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Garcia-Higuera JA, Paredes-Acuna N, Le T, Auclair B, Tomaskovic K, Berberich N, Cheng G. Damping TENS-Induced Essential Tremor Symptoms in Activities of Daily Living Using the TuMove Wrist Exoskeleton. IEEE Int Conf Rehabil Robot 2023; 2023:1-6. [PMID: 37941195 DOI: 10.1109/icorr58425.2023.10304605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Essential Tremor (ET) is the most frequent movement disorder in adults. Upper-limb exoskeletons are a promising solution to alleviate ET symptoms. We propose a novel wrist exoskeleton for tremor damping. The TuMove exoskeleton is light-weight, portable, easy to use, and designed for ADLs and activities requiring hand dexterity. We validated the effectiveness of our exoskeleton by inducing forearm tremors using TENS on 5 healthy subjects. Our results show that wrist ranges are generally kept in most of the ROM needed in ADLs. The damping system reduced more than 30% of the tremor's angular velocity during drinking and pouring tasks. Furthermore, the completion time of the Archimedes spiral was decreased by 2.76 seconds (13.0%) and for the 9-Hole-Peg-Test by 2.77 seconds (11.8 %), indicating a performance improvement in dexterity tasks.
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Fujikawa J, Morigaki R, Yamamoto N, Nakanishi H, Oda T, Izumi Y, Takagi Y. Diagnosis and Treatment of Tremor in Parkinson's Disease Using Mechanical Devices. LIFE (BASEL, SWITZERLAND) 2022; 13:life13010078. [PMID: 36676025 PMCID: PMC9863142 DOI: 10.3390/life13010078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/09/2022] [Accepted: 12/23/2022] [Indexed: 12/29/2022]
Abstract
BACKGROUND Parkinsonian tremors are sometimes confused with essential tremors or other conditions. Recently, researchers conducted several studies on tremor evaluation using wearable sensors and devices, which may support accurate diagnosis. Mechanical devices are also commonly used to treat tremors and have been actively researched and developed. Here, we aimed to review recent progress and the efficacy of the devices related to Parkinsonian tremors. METHODS The PubMed and Scopus databases were searched for articles. We searched for "Parkinson disease" and "tremor" and "device". RESULTS Eighty-six articles were selected by our systematic approach. Many studies demonstrated that the diagnosis and evaluation of tremors in patients with PD can be done accurately by machine learning algorithms. Mechanical devices for tremor suppression include deep brain stimulation (DBS), electrical muscle stimulation, and orthosis. In recent years, adaptive DBS and optimization of stimulation parameters have been studied to further improve treatment efficacy. CONCLUSIONS Due to developments using state-of-the-art techniques, effectiveness in diagnosing and evaluating tremor and suppressing it using these devices is satisfactorily high in many studies. However, other than DBS, no devices are in practical use. To acquire high-level evidence, large-scale studies and randomized controlled trials are needed for these devices.
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Affiliation(s)
- Joji Fujikawa
- Department of Advanced Brain Research, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
| | - Ryoma Morigaki
- Department of Advanced Brain Research, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
- Department of Neurosurgery, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
- Parkinson’s Disease and Dystonia Research Center, Tokushima University Hospital, 2-50-1 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
- Correspondence: ; Tel.: +81-88-633-7149
| | - Nobuaki Yamamoto
- Department of Neurology, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
| | - Hiroshi Nakanishi
- Department of Neurosurgery, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
- Beauty Life Corporation, 2 Kiba-Cho, Minato-Ku, Nagoya 455-0021, Aichi, Japan
| | - Teruo Oda
- Department of Advanced Brain Research, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
| | - Yuishin Izumi
- Parkinson’s Disease and Dystonia Research Center, Tokushima University Hospital, 2-50-1 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
- Department of Neurology, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
| | - Yasushi Takagi
- Department of Advanced Brain Research, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
- Department of Neurosurgery, Institute of Biomedical Sciences, Graduate School of Medicine, Tokushima University, 3-18-15 Kuramoto-Cho, Tokushima-Shi 770-8503, Tokushima, Japan
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Farhani G, Zhou Y, Jenkins ME, Naish MD, Trejos AL. Using Deep Learning for Task and Tremor Type Classification in People with Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2022; 22:7322. [PMID: 36236422 PMCID: PMC9570986 DOI: 10.3390/s22197322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
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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Affiliation(s)
- Ghazal Farhani
- Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada
| | - Yue Zhou
- School of Biomedical Engineering, Western University, London, ON N6A 5B9, Canada
| | - Mary E. Jenkins
- Movement Disorders Program, Clinical Neurological Sciences, Western University, London, ON N6A 3K7, Canada
| | - Michael D. Naish
- Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada
- School of Biomedical Engineering, Western University, London, ON N6A 5B9, Canada
- Department of Mechanical and Materials Engineering, Western University, London, ON N6A 5B9, Canada
| | - Ana Luisa Trejos
- Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada
- School of Biomedical Engineering, Western University, London, ON N6A 5B9, Canada
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Zhou Y, Jenkins ME, Naish M, Trejos AL. Preliminary Assessment of the Safety of a Fault-Tolerant Control-based Wearable Tremor Suppression Glove . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2874-2877. [PMID: 36086514 DOI: 10.1109/embc48229.2022.9871546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The advent of wearable tremor suppression de-vices (WTSDs) has provided a promising alternative approach for parkinsonian tremor management, especially for individuals whose tremors are not managed by conventional treatment options. Currently, research in WTSDs has shown successful results with a tremor suppression ratio of up to 99 %; however, the user safety of WTSDs has not been properly considered, especially in the occurrence of unexpected events, such as faults and disturbances. In this study, a fault-tolerant control system was developed and integrated into the control system of a WTSD for the first time. The safety and tremor suppression performance of the proposed system under the influence of a measurement loss fault were tested and evaluated on 18 tremor motion datasets, specifically by quantifying the tremor power suppression ratio and the error when tracking voluntary motion. The experimental evaluation showed that the proposed system could remain functional and safe to use in the existence of the fault, with an average user motion tracking error of 1.5º. It was also found that the proposed system achieved significantly improved performance in both metrics when compared to the system without a fault-tolerant controller. Clinical Relevance-This work improves the safety and robustness of WTSDs making them more suitable for use as an additional treatment for parkinsonian tremor.
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Zhou Y, Box D, Hardy KG, Jenkins ME, Garland J, Naish MD, Trejos AL. Survey-based identification of design requirements and constraints for a wearable tremor suppression device. J Rehabil Assist Technol Eng 2022; 9:20556683221094480. [PMID: 35548101 PMCID: PMC9083043 DOI: 10.1177/20556683221094480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 03/30/2022] [Indexed: 11/17/2022] Open
Abstract
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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Affiliation(s)
- Yue Zhou
- School of Biomedical Engineering, Western University, London, ON, Canada
| | - Devin Box
- School of Kinesiology, Western University, London, ON, Canada
| | - Kenneth G Hardy
- Ivey Business School, Western University, London, ON, Canada
| | - Mary E Jenkins
- Movement Disorders Program, Clinical Neurological Sciences, Western University, London, ON, Canada
| | - Jayne Garland
- Faculty of Health Sciences, Western University, London, ON, Canada
| | - Michael D Naish
- School of Biomedical Engineering, Western University, London, ON, Canada
- Department of Mechanical and Materials Engineering, Western University, London, ON, Canada
- Department of Electrical and Computer Engineering, Western University, London, ON, Canada
| | - Ana Luisa Trejos
- School of Biomedical Engineering, Western University, London, ON, Canada
- Department of Electrical and Computer Engineering, Western University, London, ON, Canada
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