A systematic review of impairment focussed technology in neurology

Characterization and classification of kinesthetic motor imagery levels

Objective.Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals.Approach.A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0%, 10%, 40%, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbors (kNN).Main results.A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN.Significance.Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.

Efficacy assessment of virtual reality therapy for neuromotor rehabilitation in home environment: a systematic review

PURPOSE

Neuromotor impairments can affect any part of the body. It leads to many disorders, injuries, or disabilities, conventional rehabilitation is a long, rigorous, and tedious process. There is a need to inculcate new and innovative techniques in rehabilitation processes, to increase individuals' interest and overall performance with such therapies. Virtual Reality is considered a new technology that has the potential to be a useful aid in overcoming therapeutic problems in clinical and home-based environments. However, providing home-based rehabilitation is more practicable, cost-effective, and even safer than in-hospital rehabilitation. In addition, the need for home-based rehabilitation is growing as the number of neuromotor disorders rises and the capacity of acute inpatient rehabilitation decreases. Therefore, the main objective of this study was to assess the efficacy of a home-based Virtual Reality exercise treatment to identify the areas for future rehabilitation research.

MATERIALS AND METHODS

Data Extraction of 24,257 articles from seven databases were identified and the review is narrowed down and only 45 studies were focussed on efficacy assessment of Virtual Reality in the home environment.

RESULTS

The significant outcome of the effective home-based therapy system for the exercise improved functional ability, increasing range of motion, and motivation through Virtual reality-based rehabilitation is inferred.

CONCLUSION

Unlike clinical settings, a home-based system provides efficacious therapy with a controlled environment. This survey facilitates bettering methods and devices for neuromotor disorders. It is a good living long-term problem-solving approach and investigates awareness, needs, and a preferred component of home-based rehabilitation services.IMPLICATIONS FOR REHABILITATIONVR-based rehabilitation in the home environment has many physical and mental benefits in persons with neuromotor disorders.The most commonly neuromotor disorders considered in the study were Stroke, Spinal Cord Injury, Parkinson's disease, and Cerebral Palsy.Assistive technologies in home environments can compensate for long-term disorders or be used in rehabilitation as an addition to conventional therapy.The study gives an overview of current interventions and how they can be of benefit for a person suffering from neuromotor disorders in the home environment.

A wearable tool for continuous monitoring of movement disorders: clinical assessment and comparison with tremor scores

BACKGROUND

The current gold standard for evaluating normal and impaired motor performances includes the use of the information provided by the patient and the Unified Parkinson's Disease Rating Scale (UPDRS). However, clinical rating scales are typically subjective and their time-limited duration may fail to capture daily fluctuations in motor symptoms resulting from Parkinson's disease. Recently, a new tool has been proposed for objective and continuous assessment of movement disorders based on the evaluation of frequential data content from multi-axial sensors and the identification of specific movement patterns typically associated with disorders. This reduces the probability of confusing physiological or pathological movements occurring at the same frequency with a different movement pattern. However, the data provided by the tool have not yet been compared with the information provided by the typically used clinical rating scales.

OBJECTIVES

The aim of this work is to investigate the possible relationship between UPRDS scores and the information provided by the tool for continuous and long-term monitoring.

MATERIALS AND METHODS

In this study, 20 patients with hand tremor were recruited. The UPDRS scoring was performed by a neurologist. Then, continuous monitoring was performed; data were acquired by means of the proposed wrist-worn-device "PD-Watch" for 24 h and then processed in order to get information and indexes on motor symptoms. Finally, these indexes were correlated to the UPDRS scores.

RESULTS

Results show that the concise indexes provided by the tool correlate well with some items in UPDRS Part III, and this correlation has allowed to provide a more direct and immediate meaning to the values of the concise indexes detected by the tool.

CONCLUSIONS

While results need to be extended with further studies, this can be considered useful information in the context of clinical trials and routine clinical practice for assessing motor symptoms and movement disorders.