Activity

The proposed approach deeply checked for their capability of prediction using cognitive scores and EEG measures. The highest accuracies obtained by k NN with 10-fold cross-validation for dementia, early dementia and healthy are 92.00%, 91.67% and 91.87%, respectively.

The essential findings of this study are 1) Experimental results indicate that k NN is superior over other classifier algorithms for dementia diagnosis. 2) CPT is the best predictor for healthy subjects. 3) FTT can be an essential test to diagnose significant dementia.

IF decomposition technique enhances the diagnostic accuracy even with a limited dataset.

IF decomposition technique enhances the diagnostic accuracy even with a limited dataset.

When evaluating methods for machine-learning controlled prosthetic hands, able-bodied participants are often recruited, for practical reasons, instead of participants with upper limb absence (ULA). However, able-bodied participants have been shown to often perform myoelectric control tasks better than participants with ULA. It has been suggested that this performance difference can be reduced by restricting the wrist and hand movements of able-bodied participants. However, the effect of such restrictions on the consistency and separability of the electromyogram’s (EMG) features remains unknown. The present work investigates whether the EMG separability and consistency between unaffected and affected arms differ and whether they change after restricting the unaffected limb in persons with ULA.

Both arms of participants with unilateral ULA were compared in two conditions with the unaffected hand and wrist restricted or not. Furthermore, it was tested if the effect of arm and restriction is influenced by arm posture (arm down, arm in front, or arm up).

Fourteen participants (two women, age = 53.4±4.05) with acquired transradial limb loss were recruited. We found that the unaffected limb generated more separated EMG than the affected limb. Furthermore, restricting the unaffected hand and wrist lowered the separability of the EMG when the arm was held down.

Limb restriction is a viable method to make the EMG of able-bodied participants more similar to that of participants with ULA.

Future research that evaluates methods for machine learning controlled hands in able-bodied participants should restrict the participants’ hand and wrist.

Future research that evaluates methods for machine learning controlled hands in able-bodied participants should restrict the participants’ hand and wrist.

Custom foot orthoses (CFOs) are typically used for the prevention and cure of lower extremity injuries (LEIs). Typically, CFOs are designed and prescribed based on iterative loops including (1) follow-up loops between the patient and the physician, and (2) design feedback loops between the physician and the fabricator. The current prescription methodology has some deficiencies, i.e. excessive time to satisfactory treatment, and low repeatability in custom fabrication because of missing alignment, soft tissue considerations, and subjective feedback. There are significant opportunities to develop a new CFOs prescription procedure which can improve accuracy prior to the fabrication process by reducing time through minimizing iterations.

First, a novel “rapid evaluate and adjust device” (READ) prescription methodology is proposed for CFO design by combining the follow-up loops with design feedback loop. To support the idea of the READ prescription method a novel 3D ergonomic measurement system (3DEMS) is deveproposed READ prescription method with the 3DEMS may be used for CFOs prescription due to better communication between individuals in the follow-up and design loops, less time for satisfactory treatment, improved repeatability, archivable data, and low system complexity.

The developed system may be used as measurement systems for CFOs, and in the future the proposed 3DEMS may prove highly important for the measurement of CFOs for flat feet.

The developed system may be used as measurement systems for CFOs, and in the future the proposed 3DEMS may prove highly important for the measurement of CFOs for flat feet.Artificial intelligence provides new feasibilities to the control of dexterous prostheses. To achieve suitable grasps over various objects, a novel computer vision-based classification method assorting objects into different grasp patterns is proposed in this paper. This method can be applied in the autonomous control of the multi-fingered prosthetic hand, as it can help users rapidly complete “reach-and-pick up” tasks on various daily objects with low demand on the myoelectric control. Firstly, an RGB-D image database (121 objects) was established according to four important grasp patterns (cylindrical, spherical, tripod, and lateral). The image samples in the RGB-D dataset were acquired on a large variety of daily objects of different sizes, shapes, postures (16), as well as different illumination conditions (4) and camera positions (4). Then, different inputs and structures of the discrimination model (multilayer CNN) were tested in terms of the classification success rate through cross-validation. Our results showed that depth data play an important role in grasp pattern recognition. The bimodal data (Gray-D) integrating both grayscale and depth information about the objects can improve the classification accuracy acquired from the RGB images (> 10%) effectively. Within the database, the network could achieve the classification with high accuracy (98%); it also has a strong generalization capability on novel samples (93.9 ± 3.0%). We finally applied the method on a dexterous prosthetic hand and tested the whole system on performing the “reach-and-pick up” tasks. The experiments showed that the proposed computer vision-based myoelectric control method (Vision-EMG) could significantly improve the control effectiveness (6.4 s), with comparison to the traditional coding-based myoelectric control method (Coding-EMG, 13 s ).While functional integration has been suggested to reflect brain health, non-standardized network thresholding methods complicate network interpretation. We propose a new method to analyze functional near-infrared spectroscopy-based functional connectivity (fNIRS-FC). In this study, we employed wavelet analysis for motion correction and orthogonal minimal spanning trees (OMSTs) to derive the brain connectivity. The proposed method was applied to an Alzheimer’s disease (AD) dataset and was compared with a number of well-known thresholding techniques. The results demonstrated that the proposed method outperformed the benchmarks in filtering cost-effective networks and in differentiation between patients with mild AD and healthy controls. click here The results also supported the proposed method as a feasible technique to analyze fNIRS-FC, especially with cost-efficiency, assortativity and laterality as a set of effective features for the diagnosis of AD.