Assessment of Self-report, Palpation, and Surface Electromyography Dataset During Isometric Muscle Contraction.

Measuring muscle fatigue involves assessing various components within the motor system. While subjective and sensor-based measures have been proposed, a comprehensive comparison of these assessment measures is currently lacking. This study aims to bridge this gap by utilizing three commonly used measures: participant self-reported perceived muscle fatigue scores, a sports physiotherapist's manual palpation-based muscle tightness scores, and surface electromyography sensors. Compensatory muscle fatigue occurs when one muscle group becomes fatigued, leading to the involvement and subsequent fatigue of other muscles as they compensate for the workload. The evaluation of compensatory muscle fatigue focuses on nine different upper body muscles selected by the sports physiotherapist. With a cohort of 30 male subjects, this study provides a valuable dataset for researchers and healthcare practitioners in sports science, rehabilitation, and human performance. It enables the exploration and comparison of diverse methods for evaluating different muscles in isometric contraction.

The mental health-related barriers and benefits to exercise in adults with and without chronic pain.

Background: Qualitative evidence points to the importance of both mental health-related barriers and benefits to exercise in chronic pain, yet this bidirectional relationship has not been established quantitatively. Methods: 89 adults with chronic pain (75 female, Age: M = 34.7, SD=13.2), and 89 demographically-matched individuals without chronic pain (73 female, Age: M = 32.0, SD=13.3) self-reported demographic and health information, mental health-related barriers and benefits to exercise, and leisure-time exercise activity. Results: Adults with chronic pain had significantly higher scores on mental health-related barriers to exercise, and lower leisure-time exercise participation than adults without chronic pain. The groups did not differ on mental health-related benefits of exercise scores. Benefits scores positively predicted exercise, yet there was a significant negative interaction between pain and benefit scores, indicating a weaker positive relationship between benefits and exercise for adults with chronic pain than for those without chronic pain. Barrier scores significantly negatively predicted exercise engagement, but did not interact significantly with chronic pain. Conclusion: Mental health-related barriers and benefits to exercise are important considerations when prescribing exercise for adults with chronic pain. Adults with chronic pain may require individualised support to address mental health-related barriers to leisure-time exercise.

Evaluation of ceiling-supported back harnesses in preventing injury in sheep shearing.

Lower back injuries are a significant global problem. They are particularly common in occupations that require prolonged or repetitive spinal flexion. Sheep shearing is one such occupation and the prevalence of back injuries is severe. Ceiling-supported back harnesses are a commonly used safety device in this occupation but its effectiveness in sheep shearing tasks has yet to be quantified. It is likely that accumulated and time-dependent changes in kinematics and neuromuscular control are relevant in the development of many lower back injuries. This is supported by the literature in sheep shearing, where 68% more injuries occur towards the end of the working day compared to the start. This means that data collected over a full working day is beneficial for measuring the effectiveness of safety interventions. The previous research in safety interventions in shearing have not collected data for more than 15 minutes, and do not adequately address longer term effects. This study compares the effects of wearing a ceiling-supported back harness on shearer kinematics and muscle activity, from the collected data over a full working day and incorporating time-of-day effects. The outcome shows that the use of ceiling-supported back harness results in improvements in kinematic features, but also an increase in muscle activity and fatigue.

A Weak Monotonicity Based Muscle Fatigue Detection Algorithm for a Short-Duration Poor Posture Using sEMG Measurements.

Muscle fatigue is usually defined as a decrease in the ability to produce force. The surface electromyography (sEMG) signals have been widely used to provide information about muscle activities including detecting muscle fatigue by various data-driven techniques such as machine learning and statistical approaches. However, it is well-known that sEMGs are usually weak signals with a smaller amplitude and a lower signal-to-noise ratio, making it difficult to apply the traditional signal processing techniques. In particular, the existing methods cannot work well to detect muscle fatigue coming from static poses. This work exploits the concept of weak monotonicity, which has been observed in the process of fatigue, to robustly detect muscle fatigue in the presence of measurement noises and human variations. Such a population trend methodology has shown its potential in muscle fatigue detection as demonstrated by the experiment of a static pose.

Effective Assessments of a Short-Duration Poor Posture on Upper Limb Muscle Fatigue Before Physical Exercise.

A forward head and rounded shoulder posture is a poor posture that is widely seen in everyday life. It is known that sitting in such a poor posture with long hours will bring health issues such as muscle pain. However, it is not known whether sitting in this poor posture for a short period of time will affect human activities. This paper investigates the effects of a short-duration poor posture before some typical physical activities such as push-ups. The experiments are set up as follows. Fourteen male subjects are asked to do push-ups until fatigue with two surface electromyography (sEMG) at the upper limb. Two days later, they are asked to sit in this poor posture for 15 min with eight sEMG sensors located at given back muscles. Then they do the push-ups after the short-duration poor posture. The observations from the median frequency of sEMG signals at the upper limb indicate that the short-duration poor posture does affect the fatigue procedure of push-ups. A significant decreasing trend of the performance of push-ups is obtained after sitting in this poor posture. Such effects indicate that some parts of the back muscles indeed get fatigued with only 15 min sitting in this poor posture. By further investigating the time-frequency components of sEMG of back muscles, it is observed that the low and middle frequencies of sEMG signals from the infraspinatus muscle of the dominant side are demonstrated to be more prone to fatigue with the poor posture. Although this study focuses only on push-ups, similar experiments can be arranged for other physical exercises as well. This study provides new insights into the effect of a short-duration poor posture before physical activities. These insights can be used to guide athletes to pay attention to postures before physical activities to improve performance and reduce the risk of injury.

Enabling context aware data analysis for long-duration repetitive stooped work through human activity recognition in sheep shearing.

There is evidence to suggest that changes in kinematics and neuromuscular control in activities that take place over long periods of time lead to increased injury risk. The collection of biometric data over long time periods could provide insight into these injuries. However, it is difficult to analyse long period biometric data for occupations as the analysis depends on the activity being performed, and it is not practical to manually label the amount of data required. A sufficiently accurate human activity recognition algorithm can provide a means to segment the activities and allow this analysis, but the classification must be robust to the inter-individual differences, as well as the intra-individual variations in movement over time that are the target of analysis. This work presents a person-independent human activity recognition algorithm for sheep shearing using a Hidden Markov Model with physical features that are identified to be relevant to spinal movement quality. The classifier achieved an F1 score of 96.47% in identifying the shearing task.