Design and Preliminary Performance Assessment of a Wearable Tremor Suppression Glove

Zhou, Yue; Ibrahim, Abu Bakar; Hardy, Kenneth G.; Jenkins, Mary E.; Naish, Michael D.; Trejos, Ana Luisa

doi:10.1109/tbme.2021.3080622

Cited by 19 publications

(13 citation statements)

References 46 publications

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“…A recent study assessed a WTSD on an individual with PD reported that a weight of 580 g is acceptable. 13 5. An optimal control system that considers the dynamic model of the WTSD for accurate motion control.…”

Section: Discussionmentioning

confidence: 99%

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Survey-based identification of design requirements and constraints for a wearable tremor suppression device

Zhou

Box

Hardy

et al. 2022

Journal of Rehabilitation and Assistive Technologies Engineerin

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Introduction Parkinsonian tremor has severely impacted the lives of 65% of individuals with Parkinson’s disease, and nearly 25% do not respond to traditional treatments. Although wearable tremor suppression devices (WTSDs) have become a promising alternative approach, this technology is still in the early stages of development, and no studies have reported the stakeholders’ opinions on this technology and their desired design requirements. Methods An online survey was distributed to affected Canadians and Canadian movement disorder specialists (MDS) to acquire information on demographics, the current state of treatments, opinions on the WTSDs, and the desired design requirements of future WTSDs. Results A total of 101 affected individuals and 24 MDS completed the survey. It was found that both groups are generally open to using WTSDs to manage tremor. The most important design requirement to end users is the adaptability to lifestyle, followed by weight and size, accurate motion, comfort, safety, quick response, and cost. Lastly, most of the participants (65%) think that the device should cost under $500. Conclusions The findings from this study can be used as guidelines for the development of future WTSDs, such that the future generations could be evaluated and accepted by the end users.

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Section: Discussionmentioning

confidence: 99%

“…A recent study assessed a WTSD on an individual with PD reported that a weight of 580 g is acceptable. 13 …”

Section: Discussionmentioning

confidence: 99%

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Survey-based identification of design requirements and constraints for a wearable tremor suppression device

Zhou

Box

Hardy

et al. 2022

Journal of Rehabilitation and Assistive Technologies Engineerin

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“…In addition to the traditional treatments, an emerging tremor management approach has been proposed and studied. This approach suppresses tremor by applying external stimulation, such as mechanical loading [5]- [10] and electrical stimulation [11]- [13], to the target joints and muscles. Although traditional treatments are the mainstay of treatment for managing tremor, the development of a new approach is considered indispensable given the side effects and complications induced by traditional treatments [14].…”

Section: Introductionmentioning

confidence: 99%

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

Zhou¹,

Habibollahi

Ibrahim

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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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 benchtop 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.

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“…In many wearable robotic tremor suppression devices, IMUs are being used [ 27 , 28 , 29 ]. Alternatively, using sEMG data means that parts of the sensing system can be moved from the hands onto the forearms to reduce the required hardware within the device.…”

Section: Introductionmentioning

confidence: 99%

Using Deep Learning for Task and Tremor Type Classification in People with Parkinson’s Disease

Farhani

Zhou

Jenkins

et al. 2022

Sensors

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Hand tremor is one of the dominating symptoms of Parkinson’s disease (PD), which significantly limits activities of daily living. Along with medications, wearable devices have been proposed to suppress tremor. However, suppressing tremor without interfering with voluntary motion remains challenging and improvements are needed. The main goal of this work was to design algorithms for the automatic identification of the tremor type and voluntary motions, using only surface electromyography (sEMG) data. Towards this goal, a bidirectional long short-term memory (BiLSTM) algorithm was implemented that uses sEMG data to identify the motion and tremor type of people living with PD when performing a task. Moreover, in order to automate the training process, hyperparamter selection was performed using a regularized evolutionary algorithm. The results show that the accuracy of task classification among 15 people living with PD was 84±8%, and the accuracy of tremor classification was 88±5%. Both models performed significantly above chance levels (20% and 33% for task and tremor classification, respectively). Thus, it was concluded that the trained models, based on using purely sEMG signals, could successfully identify the task and tremor types.

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Design and Preliminary Performance Assessment of a Wearable Tremor Suppression Glove

Cited by 19 publications

References 46 publications

Survey-based identification of design requirements and constraints for a wearable tremor suppression device

Survey-based identification of design requirements and constraints for a wearable tremor suppression device

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

Using Deep Learning for Task and Tremor Type Classification in People with Parkinson’s Disease

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