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.

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%.

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.

Park J, et al. J Clin Neurol. 2020 Oct;16(4):633-645. https://doi.org/10.3988/jcn.2020.16.4.633

The effect of music therapy on cognitive function has been widely reported; however, its clinical implications remain controversial. Performing therapeutic musical activities in groups using individualized instruments can help overcome the issues of engagement and compliance. We aimed to evaluate the effect of a cognitive intervention with musical stimuli using digital devices on mild cognitive impairment (MCI). In this prospective study, 24 patients with MCI (intervention group, 12; and control group, 12) were enrolled. We developed an electronic device with musical instruments and the Song-based Cognitive Stimulation Therapy protocol (SongCST). Patients in the intervention group underwent a 10-week cognitive intervention involving musical stimuli generated by our device. Effect of the intervention on cognitive function was evaluated by the Mini-Mental State Examination-Dementia Screening (MMSE-DS), Montreal Cognitive Assessment-Korean (MOCA-K), and Clinical Dementia Rating Scale Sum of Boxes (CDR-SOB). In the intervention group, MMSE-DS and MOCA-K scores improved significantly after the 10-week intervention. The changes in MOCA-K and CDR-SB scores were significantly different between the intervention and control groups. Our study showed that music therapy with digital devices has a positive effect on the executive function and overall disease severity in patients with MCI. Our study can facilitate individualization of music therapy using digital devices in groups.