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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.

Movement disorders are primarily associated with the basal ganglia and the thalamus; therefore, movement disorders are more frequently manifest after stroke compared with neurological injuries associated with other structures of the brain. Overall clinical features, such as types of movement disorder, the time of onset and prognosis, are similar with movement disorders after stroke in other structures. Dystonia and chorea are commonly occurring post-stroke movement disorders in basal ganglia circuit, and these disorders rarely present with tremor. Rarer movement disorders, including tic, restless leg syndrome, and blepharospasm, can also develop following a stroke. Although the precise mechanisms underlying the pathogenesis of these conditions have not been fully characterized, disruptions in the crosstalk between the inhibitory and excitatory circuits resulting from vascular insult are proposed to be the underlying cause. The GABA (gamma-aminobutyric acid)ergic and dopaminergic systems play key roles in post-stroke movement disorders. This review summarizes movement disorders induced by basal ganglia and thalamic stroke according to the anatomical regions in which they manifest.

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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.

There is an association between epileptiform discharges and SCM. Additionally, the involvement of the unilateral cortex and ipsilateral thalamus in SCM and its hyperperfusion state could be helpful in differentiating the consequences of epileptic seizures from other pathologies.

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