The Feasibility of Longitudinal Upper Extremity Motor Function Assessment Using EEG

A novel scoring approach for the Wolf Motor Function Test in stroke survivors using motion-sensing technology and machine learning: A preliminary study

BACKGROUND AND OBJECTIVE

Human-administered clinical scales, such as the Functional Ability Scale of the Wolf Motor Function Test (WMFT-FAS), are widely utilized to evaluate upper-limb motor function in stroke survivors. However, these scales are generally subjective and labor-intensive. To end this, we proposed a novel scoring approach for the motor function assessment.

METHODS

The proposed novel scoring approach mainly contained one Microsoft Kinect v2, one customized motion tracking system, and one customized intelligent scoring system. Specifically, the Kinect v2 was used to capture stroke survivors' functional movements, the motion tracking system was developed for recording the gathered movement data, and the intelligent scoring system (kernel: feed-forward neural network, FFNN) was developed to evaluate movement quality and provide corresponding WMFT-FAS scores. Several methods have been applied to enhance the approach's usability, such as singular spectrum analysis and multi-ReliefF method.

RESULTS

Sixteen stroke survivors and ten healthy subjects were recruited for validation. Inspiring results of the proposed approach were achieved when compared with the clinical scores provided by a physiotherapist: 0.924 ± 0.027 for accuracy, 0.875 ± 0.063 for F1-score, 0.915 ± 0.051 for sensitivity, 0.969 ± 0.013 for specificity, 0.952 ± 0.038 for AUC, 0.098 ± 0.037 for mean absolute error, and 0.214 ± 0.078 for root mean squared error.

CONCLUSIONS

The results indicate that the proposed novel scoring approach can provide objective and accurate assessment scores, which can help physiotherapists make individualized treatment decisions.

Monitoring the Impact of Spaceflight on the Human Brain

Extended exposure to radiation, microgravity, and isolation during space exploration has significant physiological, structural, and psychosocial effects on astronauts, and particularly their central nervous system. To date, the use of brain monitoring techniques adopted on Earth in pre/post-spaceflight experimental protocols has proven to be valuable for investigating the effects of space travel on the brain. However, future (longer) deep space travel would require some brain function monitoring equipment to be also available for evaluating and monitoring brain health during spaceflight. Here, we describe the impact of spaceflight on the brain, the basic principles behind six brain function analysis technologies, their current use associated with spaceflight, and their potential for utilization during deep space exploration. We suggest that, while the use of magnetic resonance imaging (MRI), positron emission tomography (PET), and computerized tomography (CT) is limited to analog and pre/post-spaceflight studies on Earth, electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and ultrasound are good candidates to be adapted for utilization in the context of deep space exploration.

[Research progress on intelligent assessment system for upper limb function of stroke patients]

At present, the upper limb function of stroke patients is often assessed clinically using a scale method, but this method has problems such as time-consuming, poor consistency of assessment results, and high participation of rehabilitation physicians. To overcome the shortcomings of the scale method, intelligent upper limb function assessment systems combining sensors and machine learning algorithms have become one of the hot research topics in recent years. Firstly, the commonly used clinical upper limb functional assessment methods are analyzed and summarized. Then the researches on intelligent assessment systems in recent years are reviewed, focusing on the technologies used in the data acquisition and data processing parts of intelligent assessment systems and their advantages and disadvantages. Lastly, the current challenges and future development directions of intelligent assessment systems are discussed. This review is hoped to provide valuable reference information for researchers in related fields.