Gait Patterns in Patients with Hereditary Spastic Paraparesis

Spastic gait is a key feature in patients with hereditary spastic paraparesis, but the gait characterization and the relationship between the gait impairment and clinical characteristics have not been investigated

The sensor-based biomechanical risk assessment at the base of the need for revising of standards for human ergonomics

Due to the epochal changes introduced by “Industry 4.0”, it is getting harder to apply the varying approaches for biomechanical risk assessment of manual handling tasks used to prevent work-related musculoskeletal disorders (WMDs) considered within the International Standards for ergonomics. In fact, the innovative human–robot collaboration (HRC) systems are widening the number of work motor tasks that cannot be assessed. On the other hand, new sensor-based tools for biomechanical risk assessment could be used for both quantitative “direct instrumental evaluations” and “rating of standard methods”, allowing certain improvements over traditional methods. In this light, this Letter aims at detecting the need for revising the standards for human ergonomics and biomechanical risk assessment by analyzing the WMDs prevalence and incidence; additionally, the strengths and weaknesses of traditional methods listed within the International Standards for manual handling activities and the next challenges needed for their revision are considered. As a representative example, the discussion is referred to the lifting of heavy loads where the revision should include the use of sensor-based tools for biomechanical risk assessment during lifting performed with the use of exoskeletons, by more than one person (team lifting) and when the traditional methods cannot be applied. The wearability of sensing and feedback sensors in addition to human augmentation technologies allows for increasing workers’ awareness about possible risks and enhance the effectiveness and safety during the execution of in many manual handling activities

Generalization of a wavelet-based algorithm to adaptively detect activation intervals in weak and noisy myoelectric signals

This study introduces an adaptive implementation of a Continuous Wavelet Transform (CWT) decomposition technique used to estimate the timing of muscular activation in weak and noisy myoelectric signals. The algorithm updates automatically the threshold based on the statistical properties of the EMG data, through an iterative estimation of the Signal-to-Noise Ratio (SNR). Moreover, it includes a stopping criterion for the number of CWT decomposition levels, and this allows a relevant decrease of the computational burden. This algorithm was applied to both synthetic and semi-synthetic signals, and compared against the original formulation of the CWT-based technique and a common threshold-based technique for the detection of muscle activations. Performance of these techniques was assessed by using Bias, Relative Timing Error and Accuracy of the detection. Bias values resulted lower than 18 ms, Relative Timing Error lower than 5% and Accuracy greater than 97% for all the tested SNR (ranging from −2 dB to 10 dB), and with a substantial independence from SNR levels. The performance was shown to hold also if the hypothesis of absence of muscular activation in the reference window cannot be guaranteed. The results show that the proposed approach, which is adaptive, operator-independent and iterative, performs properly when applied to weak and noisy myoelectric signals, and is thus a valid general solution when dealing with clinical conditions where muscular activity is low, and when recording conditions cannot be entirely controlled

Myoelectric Signs of Sustained Muscular Activity During Smartphone Texting

The aim of the present study was to analyze the upper trapezius activity, and its relationship with muscular discomfort perceived, during prolonged smartphone texting. Seventeen healthy young subjects participated in the experiment; they were asked to use their own smartphone for texting (10 min), maintaining two different postures, sitting and standing. The muscular activity of the right and left upper trapezius was recorded, and the CR10 BorgScale was administered after each experimental section. To normalize the EMG signals, the maximum voluntary contraction was acquired at the beginning of the experiment. The median, the 10th percentile (P10) and the range (difference between 90th and 10th) of EMG RMS, the relative rest time (RRT), the correlation between P10 and RRT and between P10 and CR10 scale were calculated. The results showed no statistical difference between the postures, and the body side. The value of RMS parameters was around the 2% of MVC, showing a constant muscular activity throughout the experimental section. A significant negative correlation between P10 and RRT suggested that the subjects with greater P10 showed a lower rest period; moreover, the significant positive correlation between P10 and CR10 Borg scale, for both postures, suggested that the subjects with high P10 values perceived greater discomfort in neck and cervical zone. The results support the hypothesis that the prolonged use of smartphone for texting influences the upper trapezius activity and it is strictly linked with the absence of a period of muscular recovery, and with the perception of muscular discomfort: that may be a potential risk factor to develop neck pain and musculoskeletal cervical disorders

Detecting low-to-moderate isometric muscle activity through a generalized CWT-based technique

A recently introduced generalization of a Continuous Wavelet Transform-based (gCWT) algorithm for the detection of muscular activation from EMG recordings, previously tested in silico on synthetic and semi-synthetic data, is here applied for the detection of experimental data. Healthy young adults were requested to isometrically contract the upper trapezius while seated, and a visual bio-feedback was implemented to maintain the level of the upper trapezius activity between 5% and the 10% of the maximum voluntary contraction. Difference of duration between the estimated muscular activity and the force exertion captured by a pressure switch was calculated for both the gCWT technique and an amplitude-based technique, using the Teager-Kaiser Energy Operator (TKE). Difference of duration resulted in line with uncertainties associated with the variations in electromechanical delay (EMD) and relaxation-EMD reported in the literature, and they were found lower than those obtained with TKE

Lifting activity assessment using kinematic features and neural networks

Work-related low-back disorders (WLBDs) can be caused by manual lifting tasks. Wearable devices used to monitor these tasks can be one possible way to assess the main risk factors for WLBDs. This study aims at analyzing the sensitivity of kinematic data to the risk level changes, and to define an instrument-based tool for risk classification by using kinematic data and artificial neural networks (ANNs). Twenty workers performed lifting tasks, designed by following the rules of the revised NIOSH lifting equation, with an increasing lifting index (LI). From the acquired kinematic data, we computed smoothness parameters together with kinetic, potential and mechanical energy. We used ANNs for mapping different set of features on LI levels to obtain an automatic risk estimation during these tasks. The results show that most of the calculated kinematic indexes are significantly affected by changes in LI and that all the lifting condition pairs can be correctly distinguished. Furthermore, using specific set of features, different topologies of ANNs can lead to a reliable classification of the biomechanical risk related to lifting tasks. In particular, the training sets and numbers of neurons in each hidden layer influence the ANNs performance, which is instead independent from the numbers of hidden layers. Reliable biomechanical risk estimation can be obtained by using training sets combining body and load kinematic features

Trunk Muscle Coactivation in People with and without Low Back Pain during Fatiguing Frequency-Dependent Lifting Activities

Lifting tasks are manual material-handling activities and are commonly associated with work-related low back disorders. Instrument-based assessment tools are used to quantitatively assess the biomechanical risk associated with lifting activities. This study aims at highlighting different motor strategies in people with and without low back pain (LBP) during fatiguing frequency-dependent lifting tasks by using parameters of muscle coactivation. A total of 15 healthy controls (HC) and eight people with LBP performed three lifting tasks with a progressively increasing lifting index (LI), each lasting 15 min. Bilaterally erector spinae longissimus (ESL) activity and rectus abdominis superior (RAS) were recorded using bipolar surface electromyography systems (sEMG), and the time-varying multi-muscle coactivation function (TMCf) was computed. The TMCf can significantly discriminate each pair of LI and it is higher in LBP than HC. Collectively, our findings suggest that it is possible to identify different motor strategies between people with and without LBP. The main finding shows that LBP, to counteract pain, coactivates the trunk muscles more than HC, thereby adopting a strategy that is stiffer and more fatiguing

Global lower limb muscle coactivation during walking at different speeds. Relationship between spatio-temporal, kinematic, kinetic, and energetic parameters

Muscle coactivation is the mechanism that regulates the simultaneous activity of antagonist muscles around the same joint. During walking, muscle joint coactivation varies within the gait cycle according to the functional role of the lower limb joints. In the present study, we used a time-varying multi-muscle coactivation function (TMCf) with the aim of investigating the coactivation of 12 lower limb muscles and its relationship with the gait cycle, gait speed (low, self-selected, and fast), ground reaction force, gait variability, and mechanical energy consumption, and recovery in a sample of 20 healthy subjects. Results show that the TMCf is speed dependent and highly repeatable within and between subjects, similar to the vertical force profile, and negatively correlated with energy recovery and positively correlated with both energy consumption and balance-related gait parameters. These findings suggest that the global lower limb coactivation behavior could be a useful measure of the motor control strategy, limb stiffness, postural stability, energy efficiency optimization, and several aspects in pathological conditions

Global lower limb muscle coactivation during walking at different speeds: Relationship between spatio-temporal, kinematic, kinetic, and energetic parameters

Muscle coactivation is the mechanism that regulates the simultaneous activity of antagonist muscles around the same joint. During walking, muscle joint coactivation varies within the gait cycle according to the functional role of the lower limb joints. In the present study, we used a time-varying multi-muscle coactivation function (TMCf) with the aim of investigating the coactivation of 12 lower limb muscles and its relationship with the gait cycle, gait speed (low, self-selected, and fast), ground reaction force, gait variability, and mechanical energy consumption, and recovery in a sample of 20 healthy subjects. Results show that the TMCf is speed dependent and highly repeatable within and between subjects, similar to the vertical force profile, and negatively correlated with energy recovery and positively correlated with both energy consumption and balance-related gait parameters. These findings suggest that the global lower limb coactivation behavior could be a useful measure of the motor control strategy, limb stiffness, postural stability, energy efficiency optimization, and several aspects in pathological conditions

Trunk stability in fatiguing frequency-dependent lifting activities

Background: Work-related low-back disorders (WLBDs) are one of the most frequent and costly musculoskeletal conditions. It has been showed that WLBDs may occur when intervertebral or torso equilibrium is altered by a biomechanical perturbations or neuromuscular control error. The capacity to react to such disturbances is heavily determined by the spinal stability, provided by active and passive tissues and controlled by the central nervous system. Research question: This study aims to investigate trunk stability through the Lyapunov's maximum exponent during repetitive liftings in relation to risk level, as well as to evaluate its ability to discriminate these risk levels. Methods: Fifteen healthy volunteers performed fatiguing lifting tasks at three different frequencies corresponding to low, medium, and high risk levels according to the National Institute for Occupational Safety and Health (NIOSH) equation. We investigated changes in spinal stability during fatiguing lifting tasks at different risk levels using the maximum Lyapunov's index (λMax) computed from trunk accelerations recorded by placing three IMUs at pelvis, lower and upper spine levels. A two-way repeated-measures ANOVA was performed to determine if there was any significant effect on λMax among the three risk levels and the time (start, mid, and end of the task). Additionally, we examined the Pearson's correlation of λMax with the trunk muscle co-activation, computed from trunk sEMG. Results: Our findings show an increase in trunk stability with increasing risk level and as the lifting task progressed over time. A negative correlation between λMax and trunk co-activation was observed which illustrates that the increase in spinal stability could be partially attributed to increased trunk muscle co-activation. Significance: This study highlights the possibility of generating stability measures from kinematic data as risk assessment features in fatiguing tasks which may prove useful to detect the risk of developing work-related low back pain disorders and allow the implementation of early ergonomic interventions