Planning to Minimize the Human Muscular Effort during Forceful Human-Robot Collaboration

This work addresses the problem of planning a robot configuration and grasp to position a shared object during forceful human-robot collaboration, such as a puncturing or a cutting task. Particularly, our goal is to find a robot configuration that positions the jointly manipulated object such that the muscular effort of the human, operating on the same object, is minimized while also ensuring the stability of the interaction for the robot. This raises three challenges. First, we predict the human muscular effort given a human-robot combined kinematic configuration and the interaction forces of a task. To do this, we perform task-space to muscle-space mapping for two different musculoskeletal models of the human arm. Second, we predict the human body kinematic configuration given a robot configuration and the resulting object pose in the workspace. To do this, we assume that the human prefers the body configuration that minimizes the muscular effort. And third, we ensure that, under the forces applied by the human, the robot grasp on the object is stable and the robot joint torques are within limits. Addressing these three challenges, we build a planner that, given a forceful task description, can output the robot grasp on an object and the robot configuration to position the shared object in space. We quantitatively analyze the performance of the planner and the validity of our assumptions. We conduct experiments with human subjects to measure their kinematic configurations, muscular activity, and force output during collaborative puncturing and cutting tasks. The results illustrate the effectiveness of our planner in reducing the human muscular load. For instance, for the puncturing task, our planner is able to reduce muscular load by 69.5\% compared to a user-based selection of object poses.

EEG–EMG coupling as a hybrid method for steering detection in car driving settings

Understanding mental processes in complex human behavior is a key issue in driving, representing a milestone for developing user-centered assistive driving devices. Here, we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left and right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings of 128-channel EEG and EMG activity from deltoids and forearm extensors in non-ecological and ecological steering tasks. Specifically, we identified the EEG mu rhythm modulation correlates with motor preparation of self-paced steering actions in the non-ecological task, while the concurrent EMG activity of the left (right) deltoids correlates with right (left) steering. Consequently, we exploited the mu rhythm de-synchronization resulting from the non-ecological task to detect the steering side using cross-correlation analysis with the ecological EMG signals. Results returned significant cross-correlation values showing the coupling between the non-ecological EEG feature and the muscular activity collected in ecological driving conditions. Moreover, such cross-correlation patterns discriminate the steering side earlier relative to the single EMG signal. This hybrid system overcomes the limitation of the EEG signals collected in ecological settings such as low reliability, accuracy, and adaptability, thus adding to the EMG the characteristic predictive power of the cerebral data. These results prove how it is possible to complement different physiological signals to control the level of assistance needed by the driver.

Automatic Coding of Facial Expressions of Pain: Are We There Yet?

Introduction. The experience of pain is regularly accompanied by facial expressions. The gold standard for analyzing these facial expressions is the Facial Action Coding System (FACS), which provides so-called action units (AUs) as parametrical indicators of facial muscular activity. Particular combinations of AUs have appeared to be pain-indicative. The manual coding of AUs is, however, too time- and labor-intensive in clinical practice. New developments in automatic facial expression analysis have promised to enable automatic detection of AUs, which might be used for pain detection. Objective. Our aim is to compare manual with automatic AU coding of facial expressions of pain. Methods. FaceReader7 was used for automatic AU detection. We compared the performance of FaceReader7 using videos of 40 participants (20 younger with a mean age of 25.7 years and 20 older with a mean age of 52.1 years) undergoing experimentally induced heat pain to manually coded AUs as gold standard labeling. Percentages of correctly and falsely classified AUs were calculated, and we computed as indicators of congruency, “sensitivity/recall,” “precision,” and “overall agreement (F1).” Results. The automatic coding of AUs only showed poor to moderate outcomes regarding sensitivity/recall, precision, and F1. The congruency was better for younger compared to older faces and was better for pain-indicative AUs compared to other AUs. Conclusion. At the moment, automatic analyses of genuine facial expressions of pain may qualify at best as semiautomatic systems, which require further validation by human observers before they can be used to validly assess facial expressions of pain.

Orofacial Muscle Weakening in Facioscapulohumeral Muscular Dystrophy (FSHD) Patients

Background: Facioscapulohumeral muscular dystrophy is the third most commonly found type of muscular dystrophy. The aim of this study was to correlate the D4Z4 repeat array fragment size to the orofacial muscle weakening exhibited in a group of patients with a genetically supported diagnosis of FSHD. Methods: Molecular genetic analysis was performed for 52 patients (27 female and 25 male) from a group that consisted of 36 patients with autosomal dominant pedigrees and 16 patients with either sporadic or unknown family status. The patients were tested with the southern blotting technique, using EcoRI/Avrll double digestion, and fragments were detected by a p13E-11 telomeric probe. Spearman’s correlation was used to compare the fragment size with the degree of muscle weakening found in the forehead, periocular and perioral muscles. Results: A positive non-significant correlation between the DNA fragment size and severity of muscle weakness was found for the forehead (r = 0.27; p = 0187), the periocular (r = 0.24; p = 0.232) and the left and right perioral (r = 0.29; p = 0.122), (r = 0.32; p = 0.085) muscles. Conclusions: Although FSHD patients exhibited a decrease in muscular activity related to the forehead, perioral, and periocular muscles the genotype–phenotype associations confirmed a weak to moderate non-significant correlation between repeat size and the severity of muscle weakness. Orofacial muscle weakening and its association with a D4Z4 contraction alone may not have the significance to serve as a prognostic biomarker, due to the weak to moderate association. Further studies with larger sample sizes are needed to determine the degree of genetic involvement in the facial growth in FSHD patients.

Automatic analysis of muscular activity in the flexor digitorum superficialis muscles: A fast screening method for rapid eye movement sleep without atonia

Study objectives To identify a fast and reliable method for rapid eye movement (REM) sleep without atonia (RWA) quantification. Methods We analyzed 36 video-polysomnographies (v-PSGs) of isolated REM sleep behavior disorder (iRBD) patients and 35 controls’ v-PSGs. Patients diagnosed with RBD had: i) RWA, quantified with a reference method, i.e. automatic and artifact-corrected 3-s Sleep Innsbruck Barcelona (SINBAR) index in REM sleep periods (RSPs, i.e. manually selected portions of REM sleep); and ii) v-PSG-documented RBD behaviors. We quantified RWA with other (semi)-automated methods requiring less human intervention than the reference one: the indices proposed by the SINBAR group (the 3-s and 30-s phasic flexor digitorum superficialis (FDS), phasic/”any”/tonic mentalis), and the REM atonia, short and long muscle activity indices (in mentalis/submentalis/FDS muscles). They were calculated in whole REM sleep (i.e. REM sleep scored following international guidelines), in RSPs, with and without manual artifact correction. Area under curves (AUC) discriminating iRBD from controls were computed. Using published cut-offs, the indices’ sensitivity and specificity for iRBD identification were calculated. Apnea-hypopnea index in REM sleep (AHIREM) was considered in the analyses. Results RWA indices from FDS muscles alone had the highest AUCs and all of them had 100% sensitivity. Without manual RSP selection and artifact correction, the “30-s phasic FDS” and the “FDS long muscle activity” had the highest specificity (85%) with AHIREM<15/h. RWA indices were less reliable when AHIREM≥15/h. Conclusions If AHIREM<15/h, FDS muscular activity in whole REM sleep and without artifact correction is fast and reliable to rule out RWA.

Effect of the Knee and Hip Angles on Knee Extensor Torque: Neural, Architectural, and Mechanical Considerations

This study examined the influence of knee extensors’ hip and knee angle on force production capacity and their neuromuscular and architectural consequences. Sixteen healthy men performed sub-maximal and maximal voluntary isometric contractions (MVIC) of knee extensors with four different combinations of the knee and hip angles. Muscle architecture, excitation-contraction coupling process, muscular activity, and corticospinal excitability were evaluated on the vastus lateralis (VL) and rectus femoris (RF) muscles. MVIC and evoked peak twitch (Pt) torques of knee extensors increased significantly (p < 0.05) by 42 ± 12% and 47 ± 16% on average, respectively, under knee flexed positions (110° flexion, 0° = full extension) compared to knee extended positions (20° flexion) but were not different between hip positions (i.e., 0° or 60° flexion). Knee flexion also affected VL and RF muscle and fascicle lengths toward greater length than under knee extended position, while pennation angle decreased for both muscles with knee flexion. Pennation angles of the VL muscle were also lower under extended hip positions. Alternatively, no change in maximal muscle activation or corticospinal activity occurred for the VL and RF muscles across the different positions. Altogether these findings evidenced that MVIC torque of knee extensors depended particularly upon peripheral contractile elements, such as VL and RF muscle and fascicle lengths, but was unaffected by central factors (i.e., muscle activation). Furthermore, the hip position can affect the pennation angle of the VL, while VL muscle length can affect the pennation angle of the RF muscle. These elements suggest that the VL and RF muscles exert a mutual influence on their architecture, probably related to the rectus-vastus aponeurosis.

Modified Functional Reach Test: Upper-Body Kinematics and Muscular Activity in Chronic Stroke Survivors

Effective control of trunk muscles is fundamental to perform most daily activities. Stroke affects this ability also when sitting, and the Modified Functional Reach Test is a simple clinical method to evaluate sitting balance. We characterize the upper body kinematics and muscular activity during this test. Fifteen chronic stroke survivors performed twice, in separate sessions, three repetitions of the test in forward and lateral directions with their ipsilesional arm. We focused our analysis on muscles of the trunk and of the contralesional, not moving, arm. The bilateral activations of latissimi dorsi, trapezii transversalis and oblique externus abdominis were left/right asymmetric, for both test directions, except for the obliquus externus abdominis in the frontal reaching. Stroke survivors had difficulty deactivating the contralesional muscles at the end of each trial, especially the trapezii trasversalis in the lateral direction. The contralesional, non-moving arm had muscular activations modulated according to the movement phases of the moving arm. Repeating the task led to better performance in terms of reaching distance, supported by an increased activation of the trunk muscles. The reaching distance correlated negatively with the time-up-and-go test score.

Phenytoin-induced rhabdomyolysis

Rhabdomyolysis is a potentially life-threatening syndrome that can develop from a variety of causes. The most common causes are muscle injury, alcohol abuse, drugs, toxins and increased muscular activity. Phenytoin is one of the drugs that rarely cause rhabdomyolysis. We present the case of a man who developed rhabdomyolysis following phenytoin treatment.

A Novel Approach of Modelling and Predicting Track Cycling Sprint Performance

In cycling, performance models are used to investigate factors that determine performance and to optimise competition results. We present an innovative and easily applicable mathematical model describing time-resolved approaches for both the physical aspects of tractional resistance and the physiological side of propelling force generated by muscular activity and test its validity to reproduce and forecast time trials in track cycling. Six elite track cyclists completed a special preparation and two sprint time trials in an official velodrome under continuous measurement of crank force and cadence. Fatigue-free force-velocity profiles were calculated, and their fatigue-induced changes were determined by non-linear regression analysis using a monoexponential equation at a constant slope. Model parameters were calibrated based on pre-exercise performance testing and the first of the two time-trials and then used to predict the performance of the second sprint. Measured values for power output and cycling velocity were compared to the modelled data. The modelled results were highly correlated to the measured values (R2>0.99) without any difference between runs (p>0.05; d<0.1). Our mathematical model can accurately describe sprint track cycling time trial performance. It is simple enough to be used in practice yet sufficiently accurate to predict highly dynamic maximal sprint performances. It can be employed for the evaluation of completed runs, to forecast expected results with different setups, and to study various contributing factors and quantify their effect on sprint cycling performance.