A combination of physical and cognitive training appears to be the effective intervention to improve cognitive function in older adults with mild cognitive impairment (MCI). Computing technology such as virtual reality (VR) may have the potential to assist rehabilitation in shaping brain health. However, little is known about the potential of VR-based physical and cognitive training designed as an intervention for cognition and brain activation in elderly patients with MCI. Moreover, whether a VR program designed around functional tasks can improve their instrumental activities of daily living (IADL) requires further investigation. This study investigated the effects of 12 weeks of VR-based physical and cognitive training on cognitive function, brain activation and IADL and compared the VR intervention with combined physical and cognitive training. A single-blinded randomized controlled trial. Communities and day care centers in Taipei, Taiwan. Older adults with mild cognitive impairment. Thirty-four community-dwelling older adults with MCI were randomized into either a VR-based physical and cognitive training (VR) group or a combined physical and cognitive training (CPC) group for 36 sessions over 12 weeks. Participants were assessed for their cognitive function (global cognition, executive function and verbal memory) and IADL at pre- and postintervention. Changes in prefrontal cortex activation during the global cognition test were also captured by functional near-infrared spectroscopy (NIRS) to identify the potential mediating pathway of the intervention. Both groups showed improved executive function and verbal memory (immediate recall). However, only the VR group showed significant improvements in global cognition (P<0.001), verbal memory (delayed recall, P=0.002), and IADL (P<0.001) after the intervention. The group × time interaction effects further demonstrated that IADL were more significantly improved with VR training than with CPC training (P=0.006). The hemodynamic data revealed decreased activation in prefrontal areas after training (P=0.0015), indicative of increased neural efficiency, in the VR-trained subjects. VR-based physical and cognitive training improves cognitive function, IADL and neural efficiency in older adults with MCI. VR training could be implemented for older adults with MCI.

Background: Walking while performing cognitive and motor tasks simultaneously interferes with gait performance and may lead to falls in older adults with mild cognitive impairment (MCI). Executive function, which seems to play a key role in dual-task gait performance, can be improved by combined physical and cognitive training. Virtual reality (VR) has the potential to assist rehabilitation, and its effect on physical and cognitive function requires further investigation. The purpose of this study was to assess the effects of VR-based physical and cognitive training on executive function and dual-task gait performance in older adults with MCI, as well as to compare VR-based physical and cognitive training with traditional combined physical and cognitive training. Method: Thirty-four community-dwelling older adults with MCI were randomly assigned into either a VR-based physical and cognitive training (VR) group or a combined traditional physical and cognitive training (CPC) group for 36 sessions over 12 weeks. Outcome measures included executive function [Stroop Color and Word Test (SCWT) and trail making test (TMT) A and B], gait performance (gait speed, stride length, and cadence) and dual-task costs (DTCs). Walking tasks were performed during single-task walking, walking while performing serial subtraction (cognitive dual task), and walking while carrying a tray (motor dual task). The GAIT Up system was used to evaluate gait parameters including speed, stride length, cadence and DTCs. DTC were defined as 100 * (single-task gait parameters − dual-task gait parameters)/single-task gait parameters. Results: Both groups showed significant improvements in the SCWT and single-task and motor dual-task gait performance measures. However, only the VR group showed improvements in cognitive dual-task gait performance and the DTC of cadence. Moreover, the VR group showed more improvements than the CPC group in the TMT-B and DTC of cadence with borderline significances. Conclusion: A 12-week VR-based physical and cognitive training program led to significant improvements in dual-task gait performance in older adults with MCI, which may be attributed to improvements in executive function.

The aim of this study was to conduct a comprehensive analysis of the placement of multiple wearable sensors for the purpose of analyzing and classifying the gaits of patients with neurological disorders. Seven inertial measurement unit (IMU) sensors were placed at seven locations: the lower back (L5) and both sides of the thigh, distal tibia (shank), and foot. The 20 subjects selected to participate in this study were separated into two groups: stroke patients (11) and patients with neurological disorders other than stroke (brain concussion, spinal injury, or brain hemorrhage) (9). The temporal parameters of gait were calculated using a wearable device, and various features and sensor configurations were examined to establish the ideal accuracy for classifying different groups. A comparison of the various methods and features for classifying the three groups revealed that a combination of time domain and gait temporal feature-based classification with the Multilayer Perceptron (MLP) algorithm outperformed the other methods of feature-based classification. The classification results of different sensor placements revealed that the sensor placed on the shank achieved higher accuracy than the other sensor placements (L5, foot, and thigh). The placement-based classification of the shank sensor achieved 89.13% testing accuracy with the Decision Tree (DT) classifier algorithm. The results of this study indicate that the wearable IMU device is capable of differentiating between the gait patterns of healthy patients, patients with stroke, and patients with other neurological disorders. Moreover, the most favorable results were reported for the classification that used the combination of time domain and gait temporal features as the model input and the shank location for sensor placement.

This study aimed to find the correlation between conventional Arch Index (AI) measurements and our prototype of a simplified insole-based plantar pressure measurement system and to find out the effective plantar pressure sensor position. Twenty-one subjects participated in this study, which was divided into two groups: 10 subjects with flatfoot and 11 subjects with normal foot. Five force sensitive resistance sensors were used on this prototype using Arduino as the data acquisition device. Two types of trials, namely static and dynamic, were conducted to validate our system against the ink-type AI measurement as a golden standard. The results showed that in the static trial, there was a high linear correlation with the medial arch sensor configuration, while in the dynamic trial, there was a high linear correlation in the medial arch sensor configuration and sensor 5 configuration. This study showed that both static and dynamic tests using the self-developed device could effectively determine most of the flatfoot subjects and suggests that in the future, it can be applied in clinical applications because of its advantages when compared to the expensive-high tech graphic input board and conventional tools, like ink-type based measurements.

Head orientation prediction is one of the solutions to reduce end-to-end latency on Virtual Reality (VR) systems and is important since it can alleviate negative effects like motion sickness. This study compared head orientation prediction models from two different electromyography (EMG) systems: surface EMG (sEMG) and High-Density EMG (HD-EMG). The deep learning method was used to train the prediction model, and the results showed that the model with input from the pre-processed sEMG + IMU sensor outperformed the model with input from the HD-EMG + IMU sensor. However, the decreasing performance from HD-EMG was compensated by its comfort and the ease of use of its electrode. This tradeoff between performance and usability with sEMG compared to HD-EMG should be a consideration for users who want to choose between performance and ease of use for head orientation prediction purposes. Comparison with state-of-the-art head prediction methods proved that the sEMG-based model offers better performance in predictions when users change their head directions, which was quantified by calculating the dt peaks. In other words, our sEMG-based prediction model is suitable for VR applications, which require the user to perform high-intensity or abrupt movements, such as in FPS games or exercise/sports games.