Effects of 4-Week Intensive Active-Resistive Training with an EMG-Based Exoskeleton Robot on Muscle Strength in Older People: A Pilot Study

All you need to know about sarcopenia: a short guide for an internal medicine physician in questions and answers

Sarcopenia is associated with social, economic, and individual burdens, including loss of independence, poor quality of life, and disability. In a short period of time, ideas about sarcopenia transformed from geriatric syndrome to disease. Initially, sarcopenia was considered in the context of gradual age-related deterioration in the functioning of all physiological systems. Over the years, it became clear that it can develop a second time, as a consequence of various diseases and pathological conditions.

To date, there have been no generally accepted diagnostic criteria for sarcopenia. There are several tests and tools available for screening sarcopenia, the choice of which depends on physical capabilities of the patient, capabilities of the medical institution, and the purpose for which it is detected (research or clinical practice).

From the point of view of human health, sarcopenia increases the risk of falls and fractures; impairs the ability to perform daily activities; is associated with the progression of major diseases and cognitive impairments; leads to movement disorders; contributes to a decrease in the quality of life, loss of independence or a need for long-term care. The presence of sarcopenia increases both the risk of hospitalization and hospitalization costs.

The aim of the literature review is to provide an analysis of up-to-date information on the causes, pathogenesis, screening, diagnosis, treatment, and consequences of sarcopenia, myosteatosis, and sarcopenic obesity. The search for literature containing information on relevant studies was conducted in PubMed and Google Scholar by the following keywords: sarcopenia, dynapenia, myosteatosis, sarcopenic obesity, nutritional status, malnutrition.

The Influence of EMG-Triggered Robotic Movement on Walking, Muscle Force and Spasticity after an Ischemic Stroke

Background and Objectives: Application of the EMG-driven robotic training in everyday therapeutic processes is a modern and innovative form of neurorehabilitation among patients after stroke. Active participation of the patient contributes to significantly higher activation of the sensorimotor network during active motor control rather than during passive movement. The study objective was to determine the effect of electromyographic triggering (EMG-triggered) robotic rehabilitation device treatment on walking, muscle force, and spasticity after an ischemic stroke. Materials and Methods: A total of 60 participants with impaired motor function and gait after subacute stroke were included in the study. Each patient was randomly assigned to an intervention or control group (IG or CG). All patients, except standard therapy, underwent 1 additional session of therapy per day, 5 days a week for 6 weeks. IG had 30 min of training on the robot, while CG received exercises on the lower limb rotor. The subjects were assessed with Timed Up and Go Test (TUG), Ashworth scale, knee range of motion (ROM), Lovett Scale, and tight circumference at baseline and at weeks 2, 4, and 6. Results: For seven parameters, the values credibly increased between consecutive measurements, and for the Ashworth scale, they credibly decreased. The biggest changes were observed for the measurements made with Lovett scale. The average thigh circumference as measured 5 and 15 cm above the knee increased credibly more in the robot condition, as compared to control condition. Additionally, the decrease in Ashworth values over time, although statistically credible in both groups, was credibly higher in the robot condition. Conclusion: The inclusion of the EMG-triggered neurorehabilitation robot in the patient's daily rehabilitation plan has a positive effect on outcomes of the treatment. Both proposed rehabilitation protocols significantly improved patients' condition regarding all measured outcomes, but the spasticity and thigh circumference improved significantly better in the robotic group in comparison to controls.

Adaptive robot mediated upper limb training using electromyogram-based muscle fatigue indicators

Studies on improving the adaptability of upper limb rehabilitation training do not often consider the implications of muscle fatigue sufficiently. In this study, electromyogram features were used as fatigue indicators in the context of human-robot interaction. They were utilised for auto-adaptation of the task difficulty, which resulted in a prolonged training interaction. The electromyogram data was collected from three gross-muscles of the upper limb in 30 healthy participants. The experiment followed a protocol for increasing the muscle strength by progressive strength training, that was an implementation of a known method in sports science for muscle training, in a new domain of robotic adaptation in muscle training. The study also compared how the participants in three experimental conditions perceived the change in task difficulty levels. One task benefitted from robotic adaptation (Intervention group) where the robot adjusted the task difficulty. The other two tasks were control groups 1 and 2. There was no difficulty adjustment at all in Control 1 group and the difficulty was adjusted manually in Control 2 group. The results indicated that the participants could perform a prolonged progressive strength training exercise with more repetitions with the help of a fatigue-based robotic adaptation, compared to the training interactions, which were based on manual/no adaptation. This study showed that it is possible to alter the level of the challenge using fatigue indicators, and thus, increase the interaction time. The results of the study are expected to be extended to stroke patients in the future by utilising the potential for adapting the training difficulty according to the patient's muscular state, and also to have a large number repetitions in a robot-assisted training environment.

Gaussian mixture model based clustering of Manual muscle testing grades using surface Electromyogram signals

Muscle strength testing has long been an important assessment procedure in rehabilitation setups, though the subjectivity and standardization of this procedure has been widely debated. To address this issue, this study involves the use of Electromyogram (EMG) features that are intuitively related to muscle strength to classify Manual muscle testing (MMT) grades of '4 -', '4', '4 + ' and '5' of the Medical Research Council scale. MMT was performed on Tibialis anterior muscle of 50 healthy participants whose MMT grades and EMG were simultaneously acquired. Chi square goodness of fit and Spectrum Decomposition of Graph Laplacian (SPEC) feature selection algorithms are used in selecting five features, namely Integrated EMG, Root Mean Square EMG, Waveform Length, Wilsons' amplitude and Energy. Gaussian Mixture Model (GMM) approach is used for unsupervised clustering into one of the grades. Internal cluster evaluation resulted in Silhouette score of 0.76 and Davies Bouldin Index of 0.42 indicating good cluster separability. Agreement between the machine-based grade and manual grade has been quantified using Cohens' Kappa coefficient. A value of '0.44' has revealed a moderate agreement, with greater differences reported in grading '4' and '4 + ' strength levels. The comparative advantage of EMG based grading over the manual method has been proved. The suggested method can be extended for muscle strength testing of all muscles across different age groups to assist physicians in evaluating patient strength and plan appropriate strength conditioning exercises as a part of rehabilitative assessment.

Assistive technologies to overcome sarcopenia in ageing

Sarcopenia is an age-related decline in skeletal muscle mass and function that results in disability and loss of independence. It affects up to 30% of older adults. Exercise (particularly progressive resistance training) and nutrition are key strategies in preventing and reversing declines in muscle mass, strength and power during ageing, but many sarcopenic older adults fail to meet recommended levels of both physical activity and dietary nutrient intake. Assistive technology (AT) describes devices or systems used to maintain or improve physical functioning. These may help sarcopenic older adults to maintain independence, and also to achieve adequate physical activity and nutrition. There is a paucity of research exploring the use of AT in sarcopenic patients, but there is evidence that AT, including walking aids, may reduce functional decline in other populations with disability. Newer technologies, such as interactive and virtual reality games, as well as wearable devices and smartphone applications, smart homes, 3D printed foods, exoskeletons and robotics, and neuromuscular electrical stimulation also hold promise for improving engagement in physical activity and nutrition behaviours to prevent further functional declines. While AT may be beneficial for sarcopenic patients, clinicians should be aware of its potential limitations. In particular, there are high rates of patient abandonment of AT, which may be minimised by appropriate training and monitoring of use. Clinicians should preferentially prescribe AT devices which promote physical activity. Further research is required in sarcopenic populations to identify strategies for effective use of current and emerging AT devices.