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Query: UMLS:C0015672 (fatigue)
 51,768 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Laparoscopic surgery techniques are customarily used in non-invasive procedures. That said traditional surgical instruments and devices used by surgeons suffer from certain ergonomic deficiencies that may lead to physical complaints in upper limbs and back and general discomfort that may, in turn, affect the surgeon's skills during surgery. A novel design of the laparoscopic gripper handle is presented and compared with one of the most used instruments in this field in an attempt to overcome this problem. The assessment of the ergonomic feature of the novel design was performed by using time-frequency analysis of the surface electromyography (sEMG) signal during dynamic activities. Singular Spectrum Analysis (SSA) was used to decompose the sEMG signal and extract the median frequency of each muscle to assess muscle fatigue. The results reveal that using the proposed ergonomic grip reduces the mean values of the muscle activity during each of the proposed tasks. The novel design also improves the ease of use in laparoscopic surgery as it minimises high-pressure contact areas, reduces large amplitude movements and promotes a neutral position of the hand, wrist and forearm. Furthermore, the SSA method for time-frequency analysis provides a powerful tool to analyse a prescribed activity in ergonomic terms. The proposed methodology to assess muscle activity during surgery activities may be useful in the selection of surgical instruments when programming extended procedures, as it provides an additional selection criterion based on the surgeon's biomechanics and the proposed activity.
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PMID:Ergonomic assessment of a new hand tool design for laparoscopic surgery based on surgeons' muscular activity. 3267 79

Autonomous driving systems are set to become a reality in transport systems and, so, maximum acceptance is being sought among users. Currently, the most advanced architectures require driver intervention when functional system failures or critical sensor operations take place, presenting problems related to driver state, distractions, fatigue, and other factors that prevent safe control. Therefore, this work presents a redundant, accurate, robust, and scalable LiDAR odometry system with fail-aware system features that can allow other systems to perform a safe stop manoeuvre without driver mediation. All odometry systems have drift error, making it difficult to use them for localisation tasks over extended periods. For this reason, the paper presents an accurate LiDAR odometry system with a fail-aware indicator. This indicator estimates a time window in which the system manages the localisation tasks appropriately. The odometry error is minimised by applying a dynamic 6-DoF model and fusing measures based on the Iterative Closest Points (ICP), environment feature extraction, and Singular Value Decomposition (SVD) methods. The obtained results are promising for two reasons: First, in the KITTI odometry data set, the ranking achieved by the proposed method is twelfth, considering only LiDAR-based methods, where its translation and rotation errors are 1.00 % and 0.0041 deg/m, respectively. Second, the encouraging results of the fail-aware indicator demonstrate the safety of the proposed LiDAR odometry system. The results depict that, in order to achieve an accurate odometry system, complex models and measurement fusion techniques must be used to improve its behaviour. Furthermore, if an odometry system is to be used for redundant localisation features, it must integrate a fail-aware indicator for use in a safe manner.
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PMID:Fail-Aware LIDAR-Based Odometry for Autonomous Vehicles. 3271 44

In this study, an attempt has been made to distinguish between nonfatigue and fatigue conditions in surface Electromyography (sEMG) signal using the time frequency distribution obtained from analytic Bump Continuous Wavelet Transform. For the analysis, sEMG signals from biceps brachii muscle of 22 healthy subjects are acquired during isometric contraction protocol. The signals acquired is preprocessed and partitioned into ten equal segments followed by the decomposition of selected segments using analytic Bump wavelets. Further, Singular Value Decomposition is applied to the time frequency distribution matrix and the maximum singular value and entropy feature for each segment are obtained. The usefulness of both the features is estimated using the Wilcoxon sign rank test that gives higher significance with a p < .00001. It is observed that the proposed method is capable of analyzing the fatigue regions in sEMG signals.
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PMID:Analysis of Isometric Muscle Contractions using Analytic Bump Continuous Wavelet Transform. 3301 91