Improvisation of classification performance based on feature optimization for differentiation of Parkinson’s disease from other neurological diseases using gait characteristics

Most neurological disorders that include Parkinson’s disease (PD) as well as other neurological diseases such as Amyotrophic Lateral Sclerosis (ALS) and Huntington’s disease (HD) have some common abnormalities regarding the movement, vocal, and cognitive behaviors of sufferers. Variations in the manifestation of these types of abnormality help distinguish one disorder from another. In this study, differentiation was performed based on the gait characteristics of patients afflicted by different neurological disorders. In the recent past, many researchers have applied different machine learning and feature selection techniques to the classification of different groups of patients based on common abnormalities. However, in an era of modernization where the focus is on timely low-cost automatization and pattern recognition, such techniques require improvisation to provide high performance. We attempted to improve the performance of such techniques using different feature optimization methods, such as a genetic algorithm (GA) and principal component analysis (PCA), and applying different classification approaches, i.e., linear, nonlinear, and probabilistic classifiers. In this study, gait dynamics data of patients suffering with PD, ALS, and HD were collated from a public database, and a binary classification approach was used by taking PD as one group and adopting ALS+HD as another group. Performance comparison was achieved using different classification techniques that incorporated optimized feature sets obtained from GA and PCA. In comparison with other classifiers using different feature sets, the highest accuracy (97.87%) was obtained using random forest combined with GA-based feature sets. The results provide evidence that could assist medical practitioners in differentiating PD from other neurological diseases using gait characteristics

A Supervised Machine Learning Approach to Detect the On/Off State in Parkinson’s Disease Using Wearable Based Gait Signals

Fluctuations in motor symptoms are mostly observed in Parkinson’s disease (PD) patients. This characteristic is inevitable, and can affect the quality of life of the patients. However, it is difficult to collect precise data on the fluctuation characteristics using self-reported data from PD patients. Therefore, it is necessary to develop a suitable technology that can detect the medication state, also termed the “On”/“Off” state, automatically using wearable devices; at the same time, this could be used in the home environment. Recently, wearable devices, in combination with powerful machine learning techniques, have shown the potential to be effectively used in critical healthcare applications. In this study, an algorithm is proposed that can detect the medication state automatically using wearable gait signals. A combination of features that include statistical features and spatiotemporal gait features are used as inputs to four different classifiers such as random forest, support vector machine, K nearest neighbour, and Naïve Bayes. In total, 20 PD subjects with definite motor fluctuations have been evaluated by comparing the performance of the proposed algorithm in association with the four aforementioned classifiers. It was found that random forest outperformed the other classifiers with an accuracy of 96.72%, a recall of 97.35%, and a precision of 96.92%

A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer

One of the most common symptoms observed among most of the Parkinson’s disease patients that affects movement pattern and is also related to the risk of fall, is usually termed as “freezing of gait (FoG)”. To allow systematic assessment of FoG, objective quantification of gait parameters and automatic detection of FoG are needed. This will help in personalizing the treatment. In this paper, the objectives of the study are (1) quantification of gait parameters in an objective manner by using the data collected from wearable accelerometers; (2) comparison of five estimated gait parameters from the proposed algorithm with their counterparts obtained from the 3D motion capture system in terms of mean error rate and Pearson’s correlation coefficient (PCC); (3) automatic discrimination of FoG patients from no FoG patients using machine learning techniques. It was found that the five gait parameters have a high level of agreement with PCC ranging from 0.961 to 0.984. The mean error rate between the estimated gait parameters from accelerometer-based approach and 3D motion capture system was found to be less than 10%. The performances of the classifiers are compared on the basis of accuracy. The best result was accomplished with the SVM classifier with an accuracy of approximately 88%. The proposed approach shows enough evidence that makes it applicable in a real-life scenario where the wearable accelerometer-based system would be recommended to assess and monitor the FoG

ZnO-TiO₂ Core–Shell Nanowires: A Sustainable Photoanode for Enhanced Photoelectrochemical Water Splitting

We present the synthesis of a unique vertically aligned ZnO-TiO₂ core–shell nanowires (NWs) heterostructure on an Si-wafer using a chemical vapor deposition method. The structural study shows the well-developed ZnO-TiO₂ core–shell NWs heterostructure. This unique ZnO-TiO₂ core–shell NWs heterostructure displays a photocurrent density of 1.23 mA cm^–2, which is 2.41 times higher than pristine ZnO NWs. A cathodic shift in the flat band potential and a lower onset potential of a ZnO-TiO₂ core–shell NWs heterostructure over ZnO NWs indicates more favorable properties for photoelectrochemical water splitting with a photoconversion efficiencey of 0.53%. A higher photocurrent density/photoconversion efficiency is due to the effective addition of photogenerated electron–hole separation originating from the ZnO NWs core and the conformal covering of a amorphous TiO₂ passivation shell. Therefore, these results suggest that the vertically aligned one-dimentional ZnO-TiO₂ core–shell NWs heterostructure is a promising photoanode for solar energy conversion devices

Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson’s Patients

In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurological diseases. As patients suffering from Parkinson’s disease (PD) are more prone to a movement disorder, the quantification of gait characteristics helps in personalizing the treatment. The wearable sensors make the measurement process more convenient as well as feasible in a practical environment. However, the question remains to be answered about the validation of the wearable sensor-based measurement system in a real-world scenario. This paper proposes a study that includes an algorithmic approach based on collected data from the wearable accelerometers for the estimation of the gait characteristics and its validation using the Tinetti mobility test and 3D motion capture system. It also proposes a machine learning-based approach to classify the PD patients from the healthy older group (HOG) based on the estimated gait characteristics. The results show a good correlation between the proposed approach, the Tinetti mobility test, and the 3D motion capture system. It was found that decision tree classifiers outperformed other classifiers with a classification accuracy of 88.46%. The obtained results showed enough evidence about the proposed approach that could be suitable for assessing PD in a home-based free-living real-time environment

Validity and Reliability Study of the Korean Tinetti Mobility Test for Parkinson’s Disease

Objective Postural instability and gait disturbance are the cardinal symptoms associated with falling among patients with Parkinson’s disease (PD). The Tinetti mobility test (TMT) is a well-established measurement tool used to predict falls among elderly people. However, the TMT has not been established or widely used among PD patients in Korea. The purpose of this study was to evaluate the reliability and validity of the Korean version of the TMT for PD patients. Methods Twenty-four patients diagnosed with PD were enrolled in this study. For the interrater reliability test, thirteen clinicians scored the TMT after watching a video clip. We also used the test-retest method to determine intrarater reliability. For concurrent validation, the unified Parkinson’s disease rating scale, Hoehn and Yahr staging, Berg Balance Scale, Timed-Up and Go test, 10-m walk test, and gait analysis by three-dimensional motion capture were also used. We analyzed receiver operating characteristic curve to predict falling. Results The interrater reliability and intrarater reliability of the Korean Tinetti balance scale were 0.97 and 0.98, respectively. The interrater reliability and intra-rater reliability of the Korean Tinetti gait scale were 0.94 and 0.96, respectively. The Korean TMT scores were significantly correlated with the other clinical scales and three-dimensional motion capture. The cutoff values for predicting falling were 14 points (balance subscale) and 10 points (gait subscale). Conclusion We found that the Korean version of the TMT showed excellent validity and reliability for gait and balance and had high sensitivity and specificity for predicting falls among patients with PD