Wireless accelerometer configuration for monitoring Parkinson’s disease hand tremor

2013 ◽  
Vol 02 (02) ◽  
pp. 62-67 ◽  
Author(s):  
Robert LeMoyne ◽  
Timothy Mastroianni ◽  
Warren Grundfest
2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 890-890
Author(s):  
JuHee Lee ◽  
Yujin Suh ◽  
Yielin Kim

Abstract Smart phone-based technology for people with Parkinson’s disease has been developed worldwide. Unmonitored non-motor symptoms decrease quality of life of people with Parkinson’s disease, so the needs for technology to manage non-motor symptoms are increasing. The technology is needed to detect subtle changes in non-motor symptoms by healthcare professional. There is no mobile app which manage comprehensive symptoms of Parkinson’s disease including non-motor symptoms. It is necessary to develop a new tracking system that can effectively manage non-motor symptoms as well as motor symptoms of Parkinson’s disease. We developed a prototype of mobile app for Android smartphones, with cooperation with Mazelone company. we also have shaped functions for monitoring of motor symptoms and medication adherence. It also provided a section for caregivers to use on behalf of people with Parkinson’s disease who have difficulty to use app due to hand tremor. Through Delphi technique, we obtained content validity from eight medical and nursing experts on the contents of the application. We provided regular telephone counseling to improve and encourage their app usage. Fifteen participants used the app for 6 weeks. To evaluate usability of mobile app, we provided constructed questionnaire and conducted individual telephone interview. A mobile app for tracking non-motor symptoms demonstrated high usability and satisfaction. We learned lessons about facilitators and barriers when implementing an app such as perception and acceptance of mobile technology. The mobile app will improve continuum of care. Future studies need to improve the contents and refine technical approach for people with Parkinson’s disease.


2021 ◽  
Vol 90 ◽  
pp. 161-164
Author(s):  
Seong-Min Choi ◽  
Soo Hyun Cho ◽  
Kyung Wook Kang ◽  
Jae-Myung Kim ◽  
Byeong C. Kim

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3236 ◽  
Author(s):  
Andrius Lauraitis ◽  
Rytis Maskeliūnas ◽  
Robertas Damaševičius ◽  
Tomas Krilavičius

We present a model for digital neural impairment screening and self-assessment, which can evaluate cognitive and motor deficits for patients with symptoms of central nervous system (CNS) disorders, such as mild cognitive impairment (MCI), Parkinson’s disease (PD), Huntington’s disease (HD), or dementia. The data was collected with an Android mobile application that can track cognitive, hand tremor, energy expenditure, and speech features of subjects. We extracted 238 features as the model inputs using 16 tasks, 12 of them were based on a self-administered cognitive testing (SAGE) methodology and others used finger tapping and voice features acquired from the sensors of a smart mobile device (smartphone or tablet). Fifteen subjects were involved in the investigation: 7 patients with neurological disorders (1 with Parkinson’s disease, 3 with Huntington’s disease, 1 with early dementia, 1 with cerebral palsy, 1 post-stroke) and 8 healthy subjects. The finger tapping, SAGE, energy expenditure, and speech analysis features were used for neural impairment evaluations. The best results were achieved using a fusion of 13 classifiers for combined finger tapping and SAGE features (96.12% accuracy), and using bidirectional long short-term memory (BiLSTM) (94.29% accuracy) for speech analysis features.


1999 ◽  
Vol 13 (4) ◽  
pp. 245-256 ◽  
Author(s):  
M. Smeja ◽  
F. Foerster ◽  
G. Fuchs ◽  
D. Emmans ◽  
A. Hornig ◽  
...  

Abstract This study describes a new method, based on accelerometry, which quantifies tremor activity and posture continuously. A total of 25 right-handed patients with Parkinson's disease were recorded in a rest condition and in a postural tremor test, and during 24-h ambulatory monitoring. The tremor parameters, such as amplitude, frequency, and occurrence (percent of time), were derived by joint amplitude-frequency analysis. The DC components of multi-channel accelerometry allowed the detection of posture. A repeated measurement MANOVA was used to test the effects of posture and night-day differences in tremor activity. Further issues included consistencies of amplitude measurements across hands, between tasks, and between segments of recordings. Findings indicated an increase between resting tremor and postural tremor in the three tremor parameters, an increase under distraction, and enhanced activity in sitting compared to standing/walking. The best predictions of daytime monitoring measures, based on resting measures, were made for left hand tremor. This methodology is suitable for the detection of diurnal changes in tremor activity, especially amplitude changes, and for the psychophysiological investigation of enhanced tremor caused by task demands and emotional reactions.


Author(s):  
Khoi Ly ◽  
Aimee Cloutier ◽  
James Yang

Parkinson’s disease (PD) is difficult to detect before the onset of symptoms; further, PD symptoms share characteristics with symptoms of other diseases, making diagnosis of PD a challenging task. Without proper diagnosis and treatment, PD symptoms including tremor, bradykinesia, and cognitive problems deteriorate quickly into patients’ late life. Among them, the most distinguishable manifestations of PD are rest and postural tremor. Tremor is defined as an involuntary shaking or quivering movement of the hands or feet. Unified Parkinson’s Disease Rating Scale (UPDRS), Hoehn and Yahr (H&Y) scales are the most common rating scales that quantify the severity of PD. Due to the lack of consistency in these diagnostic tests, researchers are looking for devices for quantification and detection that can provide more objective PD motor assessments. Additionally, since there is currently no cure for PD, temporary PD symptom suppression is an active research area for improving patients’ quality of life. In this survey, the current state of research on Parkinson’s disease hand tremor quantification, detection, and suppression is discussed, especially focusing on electromechanical devices. The future direction of research on these devices is also considered.


2019 ◽  
Vol 90 (e7) ◽  
pp. A12.2-A12
Author(s):  
Shoaib Dal ◽  
Scott Whyte

IntroductionWe describe two cases of valproate-induced parkinsonism, where the parkinsonian features develop after commencing valproate (VPA), in a time frame and manner well described by previous publications.1 In contrast to the published literature, that has short duration of follow-up, we have had a prolonged follow-up of these two cases, who initially improved on ceasing valproate, but then after a period progressed again, with the development of levodopa-responsive asymmetrical Parkinson’s disease.2Cases67 years old man, on VPA 500 mg twice a day for seizures since 2008, developed new rest, re-emergent left hand tremor and hypomimia in 2012. Within months, the parkinsonism progressed to decremental bradykinesia, cog-wheel rigidity and hypophonia. The symptoms initially improved with changing VPA to levetiracetam; however, the disease reappeared after 6 months and although levodopa-responsive, it has been progressive since then.88 years old man, on VPA 700 mg twice a day for seizures since 2010, developed upper limb decremental bradykinesia, cog-wheel rigidity, camptocormia and rest, re-emergent tremor in hands in 2011. The parkinsonism improved significantly with cessation of VPA; however, the disease re-emerged after 2 years and has been progressive since then.ConclusionVPA may be responsible for unmasking underlying subclinical parkinsonism and these patients represent an earlier onset of Parkinson’s disease related to VPA exposure, rather than a purely drug related parkinsonian dysfunction. These cases also highlight the importance of long-term follow up in VPA-induced parkinsonism. These observations are an important consideration for clinicians treating patients with parkinsonian features secondary to VPA exposure.ReferencesBrugger F, Bhatia KP, Besag FM. Valproate-associated parkinsonism: a critical review of the literature. CNS Drugs 2016;30(6):527–540.Mahmoud F, Tampi RR. Valproic acid-induced parkinsonism in the elderly: a comprehensive review of the literature. Am J Geriatr Pharmacother 2011;9(6):405–412.


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