Better combination of thickness and hardness of mattress topper for supine sleeping posture: A physiological measurements evaluation

Placing a topper on a sleeping system is common practice to enhance sleeping comfort. The aim of this study was to determine the thickness and hardness effect of a mattress topper by measuring the four physiological measurements from 40 healthy males. The results showed that the use of a thin mattress topper (30 mm) significantly induced lower body pressure and temperature, higher muscle activities in the biceps femoris, and a straighter spinal alignment in the T12–L4 vertebrae. The use of a hard mattress topper significantly induced a higher body pressure, a lower body temperature in the lower extremities, a higher EMG (%MVC) in the trapezius, but a lower EMG (%MVC) in the biceps femoris, and a straighter spinal alignment at T1–T4, T4–T6, and T6–T8. Overall, a soft topper of 30-mm thickness was suggested as the best combination. The findings can provide very useful information for topper design and selection.     

Deep Learning for EMG-based Human-Machine Interaction: A Review

Electromyography (EMG) has already been broadly used in human-machine interaction (HMI) applications. Determining how to decode the information inside EMG signals robustly and accurately is a key problem for which we urgently need a solution. Recently, many EMG pattern recognition tasks have been addressed using deep learning methods. In this paper, we analyze recent papers and present a literature review describing the role that deep learning plays in EMG-based HMI. An overview of typical network structures and processing schemes will be provided. Recent progress in typical tasks such as movement classification, joint angle prediction, and force/torque estimation will be introduced. New issues, including multimodal sensing, inter-subject/inter-session, and robustness toward disturbances will be discussed. We attempt to provide a comprehensive analysis of current research by discussing the advantages, challenges, and opportunities brought by deep learning. We hope that deep learning can aid in eliminating factors that hinder the development of EMG-based HMI systems. Furthermore, possible future directions will be presented to pave the way for future research.      

Electromyography applied to sport ergonomics

Abstract

The improvement of electromyographic (EMG) devices for the detection of electric potentials produced in voluntary complex movements and the evolution of methodological approaches to data acquisition and computerized analysis of patterns, are responsible for the increased applications of EMG in bioengineering, rehabilitation, sport and occupational biomechanics, physiology and zoology and to a lesser extent in ergonomics. This paper describes three different EMG applications related to a sport environment using three different EMG registration and data acquisition approaches.

The first study examined the relation between the swimming action in water and its simulation using training equipment on land. It was found that with respect to the mechanical aspects of dry-land equipment and to the biomechanical differences in the execution of the front crawl action on dry land and in water, based on EMG activity of the propulsion muscles, the best results were found when using devices with accommodating resistance. Recovery muscles were best imitated using isokinetics, but despite the greater effort on land, lower EMG activity was recorded than in water. It was generally observed that whenever the swimmer acted against a mechanical resistance an important pattern deviation was noted.

Second, a study to determine the influence of ski materials on the EMG muscle activity of skiers showed systematic differences between the use of racing, soft and compact skis. The results supported the idea that the soft ski should be more highly recommended than the compact or racing ski for both general and competive use.

Finally, in a third study we found that there was a very high degree of similarity in the pattern and intensity of muscle activity in both free swimming and swimming against a mechanical resistance (MAD swimming), even though the kinesiological aspects of the movement trajectory were proved different beforehand.     

Channel and feature selection for a surface electromyographic pattern recognition task

The objective of this research is to select a reduced group of surface electromyographic (sEMG) channels and signal-features that is able to provide an accurate classification rate in a myoelectric control system for any user. To that end, the location of 32 sEMG electrodes placed around-along the forearm and 86 signal-features are evaluated simultaneously in a static-hand gesture classification task (14 different gestures). A novel multivariate variable selection filter method named mRMR-FCO is presented as part of the selection process. This process finds the most informative and least redundant combination of sEMG channels and signal-features among all the possible ones. The performance of the selected set of channels and signal-features is evaluated with a Support Vector Machine classifier.     

Development and validation of a three dimensional dynamic biomechanical lifting model for lower back evaluation for careful box placement

One of the major causes to low back injury is the box lifting activity, thus for many years biomechanics has been utilized by designers for ergonomic evaluations of the box lifting activity which includes the placement of the box. More recently these ergonomic investigations have focused on the careful placement of the box. The AnyBody (AB) biomechanical models and optimization within the AB software system in conjunction with motion capture has been shown to obtain adequate estimates of joint reaction forces of the body. To date there has not been a dynamic 3D box lifting model developed and validated for carefully placing a box using the AB modeling system and motion capture. Thus the focus of this paper is on the development, verification and validation of a box lifting full body model for lower back evaluations for a dynamic lifting activity for carefully placing a box on a shelf.     

Processes underlying congruent and incongruent facial reactions to emotional facial expressions.

The present electromyographic study is a first step toward shedding light on the involvement of affective processes in congruent and incongruent facial reactions to facial expressions. Further, empathy was investigated as a potential mediator underlying the modulation of facial reactions to emotional faces in a competitive, a cooperative, and a neutral setting. Results revealed less congruent reactions to happy expressions and even incongruent reactions to sad and angry expressions in the competition condition, whereas virtually no differences between the neutral and the cooperation condition occurred. Effects on congruent reactions were found to be mediated by cognitive empathy, indicating that the state of empathy plays an important role in the situational modulation of congruent reactions. Further, incongruent reactions to sad and angry faces in a competition setting were mediated by the emotional reaction of joy, supporting the assumption that incongruent facial reactions are mainly based on affective processes. Additionally, strategic processes (specifically, the goal to create and maintain a smooth, harmonious interaction) were found to influence facial reactions while being in a cooperative mindset. Now, further studies are needed to test for the generalizability of these effects. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

基于EMG频域特征的假肢机电信号识别研究

采集人体表面肌电信号并进行有效模式识别,是多功能假肢研究的关键技术之一.本文研究肌电信号(EMG)的频域信息,以提取有效特征识别不同的手腕部动作.实验采集了8种不同的手腕动作的EMG信号.分别提取了EMG信号的时间序列模型(AR)、时域统计量(DT)、功率谱估计(PSD)、短时傅里叶变换(STFT)和互功率谱(CPSD...     

Corrugator muscle responses are associated with individual differences in positivity-negativity bias.

Corrugator supercilii muscle activity is considered an objective measure of valence because it increases in response to negatively valenced facial expressions (angry) and decreases to positive expressions (happy). The authors sought to determine if corrugator activity could be used as an objective measure of positivity-negativity bias. The authors recorded corrugator responses as participants rated angry, happy, and surprised faces as “positive” or “negative.” The critical measure of bias was the percentage of positive versus negative ratings assigned to surprised faces by each participant. Reaction times for surprise expressions were longer than for happy and angry expressions, consistent with their ambiguous valence. Participants who tended to rate surprised faces as negative showed increased corrugator activity to surprised faces, whereas those who tended to rate surprise as positive showed decreased activity. Critically, corrugator responses reflected the participants’ bias (i.e., their tendency to rate surprise as positive or negative). These data show that surprised faces constitute a useful tool for assessing individual differences in positivity-negativity bias, and that corrugator activity can objectively reflect this bias. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An ergonomics study of thumb movements on smartphone touch screen

This study investigated the relationships between thumb muscle activity and thumb operating tasks on a smartphone touch screen with one-hand posture. Six muscles in the right thumb and forearm were targeted in this study, namely adductor pollicis, flexor pollicis brevis, abductor pollicis brevis (APB), abductor pollicis longus, first dorsal interosseous (FDI) and extensor digitorum. The performance measures showed that the thumb developed fatigue rapidly when tapping on smaller buttons (diameter: 9 mm compared with 3 mm), and moved more slowly in flexion–extension than in adduction–abduction orientation. Meanwhile, the electromyography and perceived exertion values of FDI significantly increased in small button and flexion–extension tasks, while those of APB were greater in the adduction–abduction task. This study reveals that muscle effort among thumb muscles on a touch screen smartphone varies according to the task, and suggests that the use of small touch buttons should be minimised for better thumb performance.