Design and testing of a textile EMG sensor for prosthetic control

Nowadays, Electromyography (EMG) signals generated by the amputee’s residual limbs are widely used for the control of myoelectric prostheses, usually with the aid of pattern-recognition algorithms. Since myoelectric prostheses are wearable medical devices, the sensors that integrate them should be appropriate for the users’ daily life, meeting the requirements of lightness, flexibility, greater motion identification, and skin adaptability. Therefore, this study aims to design and test an EMG sensor for prosthetic control, focusing on aspects such as adjustability, lightness, precise and constant signal acquisition; and replacing the conventional components of an EMG sensor with textile materials. The proposed sensor was made with Shieldex Technik-tex P130 + B conductive knitted fabric, with 99% pure silver plating. EMG data acquisition was performed twice on three volunteers: one with the textile sensor, and other with a commercial sensor used in prosthetic applications. Overall, the textile and the commercial sensor presented total average Signal-to-Noise Ratio (SNR) values of 10.24 ± 5.45 dB and 11.74 ± 8.64 dB, respectively. The authors consider that the obtained results are promising and leave room for further improvements in future work, such as designing strategies to deal with known sources of noise contamination and to increase the adhesion to the skin. In sum, the results presented in this paper indicate that, with the appropriate improvements, the proposed textile sensor may have the potential of being used for myoelectric prosthetic control, which can be a more ergonomic and accessible alternative to the sensors that are currently used for controlling these devices.This work is financed by Project “Deus ex Machina”, NORTE-01-0145-FEDER-000026, funded by CCDRN, through Sistema de Apoio à Investigação Científica e Tecnológica (Projetos Estruturados I&D&I) of Programa Operacional Regional do Norte, from Portugal 2020 and by Project UID/CTM/00264/2019 of 2C2T –Centro de Ciência e TecnologiaTêxtil, funded by National Founds through FCT/MCTES

Comparison of hospital charge prediction models for gastric cancer patients: neural network vs. decision tree models

<p>Abstract</p> <p>Background</p> <p>In recent years, artificial neural network is advocated in modeling complex multivariable relationships due to its ability of fault tolerance; while decision tree of data mining technique was recommended because of its richness of classification arithmetic rules and appeal of visibility. The aim of our research was to compare the performance of ANN and decision tree models in predicting hospital charges on gastric cancer patients.</p> <p>Methods</p> <p>Data about hospital charges on 1008 gastric cancer patients and related demographic information were collected from the First Affiliated Hospital of Anhui Medical University from 2005 to 2007 and preprocessed firstly to select pertinent input variables. Then artificial neural network (ANN) and decision tree models, using same hospital charge output variable and same input variables, were applied to compare the predictive abilities in terms of mean absolute errors and linear correlation coefficients for the training and test datasets. The transfer function in ANN model was sigmoid with 1 hidden layer and three hidden nodes.</p> <p>Results</p> <p>After preprocess of the data, 12 variables were selected and used as input variables in two types of models. For both the training dataset and the test dataset, mean absolute errors of ANN model were lower than those of decision tree model (1819.197 vs. 2782.423, 1162.279 vs. 3424.608) and linear correlation coefficients of the former model were higher than those of the latter (0.955 vs. 0.866, 0.987 vs. 0.806). The predictive ability and adaptive capacity of ANN model were better than those of decision tree model.</p> <p>Conclusion</p> <p>ANN model performed better in predicting hospital charges of gastric cancer patients of China than did decision tree model.</p

How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

Repeated Training with Augmentative Vibrotactile Feedback Increases Object Manipulation Performance

Most users of prosthetic hands must rely on visual feedback alone, which requires visual attention and cognitive resources. Providing haptic feedback of variables relevant to manipulation, such as contact force, may thus improve the usability of prosthetic hands for tasks of daily living. Vibrotactile stimulation was explored as a feedback modality in ten unimpaired participants across eight sessions in a two-week period. Participants used their right index finger to perform a virtual object manipulation task with both visual and augmentative vibrotactile feedback related to force. Through repeated training, participants were able to learn to use the vibrotactile feedback to significantly improve object manipulation. Removal of vibrotactile feedback in session 8 significantly reduced task performance. These results suggest that vibrotactile feedback paired with training may enhance the manipulation ability of prosthetic hand users without the need for more invasive strategies

Ionic liquids at electrified interfaces

Until recently, “room-temperature” (<100–150 °C) liquid-state electrochemistry was mostly electrochemistry of diluted electrolytes(1)–(4) where dissolved salt ions were surrounded by a considerable amount of solvent molecules. Highly concentrated liquid electrolytes were mostly considered in the narrow (albeit important) niche of high-temperature electrochemistry of molten inorganic salts(5-9) and in the even narrower niche of “first-generation” room temperature ionic liquids, RTILs (such as chloro-aluminates and alkylammonium nitrates).(10-14) The situation has changed dramatically in the 2000s after the discovery of new moisture- and temperature-stable RTILs.(15, 16) These days, the “later generation” RTILs attracted wide attention within the electrochemical community.(17-31) Indeed, RTILs, as a class of compounds, possess a unique combination of properties (high charge density, electrochemical stability, low/negligible volatility, tunable polarity, etc.) that make them very attractive substances from fundamental and application points of view.(32-38) Most importantly, they can mix with each other in “cocktails” of one’s choice to acquire the desired properties (e.g., wider temperature range of the liquid phase(39, 40)) and can serve as almost “universal” solvents.(37, 41, 42) It is worth noting here one of the advantages of RTILs as compared to their high-temperature molten salt (HTMS)(43) “sister-systems”.(44) In RTILs the dissolved molecules are not imbedded in a harsh high temperature environment which could be destructive for many classes of fragile (organic) molecules

EMG-based decoding of grasp gestures in reaching-to-grasping motions

Predicting the grasping function during reach-to-grasp motions is essential for controlling a prosthetic hand or a robotic assistive device. An early accurate prediction increases the usability and the comfort of a prosthetic device. This work proposes an electromyographic-based learning approach that decodes the grasping intention at an early stage of reach-to-grasp motion, i.e. before the final grasp/hand pre-shape takes place. Superficial electrodes and a Cyberglove were used to record the arm muscle activity and the finger joints during reach-to-grasp motions. Our results showed a 90% accuracy for the detection of the final grasp about 0.5 s after motion onset. This paper also examines the effect of different objects’ distances and different motion speeds on the detection time and accuracy of the classifier. The use of our learning approach to control a 16-degrees of freedom robotic hand confirmed the usability of our approach for the real-time control of robotic devices

The impact of using an upper-limb prosthesis on the perception of real and illusory weight differences

Little is known about how human perception is affected using an upper-limb prosthesis. To shed light on this topic, we investigated how using an upper-limb prosthesis affects individuals’ experience of object weight. First, we examined how a group of upper-limb amputee prosthetic users experienced real mass differences and illusory weight differences in the context of the ‘size-weight’ illusion. Surprisingly, the upper-limb prosthetic users reported a markedly smaller illusion than controls, despite equivalent perceptions of a real mass difference. Next, we replicated this dissociation between real and illusory weight perception in a group of non-amputees who lifted the stimuli with an upper-limb myoelectric prosthetic simulator, again noting that the prosthetic users experienced illusory, but not real, weight differences as being weaker than controls. These findings not only validate the use of a prosthetic simulator as an effective tool for investigating perception and action, but also highlight a surprising dissociation between the perception of real and illusory weight differences