Low Complex CORDIC-based Hand Movement Recognition Design Methodology for Rehabilitation and Prosthetic Applications.

Hand movement recognition using Electromyography (EMG) signals have gained much significance lately and is extensively used for rehabilitation and prosthetic applications including stroke-driven disability and other neuromuscular disorders. Herein, quantitative analysis of EMG signals is very crucial. However, such applications are constrained by power consumption limitations due to the battery backup necessitating low-complex system design and the on-chip area requirement. Existing hand movement recognition methodologies using single-channel EMG signal involve computationally intensive stages, including Ensemble Empirical Mode Decomposition (EEMD), Fast Independent Component Analysis (FastICA), feature extraction, and Linear Discriminant Analysis (LDA) classification, which can not be mapped onto the low-complex architecture directly from the algorithmic level. The high computational complexity of LDA classification makes it difficult to be used for low-complex applications. In this paper, we introduce a low-complex CORDIC-based hand movement recognition design methodology targeting resource-constrained rehabilitation applications. This work explores replacing LDA classification with K-Means clustering due to its reduced complexity and efficient clustering algorithm. CORDIC-based K-Means clustering is used to further reduce the overall computational complexity of the system. The proposed low complex, K-Means clustering-based hand movement recognition for classifying seven hand movements using single-channel EMG data is found to be 99.77 % less complex and 1.28% more accurate than the conventional LDA-based classification.

Poststroke Grasp Ability Assessment Using an Intelligent Data Glove Based on Action Research Arm Test: Development, Algorithms, and Experiments.

Growing impact of poststroke upper extremity (UE) functional limitations entails newer dimensions in assessment methodologies. This has compelled researchers to think way beyond traditional stroke assessment scales during the out-patient rehabilitation phase. In concurrence with this, sensor-driven quantitative evaluation of poststroke UE functional limitations has become a fertile field of research. Here, we have emphasized an instrumented wearable for systematic monitoring of stroke patients with right-hemiparesis for evaluating their grasp abilities deploying intelligent algorithms. An instrumented glove housing 6 flex sensors, 3 force sensors, and a motion processing unit was developed to administer 19 activities of Action Research Arm Test (ARAT) while experimenting on 20 voluntarily participating subjects. After necessary signal conditioning, meaningful features were extracted, and subsequently the most appropriate ones were selected using the ReliefF algorithm. An optimally tuned support vector classifier was employed to classify patients with different degrees of disability and an accuracy of 92% was achieved supported by a high area under the receiver operating characteristic score. Furthermore, selected features could provide additional information that revealed the causes of grasp limitations. This would assist physicians in planning more effective poststroke rehabilitation strategies. Results of the one-way ANOVA test conducted on actual and predicted ARAT scores of the subjects indicated remarkable prospects of the proposed glove-based method in poststroke grasp ability assessment and rehabilitation.

Gold-nanoparticle-embedded microchannel array for enhanced power generation.

A high streaming potential and current were generated using a gold-nanoparticle-embedded patterned PDMS microchannel array. Gold nanoparticles with dimensions of ∼70 nm were prepared inside a hydrophobic patterned PDMS microchannel. The channel array was developed on a ridge-shaped patterned surface by performing soft lithography using UV-laser micromachining with a ridge spacing of 27.0 µm, width of 22.0 µm, and height of 16.0 µm. Subsequently, tests were conducted in which ultrapure water, solutions of 0.1 M NaCl, 0.1 M HCl and 40% H2O2 were passed through the patterned channel array at various flow rates and pressures using a microfluidic pump wherein the channel inlet and outlet acted as collector electrodes. A maximum streaming potential of 2.6 V, a current of 1.3 µA, and a maximum power density of 4.3 µW cm-2 were obtained for this gold-nanoparticle-embedded PDMS channel with ultrapure water as the working fluid at an inlet pressure of 1 bar. The generated power density here was ∼256 times higher than that for the PDMS channel array without gold nanoparticles using ultrapure water as the working fluid, confirming the benefit of gold nanoparticles in the channel array, which may have potential applications in microwatt-powered lab-on-chip devices.