Tremor Control Devices for Essential Tremor: A Systematic Literature Review

Castrillo-Fraile, Victoria; Peña, Elena Casas; Galán, José María Trejo Gabriel y; Delgado-López, Pedro David; Collazo, Carla; Cubo, Esther

doi:10.7916/tohm.v0.688

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…The drawbacks of medications and DBS motivate the development of alternative treatment options. One prospective alternative is wearable active mechanical tremor suppression, whereby actuators create exoskeleton-like systems that apply torques about joints to reduce tremor [9][10][11][12][13][14][15][16][17][18][19][20]. This approach has a major advantage over typical treatments: robust mechanical suppression of motion in the tremor range ensures effective treatment for any patient.…”

Section: Introductionmentioning

confidence: 99%

Towards wearable tremor suppression using dielectric elastomer stack actuators

Kelley

Kauffman

2020

Smart Mater. Struct.

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Active wearable tremor suppression devices apply actuators to the human body to produce joint torques that reduce tremor motion. This potential alternative to medications and surgery has the advantage of providing robust tremor treatment that is non-invasive, but the bulkiness of typical engineering actuators currently prohibits clinical implementations. Dielectric elastomer stack actuators (DESAs) offer a potential pathway towards achieving soft, low-profile tremor suppression devices: DESAs have similar mechanical properties as human muscles and can conform to the human limb. However, low actuation levels and a lack of commercial availability limit the development of DESA-based orthoses. Employing a control approach that only suppresses tremor while allowing the actuators to follow voluntary motion passively significantly decreases actuation requirements to improve potential for clinical devices. Still, DESAs that may offer the necessary actuation characteristics require specialized equipment and techniques. This research advances DESA-based tremor suppression by experimentally demonstrating DESA-based suppression of tremor-like signals on a scaled system using easily manufactured DESAs. Further discussion quantifies the DESA parameters that will enable physical implementations of human-scale tremor suppression and identifies pathways towards achieving those parameters.

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Section: Introductionmentioning

confidence: 99%

Towards wearable tremor suppression using dielectric elastomer stack actuators

Kelley

Kauffman

2020

Smart Mater. Struct.

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“…In the case of active assistive upper-limb exoskeletons, mechanical actuators counteract tremor by applying opposite forces to the involuntary movements [24]. This application illustrates the importance of accurate estimation of the tremor component in order to synchronize the actuators to only interfere with the involuntary actions and minimize the disturbance of voluntary movements [25]. Different algorithms based on traditional time and frequency signal processing have been proposed for modeling and estimation of tremor, such as the Weighted Fourier Linear Combiner (WFLC), an adaptive tremor estimator based on the sinusoidal model [26]; or Wavelet Adaptive Kalman Estimation (WAKE) frameworks, which combine an adaptive Kalman filter followed by a wavelet transform to estimate tremor in realtime [27].…”

Section: Introductionmentioning

confidence: 91%

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

Pascual-Valdunciel

Lopo-Martínez

Sendra-Arranz

et al. 2022

IEEE J. Biomed. Health Inform.

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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 shortterm memory (LSTM) neural networks to predict tremor signals using kinematic data recorded from Essential Tremor (ET) patients. A dataset comprising wrist flexionextension 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.

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“…This article summarizes the results of studies that evaluate noninvasive peripheral electrical stimulation for the reduction of pathological upper limb tremor in ET and PD. While previous reviews have focused on the technical aspects of stimulation [24] and on stimulation in the context of broader wearable technology solutions and robotics [25][26][27][28], this review provides a translational perspective and evaluates the clinical readiness for these novel techniques to be deployed into clinical practice. The review first describes the strength of evidence and clinical translatability of state-of-the-art PES systems.…”

Section: Introductionmentioning

confidence: 99%