Precise hand movement telerehabilitation with virtual cubes for patients with Parkinson's disease

2016 ◽  
Author(s):  
Imre Cikajlo ◽  
Dejana Zajc ◽  
Irena Dolinšek ◽  
Tatjana Krizmanič ◽  
Alma Hukić ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Hand Movement

scholarly journals A spatio-spectral approach for movement decoding from cortical and subcortical recordings in patients with Parkinson's disease

2021 ◽  
Author(s):  
Victoria Peterson ◽  
Timon Merk ◽  
Alan Bush ◽  
Vadim Nikulin ◽  
Andrea A Kühn ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Spatial Information ◽  
Single Channel ◽  
Time Windows ◽  
Hand Movement ◽  
Electrode Position ◽  
Recording Site ◽  
Spatial Filters

The application of machine learning to intracranial signal analysis has the potential to revolutionize deep brain stimulation (DBS) by personalizing therapy to dynamic brain states, specific to symptoms and behaviors. Machine learning methods can allow behavioral states to be decoded accurately from intracranial local field potentials to trigger an adaptive DBS (aDBS) system, closing the loop between patients' needs and stimulation patterns. Most decoding pipelines for aDBS are based on single channel frequency domain features, neglecting spatial information available in multichannel recordings. Such features are extracted either from DBS lead recordings in the subcortical target and/or from electrocorticography (ECoG). To optimize the simultaneous use of both types of signals, we developed a supervised online-compatible decoding pipeline based on multichannel and multiple recording site recordings. We applied this pipeline to data obtained from 11 patients with Parkinson's disease performing a hand movement task during DBS surgery targeting the subthalamic nucleus, in which in addition a research temporary ECoG electrode was placed. Spectral and spatial features were extracted using filter-bank analysis and spatial pattern decomposition. The learned spatio-spectral features were used to train a generalized linear model with sparse regularized regression. We found that movement decoding was successful using 100 ms time windows, epoch time that is well-suited for aDBS applications. In addition, when 9 out of 16 features were selected, decoding performance was improved up to 15% when the multiple recording site features were used as compared to the single recording site approach. The prediction value was inversely correlated with both the UPDRS score and the distance of the ECoG electrode position to the hand knob motor cortex. Further evaluation of the selected features revealed that ECoG signals contribute more to decoding performance than subthalamic signals. This novel application of spatial filters to decode movement from combined cortical and subcortical recordings is an important step toward the use of machine learning for the construction of intelligent aDBS systems.

scholarly journals Remote Evaluation of Parkinson's Disease Using a Conventional Webcam and Artificial Intelligence

2021 ◽  
Vol 12 ◽  
Author(s):  
Mariana H. G. Monje ◽  
Sergio Domínguez ◽  
Javier Vera-Olmos ◽  
Angelo Antonini ◽  
Tiago A. Mestre ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Upper Limb ◽  
Rating Scale ◽  
Hand Movement ◽  
Movement Speed ◽  
Clinical Scales ◽  
Video Frames ◽  
Disease Rating

Objective: This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam.Methods: We consecutively enrolled a cohort of 42 patients with PD and healthy subjects (HSs). The participants were recorded performing MDS-UPDRS III bradykinesia upper limb tasks with a computer webcam. The video frames were processed using the artificial intelligence algorithms tracking the movements of the hands. The video extracted features were correlated with clinical rating using the Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale and inertial measurement units (IMUs). The developed classifiers were validated on an independent dataset.Results: We found significant differences in the motor performance of the patients with PD and HSs in all the bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed. The model correlated both with the IMUs for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with the existing methods.Conclusions: We present here the proof-of-concept of a novel webcam-based technology to remotely detect the parkinsonian features using artificial intelligence. This method has preliminarily achieved a very high diagnostic accuracy and could be easily expanded to other disease manifestations to support PD management.

5. Automatic evaluation of hand movement of visual target-tracking for patients with Parkinson’s disease

2008 ◽  
Vol 119 (6) ◽  
pp. e76
Author(s):  
Junko Ide ◽  
Takenao Sugi ◽  
Nobuya Murakami ◽  
Fumio Shima ◽  
Hiroshi Shibasaki ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Target Tracking ◽  
Visual Target ◽  
Hand Movement ◽  
Automatic Evaluation ◽  
Visual Target Tracking

scholarly journals A non-contact system for the assessment of hand motor tasks in people with Parkinson’s disease

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Fábio Henrique Monteiro Oliveira ◽  
Daniel Fernandes da Cunha ◽  
Amanda Gomes Rabelo ◽  
Luiza Maire David Luiz ◽  
Marcus Fraga Vieira ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Hand Movement ◽  
Electrical Potential ◽  
Body Part ◽  
The Body ◽  
Control Group ◽  
Motor Tasks ◽  
Micro Machine ◽  
Contact Sensors

AbstractClinical diagnosis of Parkinson’s disease (PD) motor symptoms remains a problem. Most of the current studies focus on objective evaluations to make the evaluation more reliable. Most of these systems are based on the use of inertial and electromyographic sensors that require contact with the body part being assessed. Contact sensors restrict natural movement, may be uncomfortable and may require preparation of the body, which may cause irritation. As an alternative to contact sensors for the study of hand motor tasks performed by subjects with and without PD, electrical potential sensing technology is used in this research. A custom hardware has been designed to enable data collection by hand movement. A micro-machine system validated the developed system, and a relationship model was established between hand displacement and non-contact capacitive (NCC) sensor response. An experiment was conducted, including 57 subjects, 30 with PD (experimental group) and 27 healthy control group, followed by an analysis of statistical features extracted from the instantaneous mean frequency (IMNF) of NCC sensor. These results were compared with those obtained from gyroscope signals that are considered in the field to be the gold standard. As a result, NCC responses were correlated linearly with hand displacement (R2 = 0.7692 and $${\text{R}}_{\text{adj}}^{2}$$ R adj 2  = 0.7631). The statistical evaluation of IMNF features showed, that both, contact and non-contact sensors, were able to discriminate movement patterns of the control group from the experimental one. The results confirm statistical similarity between features extracted from NCC and gyroscope signals.

scholarly journals Development of Smart Glove Rehabilitation Device (SGRD) for Parkinson's Disease

2019 ◽  
Vol 9 (2) ◽  
pp. 4512-4518
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Hand Movement ◽  
Applied Pressure ◽  
Cost Effective ◽  
Future Research ◽  
Hand Position ◽  
Accelerometer Sensor ◽  
Soft Actuator ◽  
Human Finger

Over the past decade, significant papers have shown that rehabilitation exercise is efficient in enhancing Parkinson's disease efficiency. However, the previous devices in Parkinson Disease Rehabilitation are not very efficient as they are far too complicated, heavy in size, and difficult to conduct. This paper will focus on developing a smart rehabilitation hand device prototype using an Arduino microcontroller, to control the soft actuator by using pneumatic system and IOT system for the individual with Parkinson's disease. The actuators are designed mechanically to match and support the human finger range as it features a lightweight structure, simplistic design, cost-effective and safer to use compared to other conventional actuators. A soft actuator, accelerometer sensor, pneumatic air valve and Arduino Mega were designed as a control hardware system to operate the smart rehabilitation glove. Therefore, this study will focus on obtaining data results based on the length of the single actuator, the bending angle of the actuator based on the applied pressure, the hand position of the accelerometer sensor based on the x, y, z-axis and the suitable pressure for the SGRD rehabilitation system for future research purposes. This prototype will assist the subject's hand movement by improving the subject quality in helping the patient with Parkinson's disorder recover.

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