Influence of muscle fatigue on electromyogram-kinematic correlation during robot-assisted upper limb training

Preliminary Head-Supported Mass Performance Guidance for Dismounted Soldier Environments

INTRODUCTION

The helmet is an ideal platform to mount technology that gives U.S. Soldiers an advantage over the enemy; the total system is recognized quantitatively as head-supported mass (HSM). The stress placed on the head and neck is magnified by adding mass and increasing the center of mass offset away from the atlanto-occipital complex, the head's pivot point on the spine. Previous research has focused on HSM-related spinal degeneration and performance decrement in mounted environments. The increased capabilities and protection provided by helmet systems for dismounted Soldiers have made it necessary to determine the boundaries of HSM and center of mass offset unique to dismounted operations.

MATERIALS AND METHODS

A human subject volunteer study was conducted to characterize the head and neck exposures and assess the impact of HSM on performance in a simulated field-dismounted operating environment. Data were analyzed from 21 subjects who completed the Load Effects Assessment Program-Army obstacle course at Fort Benning, GA, while wearing three different experimental HSM configurations. Four variable groups (physiologic/biomechanical, performance, kinematic, and subjective) were evaluated as performance assessments. Weight moments (WMs) corresponding to specific performance decrement levels were calculated using the quantitative relationships developed between each metric and the study HSM configurations. Data collected were used to develop the performance decrement HSM threshold criteria based on an average of 10% total performance decrement of dismounted Soldier performance responses.

RESULTS

A WM of 134 N-cm about the atlanto-occipital complex was determined as the preliminary threshold criteria for an average of 10% total performance decrement. A WM of 164 N-cm was calculated for a corresponding 25% average total performance decrement.

CONCLUSIONS

The presented work is the first of its kind specifically for dismounted Soldiers. Research is underway to validate these limits and develop dismounted injury risk guidance.

Translation, Adaptation, and Psychometric Testing of the Almere Technology Acceptance Questionnaire (ATAQ) Among Older Adults in China

Background

Lack of information on technology Acceptance of Social Assistant robots (SARs) limits the application of robots in the elderly care sector. Currently, no study has not reported a robot acceptance assessment tool in China.

Purpose

The current study aimed to translate the Almere Technology Acceptance Questionnaire (ATAQ) from English to Mandarin Chinese, perform cross-cultural adaptations, and to evaluate its psychometric properties among elderly people in China.

Methods

This study was conducted in two phases. Phase 1 comprised the context relevance evaluation, instrument translation, and cultural adaption from English to Mandarin Chinese. Phase 2 involved a quantitative cross-sectional survey of psychometric testing among 317 elderly Chinese, including reliability and evaluation of construct validity.

Results

In Phase 1, the Context Relevance Index of all items was rated “3 = relevant” or “4 = totally relevant”, and the Translation Validity Index of the 100% items of the ATAQ was rated 3 or 4. The ATAQ-Chinese questionnaire comprised 30 items. In Phase 2, all items of the ATAQ-Chinese questionnaire had a CR above 3 (P<0.001). Correlation coefficients of entries in the current study ranged from 0.403 to 0.763 (P<0.001). Nine factors were extracted through Exploratory Factor Analysis, and the cumulative variance contribution rate was 77.175%. Confirmatory Factor Analysis showed that the model had a good fit (χ²/df=2.006, RMSEA=0.069, RMR=0.059, GFI=0.816, IFI=0.913, TLI=0.896, CFI=0.912). The content validity index was 0.92 indicating that all questions were relevant. The value of Cronbach’s alpha coefficient showed high validity (α= 0.945, 0.664–0.891). The test-retest reliability coefficient was 0.980, indicating that the tool was reliable.

Conclusion

The Chinese version of ATAQ has good reliability and validity, and it is an acceptable, reliable, and valid tool for determining technology acceptance of Social assistant robots in older adults.

EEG Spectral Feature Modulations Associated With Fatigue in Robot-Mediated Upper Limb Gross and Fine Motor Interactions

This paper investigates the EEG spectral feature modulations associated with fatigue induced by robot-mediated upper limb gross and fine motor interactions. Twenty healthy participants were randomly assigned to perform a gross motor interaction with HapticMASTER or a fine motor interaction with SCRIPT passive orthosis for 20 min or until volitional fatigue. Relative and ratio band power measures were estimated from the EEG data recorded before and after the robot-mediated interactions. Paired-samples t-tests found a significant increase in the relative alpha band power and a significant decrease in the relative delta band power due to the fatigue induced by the robot-mediated gross and fine motor interactions. The gross motor task also significantly increased the (θ + α)/β and α/β ratio band power measures, whereas the fine motor task increased the relative theta band power. Furthermore, the robot-mediated gross movements mostly changed the EEG activity around the central and parietal brain regions, whereas the fine movements mostly changed the EEG activity around the frontopolar and central brain regions. The subjective ratings suggest that the gross motor task may have induced physical fatigue, whereas the fine motor task may have induced mental fatigue. Therefore, findings affirm that changes to localised brain activity patterns indicate fatigue developed from the robot-mediated interactions. It can also be concluded that the regional differences in the prominent EEG spectral features are most likely due to the differences in the nature of the task (fine/gross motor and distal/proximal upper limb) that may have differently altered an individual's physical and mental fatigue level. The findings could potentially be used in future to detect and moderate fatigue during robot-mediated post-stroke therapies.

Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms

Accuracy is a key index of human gait recognition. In this paper, we propose an improved gait recognition algorithm, which combines multiple feature combination and artificial bee colony for optimizing the support vector machine (ABC-SVM). Firstly, considering the complexity characteristics of surface electromyography (sEMG) signals, four types of features are extracted from the denoised sEMG signals, including the time-domain features of integral of absolute value (IAV), variance (VAR), and number of zero-crossing (ZC) points, frequency-domain features of mean power frequency (MPF) and median frequency (MF), and wavelet features and fuzzy entropy features. Secondly, the classifiers of SVM, linear discriminant analysis (LDA), and extreme learning machine (ELM) are employed to recognize the gait with obtained features, including singe-class features, multiple combination features, and optimized features of dimension reduction by principal component analysis (PCA). Thirdly, the penalty coefficient and kernel function parameter of the SVM classifier are optimized by the ABC algorithm, and the influence of different features and classifiers on the recognition results is studied. Finally, the feature samples selected to construct the SVM classifier are trained and recognized. Results show that the classification performance of the ABC-SVM classifier is significantly better than that of the nonoptimized SVM classifier, and the average recognition rate is increased by 3.18%. In addition, the combined feature samples (time-domain, frequency-domain, wavelet, and fuzzy entropy features) not only improve the gait classification accuracy but also enhance the recognition stability.