency evaluation of a new wearable device for multi-degree suppression of hand pathological tremor

Sufferers of onset upper limb tremors find difficulty performing daily tasks due to rapid involuntary movements. Traditional treatments being offered help alleviate symptoms but are costly and can be invasive. Wearable technology advancements have introduced the applications of tremor suppression devices. A low-cost wearable bracelet was developed for sufferers of Parkinson’s disease and essential tremors to target rest and active tremors. Real-life measured tremor spectrums were used as input signals to design a dynamic vibration absorber. An innovative multiple-slot design allowed for multiple frequency suppression in the range of 3–8 Hz. A finite element analysis simulated the response of the upper limb with and without the bracelet. The results showed an overall suppression decreased tremors by up to 68% and targeted suppression decreased tremors by up to 98.2%.

Diagnostic Uncertainties in Tremor

The approach and diagnosis of patients with tremor may be challenging for clinicians. According to the most recent consensus statement by the Task Force on Tremor of the International Parkinson Movement Disorder Society, the differentiation between action (i.e., kinetic, postural, intention), resting, and other task- and position-specific tremors is crucial to this goal. In addition, patients with tremor must be carefully examined for other relevant features, including the topography of the tremor, since it can involve different body areas and possibly associate with neurological signs of uncertain significance. Following the characterization of major clinical features, it may be useful to define, whenever possible, a particular tremor syndrome and to narrow down the spectrum of possible etiologies. First, it is important to distinguish between physiological and pathological tremor, and, in the latter case, to differentiate between the underlying pathological conditions. A correct approach to tremor is particularly relevant for appropriate referral, counseling, prognosis definition, and therapeutic management of patients. The purpose of this review is to outline the possible diagnostic uncertainties that may be encountered in clinical practice in the approach to patients with tremor. In addition to an emphasis on a clinical approach, this review discusses the important ancillary role of neurophysiology and innovative technologies, neuroimaging, and genetics in the diagnostic process.

A Real-Time Bionic Method Inspired by Neural Oscillators for Estimation and Extraction of Pathological Tremor.

The typical representative of pathological tremor is Parkinson's disease. One of the pathogenesis is that the synchronized neural oscillations within and between brain areas are affected. Inspired by this, this work proposes an algorithm based on neural oscillator to extract voluntary motion and estimate tremor motion in real time, which is named as RTBNO. This algorithm is composed of multiple adaptive modified Hopf oscillators linear combiner. The combiner is divided into two parts: one is used to estimate tremor motion and the other is applied to estimate voluntary motion. As it is updated iteratively in real time, this method has no phase delay. The performance of the proposed method was verified by the simulated action tremor and the actual experimental results of twenty Parkinson's disease patients. For the rest tremor signals of patients, the mean Root Mean Square Error (RMSE) values between the estimated signal and the actual signal was 0.0272±0.0077. The mean RMSE values between the estimated voluntary movement from action tremor and the actual voluntary movement were 0.0360±0.0097 (pick and put motion) and 0.0380±0.0083 (drawing motion). The execution time for the corresponding 10 seconds data was 0.0478s. The comparison results between the proposed method and the existing methods demonstrated the effectiveness of the proposed method.

Modulation of spinal circuits following phase-dependent electrical stimulation of afferent pathways

Objective. Peripheral electrical stimulation (PES) of afferent pathways is a tool commonly used to induce neural adaptations in some neural disorders such as pathological tremor or stroke. However, the neuromodulatory effects of stimulation interventions synchronized with physiological activity (closed-loop strategies) have been scarcely researched in the upper-limb. Here, the short-term spinal effects of a 20-minute stimulation protocol where afferent pathways were stimulated with a closed-loop strategy named selective and adaptive timely stimulation (SATS) were explored in 11 healthy subjects. Approach. SATS was applied to the radial nerve in-phase (INP) or out-of-phase (OOP) with respect to the muscle activity of the extensor carpi radialis (ECR). The neural adaptations at the spinal cord level were assessed for the flexor carpi radialis (FCR) by measuring disynaptic Group I inhibition, Ia presynaptic inhibition, Ib facilitation from the H-reflex and estimation of the neural drive before, immediately after, and 30 minutes after the intervention. Main results. SATS strategy delivered electrical stimulation synchronized with the real-time muscle activity measured, with an average delay of 17 ± 8 ms. SATS-INP induced increased disynaptic Group I inhibition (77 ± 23% of baseline conditioned FCR H-reflex), while SATS-OOP elicited the opposite effect (125 ± 46% of baseline conditioned FCR H-reflex). Some of the subjects maintained the changes after 30 minutes. No other significant changes were found for the rest of measurements. Significance. These results suggest that the short-term modulatory effects of phase-dependent PES occur at specific targeted spinal pathways for the wrist muscles in healthy individuals. Importantly, timely recruitment of afferent pathways synchronized with specific muscle activity is a fundamental principle that shall be considered when tailoring PES protocols to modulate specific neural circuits. (NCT number 04501133).

Reference Values of the Forearm Tremor Power Spectra for Youth Athletes.

The aim of the study was to determine reference graphs of power spectral density functions of forearm physiological tremor and to compare their parameters in the male and female population of young athletes from various sports. One hundred fifty-nine (159) female (15.7 ± 2.1 years, 59.8 ± 8.1 kg, 169.1 ± 7.5 cm) and 276 male (16.4 ± 1.9 years 72.7 ± 10.3 kg and 180.9 ± 8.7 cm) youth athletes participated in the study. Forearm tremor was measured accelerometrically in a sitting position. Power spectrum density (PSD) function was calculated for each individual tremor waveform. Because of right skewness of power distribution, the PSD functions were subjected to logarithmic transformation. Average log-powers in low (2-4 Hz) and high (8-14 Hz) frequency ranges and mean frequencies in those ranges were analyzed. Tremor log-powers for male were greater than for female athletes (p < 0.001), while frequencies of spectrum maxima did not differ from each other. Frequencies of spectrum maxima correlated (p < 0.001) with age (r = 0.277 and 0.326 for males and females, respectively). The obtained reference functions may be utilized in order to quantify and assess tremor size and its changes evoked by stress and fatigue, which can be applied for selection and training monitoring in sports, but also in medicine for detection and diagnosis of pathologic tremor in young individuals.

Classification of Kinematic and Electromyographic Signals Associated with Pathological Tremor Using Machine and Deep Learning.

Peripheral Electrical Stimulation (PES) of afferent pathways has received increased interest as a solution to reduce pathological tremors with minimal side effects. Closed-loop PES systems might present some advantages in reducing tremors, but further developments are required in order to reliably detect pathological tremors to accurately enable the stimulation only if a tremor is present. This study explores different machine learning (K-Nearest Neighbors, Random Forest and Support Vector Machines) and deep learning (Long Short-Term Memory neural networks) models in order to provide a binary (Tremor; No Tremor) classification of kinematic (angle displacement) and electromyography (EMG) signals recorded from patients diagnosed with essential tremors and healthy subjects. Three types of signal sequences without any feature extraction were used as inputs for the classifiers: kinematics (wrist flexion-extension angle), raw EMG and EMG envelopes from wrist flexor and extensor muscles. All the models showed high classification scores (Tremor vs. No Tremor) for the different input data modalities, ranging from 0.8 to 0.99 for the f1 score. The LSTM models achieved 0.98 f1 scores for the classification of raw EMG signals, showing high potential to detect tremors without any processed features or preliminary information. These models may be explored in real-time closed-loop PES strategies to detect tremors and enable stimulation with minimal signal processing steps.

Non-linear time series analysis of Parkinsonian tremor signals

Nonlinear time series analysis of Parkinsonian tremor signals involves exploring the complex dynamics and nonlinear interactions within the tremor signals to gain insights into the underlying physiological processes. By applying advanced analytical techniques, researchers aim to uncover hidden patterns, chaotic behavior, and self-organization within the signals, which can provide valuable information for the diagnosis and monitoring of Parkinson's disease. The vibrational phenomena studied in this work regards the arm and forearm vibration with the purpose to detect and recognize the dynamic properties and correlations of onset of pathological tremor in patients affected by Parkinson disease. Experimental data measured by patients will be analyzed using multiscale recursive analysis methodologies through the TISEAN package (1). Recurrence plot analysis is a method that visually represents the recurrent patterns and dependencies within a time series and helps to identify important features such as deterministic dynamics, periodicity, and phase relationships (2,3).

Prediction of Pathological Tremor Signals Using Long Short-Term Memory Neural Networks.

Previous implementations of closed-loop peripheral electrical stimulation (PES) strategies have provided evidence about the effect of the stimulation timing on tremor reduction. However, these strategies have used traditional signal processing techniques that only consider phase prediction and might not model the non-stationary behavior of tremor. Here, we tested the use of long short-term memory (LSTM) neural networks to predict tremor signals using kinematic data recorded from Essential Tremor (ET) patients. A dataset comprising wrist flexion-extension data from 12 ET patients was pre-processed to feed the predictors. A total of 180 models resulting from the combination of network (neurons and layers of the LSTM networks, length of the input sequence and prediction horizon) and training parameters (learning rate) were trained, validated and tested. Predicted tremor signals using LSTM-based models presented high correlation values (from 0.709 to 0.998) with the expected values, with a phase delay between the predicted and real signals below 15 ms, which corresponds approximately to 7.5% of a tremor cycle. The prediction horizon was the parameter with a higher impact on the prediction performance. The proposed LSTM-based models were capable of predicting both phase and amplitude of tremor signals outperforming results from previous studies (32--56% decreased phase prediction error compared to the out-of-phase method), which might provide a more robust PES-based closed-loop control applied to PES-based tremor reduction.

Thalamo-cortical network is associated with harmaline-induced tremor in rodent model

Essential tremor (ET) is the most frequent form of pathologic tremor and one of the most common adult-onset neurologic impairments. However, underlying mechanisms by which structural alterations within the tremor circuit generate the pathological state and how rhythmic neuronal activities propagate and drive tremor remains unclear. Harmaline (HA)-induced tremor model has been most frequently utilized animal model for ET studies, however, there is still a dearth of knowledge over the degree to whether HA-induced tremor mimics the actual underlying pathophysiology of ET, particularly the involvement of thalamo-cortical region. In this study, we investigated the electrophysiological response of the motor circuit including the ventrolateral thalamus (vlTh) and the primary motor cortex (M1), and the modulatory effect of thalamic deep brain stimulation (DBS) using a rat HA-induced tremor model. We found that the theta and high-frequency oscillation (HFO) band power significantly increased after HA administration in both vlTh and M1, and the activity was modulated by the tremor suppression drug (propranolol) and the thalamic DBS. The theta band phase synchronization between the vlTh and M1 was significantly enhanced during the HA-induced tremor, and the transition of cross-frequency coupling in vlTh was found to be associated with the state of HA-induced tremor. Our findings support that the HA tremor could be useful as a valid preclinical model of ET in the context of thalamo-cortical neural network interaction.

Discrimination between parkinsonian tremor and essential tremor using artificial neural network with hybrid features.

Essential tremor (ET) and the tremor in Parkinson's disease (PD) are the two most common pathological tremors with a certain overlap in the clinical presentation. The main purpose of this work is to use an artificial neural network to select the best features and to discriminate between the two types of tremors. The features used are of hybrid type obtained from two different algorithms: the statistical signal characterization (SSC) of the signal describing its morphology, and the soft-decision wavelet-decomposition (SDWD) features extracted from the accelerometer and surface EMG signals. The SSC method is used to obtain morphology-based features of the spectrum of the accelerometer and two surface EMG signals. The SDWD technique is used in this work to obtain the approximate spectral representation of both accelerometer and the two surface EMG signals. Two sets of data (training and test) are used in this paper. The training set consists of 21 ET subjects and 19 PD subjects, while the test set consists of 20 ET and 20 PD subjects. A neural network of the type feed forward back propagation has been used to combine best SSC features and best SDWD features of the accelerometer and EMG signals. Efficiency result of 92.5% was obtained using best hybrid features. The artificial neural network has been used successfully to combine two types of features in an automatic discrimination system between PD and ET.

Is essential tremor a degenerative or an electrical disorder? Electrical disorder.

Essential tremor (ET) is one of the most common movement disorders, yet we do not have a complete understanding of its pathophysiology. From a phenomenology standpoint, ET is an isolated tremor syndrome of bilateral upper limb action tremor with or without tremor in other body locations. ET is a pathological tremor that arises from excessive oscillation in the central motor network. The tremor network comprises of multiple brain regions including the inferior olive, cerebellum, thalamus, and motor cortex, and there is evidence that a dynamic oscillatory disturbance within this network leads to tremor. ET is a chronic disorder, and the natural history shows a slow progression of tremor intensity with age. There are reported data suggesting that ET follows the disease model of a neurodegenerative disorder, however whether ET is a degenerative or electrical disorder has been a subject of debate. In this chapter, we will review cumulative evidence that ET as a syndrome is a fundamentally electric disorder. The etiology is likely heterogenous and may not be primarily neurodegenerative.

Real-Time Performance Assessment of High-Order Tremor Estimators Used in a Wearable Tremor Suppression Device.

The side effects and complications of traditional treatments for treating pathological tremor have led to a growing research interest in wearable tremor suppression devices (WTSDs) as an alternative approach. Similar to how the human brain coordinates the function of the human system, a tremor estimator determines how a WTSD functions. Although many tremor estimation algorithms have been developed and validated, whether they can be implemented on a cost-effective embedded system has not been studied; furthermore, their effectiveness on tremor signals with multiple harmonics has not been investigated. Therefore, in this study, four tremor estimators were implemented, evaluated, and compared: Weighted-frequency Fourier Linear Combiner (WFLC), WFLC-based Kalman Filter (WFLC-KF), Band-limited Multiple FLC, and enhanced High-order WFLC-KF (eHWFLC-KF). This study aimed to evaluate the performance of each algorithm on a bench-top tremor suppression system with 18 recorded tremor motion datasets; and compare the performance of each estimator. The experimental evaluation showed that the eHWFLC-KF-based WTSD achieved the best performance when suppressing tremor with an average of 89.3% reduction in tremor power, and an average error when tracking voluntary motion of 6.6°/s. Statistical analysis indicated that the eHWFLC-KF-based WTSD is able to reduce the power of tremor better than the WFLC and WFLC-KF, and the BMFLC-based WTSD is better than the WFLC. The performance when tracking voluntary motion is similar among all systems. This study has proven the feasibility of implementing various tremor estimators in a cost-effective embedded system, and provided a real-time performance assessment of four tremor estimators.

Online Tracking of the Phase Difference Between Neural Drives to Antagonist Muscle Pairs in Essential Tremor Patients.

Transcutaneous electrical stimulation has been applied in tremor suppression applications. Out-of-phase stimulation strategies applied above or below motor threshold result in a significant attenuation of pathological tremor. For stimulation to be properly timed, the varying phase relationship between agonist-antagonist muscle activity during tremor needs to be accurately estimated in real-time. Here we propose an online tremor phase and frequency tracking technique for the customized control of electrical stimulation, based on a phase-locked loop (PLL) system applied to the estimated neural drive to muscles. Surface electromyography signals were recorded from the wrist extensor and flexor muscle groups of 13 essential tremor patients during postural tremor. The EMG signals were pre-processed and decomposed online and offline via the convolution kernel compensation algorithm to discriminate motor unit spike trains. The summation of motor unit spike trains detected for each muscle was bandpass filtered between 3 to 10 Hz to isolate the tremor related components of the neural drive to muscles. The estimated tremorogenic neural drive was used as input to a PLL that tracked the phase differences between the two muscle groups. The online estimated phase difference was compared with the phase calculated offline using a Hilbert Transform as a ground truth. The results showed a rate of agreement of 0.88 ± 0.22 between offline and online EMG decomposition. The PLL tracked the phase difference of tremor signals in real-time with an average correlation of 0.86 ± 0.16 with the ground truth (average error of 6.40° ± 3.49°). Finally, the online decomposition and phase estimation components were integrated with an electrical stimulator and applied in closed-loop on one patient, to representatively demonstrate the working principle of the full tremor suppression system. The results of this study support the feasibility of real-time estimation of the phase of tremorogenic neural drive to muscles, providing a methodology for future tremor-suppression neuroprostheses.

A Review on Wearable Technologies for Tremor Suppression.

Tremor is defined as a rhythmic, involuntary oscillatory movement of a body part. Although everyone exhibits a certain degree of tremor, some pathologies lead to very disabling tremors. These pathological tremors constitute the most prevalent movement disorder, and they imply severe difficulties in performing activities of daily living. Although tremors are currently managed through pharmacotherapy or surgery, these treatments present significant associated drawbacks: drugs often induce side effects and show decreased effectiveness over years of use, while surgery is a hazardous procedure for a very low percentage of eligible patients. In this context, recent research demonstrated the feasibility of managing upper limb tremors through wearable technologies that suppress tremors by modifying limb biomechanics or applying counteracting forces. Furthermore, recent experiments with transcutaneous afferent stimulation showed significant tremor attenuation. In this regard, this article reviews the devices developed following these tremor management paradigms, such as robotic exoskeletons, soft robotic exoskeletons, and transcutaneous neurostimulators. These works are presented, and their effectiveness is discussed. The article also evaluates the different metrics used for the validation of these devices and the lack of a standard validation procedure that allows the comparison among them.

Development of a Soft Exosuit for Suppressing Essential Tremor

Essential tremor, the most common pathological tremor, is assumed to originate in the central nervous system. It can result in functional disability because the tremors obstruct movement. We have developed a soft exosuit to assist essential tremor patients that provides a comfortable, unobtrusive and soft interface to the human body, and evaluated whether it can constrain joint movement and suppress tremors. We developed a prototype cable-driven exosuit based on three-point fixation that restricts joint motion only when necessary. Tremors were simulated in healthy test subjects using functional electrical stimulation. The experimental results show that the tremor intensity obtained with the exosuit in constrained mode was approximately 62% of that obtained in unconstrained mode. These results show that the cable-driven soft exosuit effectively suppressed joint motion in patients with simulated essential tremor. We also demonstrated that the exosuit could switch between constrained and unconstrained modes.