Tremor is a common symptom shared in both Parkinson's disease (PD) and Essential tremor (ET) subjects. The differential diagnosis of PD and ET tremor is important since the realization of treatment depends on specific medication. A novel feature is developed based on a hypothesis that tremor of PD subject has a larger fluctuation during resting than action task. Tremor signal is collected using a triaxial gyroscope sensor attached to subject's finger during kinetic and resting task. The angular velocity signal is analyzed by transforming a one-dimensional to two-dimensional signal using a relation of signal and its delay versions. Tremor fluctuation is defined as the area of 95% confidence ellipse covering the two-dimensional signal. The tremor fluctuation during kinetic and resting task is used as classification features. The support vector machine is used as a classifier and tested with 10-fold cross-validation. This novel feature provides a perfect PD/ET classification with 100% accuracy, sensitivity and specificity.

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Fig. 1. Location and nomenclature of the 10-10 system as standardized by American Clinical Neurophysiology Society. A = Ear lobe, AF = anterior frontal, C = central, CP = centroparietal, F = frontal, FC = frontocentral, FT = frontotemporal, N = nasion, O = occipital, P = parietal, PO = parietooccipital, T = temporal [3].Abstract-Electroencephalogram (EEG) is a non-invasive test that measures electrical activity in the brain. The source of EEG activity is the voltage differences within neurons of the brain. Therefore, it is a reflection of the synchronous activity of neurons. EEG activity shows oscillations at a variety of frequencies. This rhythmic activity is divided into bands by frequency and usually associated with different states of brain functioning. EEG is a valuable tool for clinical and research uses in many scientific fields. However, traditional devices are usually cost thousands of dollars and the preparation process is time-consuming. In recent years, newer EEG devices are introduced for consumer use and currently available on the market. The devices use dry electrodes and send signal via wireless, thus easier to use and more comfortable to wear. They are also considerably cheaper, cost around a few hundred dollars. In this paper, we used a consumer EEG device to record the brainwave of Buddhist monks during meditation and other activities. We then analyzed the recordings and demonstrated that an inexpensive device has enough features and can also be used as a tool for research as well. Muse from InteraXon Inc. was chosen as a consumer EEG device for our experiment. The device has a total of seven EEG sensors capable of reading four channels of data with active noise suppression. It also provides additional information, such as eye blink and jaw clench, for further analysis. The preliminary results show that the device can effectively record an EEG signal and could potentially be used as a research tool.

Surface electromyography (sEMG) is a non-invasive and straightforward way to allow the user to actively control the prosthesis. However, results reported by previous studies on using sEMG for hand and wrist movement classification vary by a large margin, due to several factors including but not limited to the number of classes and the acquisition protocol. The objective of this paper is to investigate the deep neural network approach on the classification of 41 hand and wrist movements based on the sEMG signal. The proposed models were trained and evaluated using the publicly available database from the Ninapro project, one of the largest public sEMG databases for advanced hand myoelectric prosthetics. Two datasets, DB5 with a low-cost 16 channels and 200 Hz sampling rate setup and DB7 with 12 channels and 2 kHz sampling rate setup, were used for this study. Our approach achieved an overall accuracy of 93.87 ± 1.49 and 91.69 ± 4.68% with a balanced accuracy of 84.00 ± 3.40 and 84.66 ± 4.78% for DB5 and DB7, respectively. We also observed a performance gain when considering only a subset of the movements, namely the six main hand movements based on six prehensile patterns from the Southampton Hand Assessment Procedure (SHAP), a clinically validated hand functional assessment protocol. Classification on only the SHAP movements in DB5 attained an overall accuracy of 98.82 ± 0.58% with a balanced accuracy of 94.48 ± 2.55%. With the same set of movements, our model also achieved an overall accuracy of 99.00% with a balanced accuracy of 91.27% on data from one of the amputee participants in DB7. These results suggest that with more data on the amputee subjects, our proposal could be a promising approach for controlling versatile prosthetic hands with a wide range of predefined hand and wrist movements.

In this study, we investigated the relationship between finger tapping tasks on the smartphone and the MDS-UPDRS I–II and PDQ-8 using the mPower dataset. mPower is a mobile application-based study for monitoring key indicators of PD progression and diagnosis. Currently, it is one of the largest, open access, mobile Parkinson’s Disease studies. Data from seven modules with a total of 8,320 participants who provided the data of at least one task were released to the public researcher. The modules comprise demographics, MDS-UPDRS I–II, PDQ-8, memory, tapping, voice, and walking. Finger-tapping is one of the tasks that easy to perform and has been analyzed for the quantitative measurement of PD. Therefore, participants who performed both the tapping activity and MDS-UPDRS I–II rating scale were selected for our analysis. Note that the MDS-UPDRS mPower Survey only contains parts of the original scale and has not been clinimetrically tested for validity and reliability. We obtained a total of 1851 samples that contained the tapping activity and MDS-UPDRS I–II for the analysis. Nine features were selected to represent tapping activity. K-mean was applied as an unsupervised clustering algorithm in our study. For determining the number of clusters, the elbow method, Sihouette score, and Davies–Bouldin index, were employed as supporting evaluation metrics. Based on these metrics and expert opinion, we decide that three clusters were appropriate for our study. The statistical analysis found that the tapping features could separate participants into three severity groups. Each group has different characteristics and could represent different PD severity based on the MDS-UPDRS I–II and PDQ-8 scores. Currently, the severity assessment of a movement disorder is based on clinical observation. Therefore, it is highly dependant on the skills and experiences of the trained movement disorder specialist who performs the procedure. We believe that any additional methods that could potentially assist with quantitative assessment of disease severity, without the need for a clinical visit would be beneficial to both the healthcare professionals and patients.

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The proposed device could non-destructively detect the water content in organic solvents at low concentrations with high accuracy and without any specific reagent. It could determine the water content in methanol, ethanol, and isopropanol at 0–1% w/w.