Hand tremor suppression device for patients suffering from Parkinson’s disease

2020 ◽  
Vol 44 (4) ◽  
pp. 190-197
Author(s):  
Mohammad Shah Faizan ◽  
Mohammad Muzammil
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Hand Tremor ◽  
Tremor Suppression

scholarly journals A mobile app for tracking non-motor symptoms of people with Parkinson’s disease: A usability study

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 890-890
Author(s):  
JuHee Lee ◽  
Yujin Suh ◽  
Yielin Kim
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Tracking System ◽  
Smart Phone ◽  
Telephone Counseling ◽  
Mobile App ◽  
Motor Symptoms ◽  
Hand Tremor ◽  
Non Motor Symptoms

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.

Family history of hand tremor in patients with early 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
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Family History ◽  
Hand Tremor ◽  
History Of ◽  
Early Parkinson’S Disease

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
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Hand Tremor ◽  
Wireless Accelerometer

scholarly journals A Mobile Application for Smart Computer-Aided Self-Administered Testing of Cognition, Speech, and Motor Impairment

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3236 ◽  
Author(s):  
Andrius Lauraitis ◽  
Rytis Maskeliūnas ◽  
Robertas Damaševičius ◽  
Tomas Krilavičius
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Energy Expenditure ◽  
Huntington's Disease ◽  
Huntington’S Disease ◽  
Mobile Application ◽  
Speech Analysis ◽  
Finger Tapping ◽  
Hand Tremor ◽  
Neural Impairment

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.

24-h Assessment of Tremor Activity and Posture in Parkinson's Disease by Multi-Channel Accelerometry

1999 ◽  
Vol 13 (4) ◽  
pp. 245-256 ◽  
Author(s):  
M. Smeja ◽  
F. Foerster ◽  
G. Fuchs ◽  
D. Emmans ◽  
A. Hornig ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Ambulatory Monitoring ◽  
Emotional Reactions ◽  
Task Demands ◽  
Diurnal Changes ◽  
Postural Tremor ◽  
Hand Tremor ◽  
Left Hand ◽  
Psychophysiological Investigation

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.

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