Robot-assisted assessment of muscle strength

Deep Learning Model Coupling Wearable Bioelectric and Mechanical Sensors for Refined Muscle Strength Assessment

Muscle strength (MS) is related to our neural and muscle systems, essential for clinical diagnosis and rehabilitation evaluation. Although emerging wearable technology seems promising for MS assessment, problems still exist, including inaccuracy, spatiotemporal differences, and analyzing methods. In this study, we propose a wearable device consisting of myoelectric and strain sensors, synchronously acquiring surface electromyography and mechanical signals at the same spot during muscle activities, and then employ a deep learning model based on temporal convolutional network (TCN) + Transformer (Tcnformer), achieving accurate grading and prediction of MS. Moreover, by combining with deep clustering, named Tcnformer deep cluster (TDC), we further obtain a 25-level classification for MS assessment, refining the conventional 5 levels. Quantification and validation showcase a patient's postoperative recovery from level 3.2 to level 3.6 in the first few days after surgery. We anticipate that this system will importantly advance precise MS assessment, potentially improving relevant clinical diagnosis and rehabilitation outcomes.

SpasticSim: a synthetic data generation method for upper limb spasticity modelling in neurorehabilitation

In neurorehabilitation, assessment of functional problems is essential to define optimal rehabilitation treatments. Usually, this assessment process requires distinguishing between impaired and non-impaired behavior of limbs. One of the common muscle motor disorders affecting limbs is spasticity, which is complicated to quantify objectively due to the complex nature of motor control. Thus, the lack of heterogeneous samples of patients constituting an acceptable amount of data is an obstacle which is relevant to understanding the behavior of spasticity and, consequently, quantifying it. In this article, we use the 3D creation suite Blender combined with the MBLab add-on to generate synthetic samples of human body models, aiming to be as sufficiently representative as possible to real human samples. Exporting these samples to OpenSim and performing four specific upper limb movements, we analyze the muscle behavior by simulating the six degrees of spasticity contemplated by the Modified Ashworth Scale (MAS). The complete dataset of patients and movements is open-source and available for future research. This approach advocates the potential to generate synthetic data for testing and validating musculoskeletal models.

Low intensity exercise training in patients with amyotrophic lateral sclerosis - factors influencing eligibility and compliance to the clinical trial

BACKGROUND

To date, there are no evidence-based recommendations for physical therapy in amyotrophic lateral sclerosis (ALS). The reason is a low number of related clinical trials (CTs), restricted sample sizes and a high dropout rate. It may influence the profile of the participants, while the final results might not translate to the general ALS population.

OBJECTIVE

To analyze factors affecting the ALS patients' enrollment and retention to the study, and to describe a profile of participants as compared to the eligible group.

METHODS

A total of 104 ALS patients were offered participation in a CT of low-intensity exercises at home. Forty-six patients were recruited. Demographic and clinical data (El Escorial criteria, site of onset, diagnosis delay, disease duration, amyotrophic lateral sclerosis functional rating scale - revised [ALSFRS-R], Medical Research Council [MRC], hand-held dynamometry) were analyzed every 3 months.

RESULTS

Male gender, younger age and a higher ALSFRS predicted enrollment, while male gender, higher ALSFRS-R and MRC predicted retention in the study. A long commute to the study site and a fast disease progression were the main reasons influencing both enrollment and retention. Despite a high dropout rate, study participants were representative for the general ALS population.

CONCLUSION

The above demographic, clinical and logistic factors need to be considered when designing studies in ALS population.

Use of Lower Limb Exoskeletons as an Assessment Tool for Human Motor Performance: A Systematic Review

Exoskeletons are a promising tool to support individuals with a decreased level of motor performance. Due to their built-in sensors, exoskeletons offer the possibility of continuously recording and assessing user data, for example, related to motor performance. The aim of this article is to provide an overview of studies that rely on using exoskeletons to measure motor performance. Therefore, we conducted a systematic literature review, following the PRISMA Statement guidelines. A total of 49 studies using lower limb exoskeletons for the assessment of human motor performance were included. Of these, 19 studies were validity studies, and six were reliability studies. We found 33 different exoskeletons; seven can be considered stationary, and 26 were mobile exoskeletons. The majority of the studies measured parameters such as range of motion, muscle strength, gait parameters, spasticity, and proprioception. We conclude that exoskeletons can be used to measure a wide range of motor performance parameters through built-in sensors, and seem to be more objective and specific than manual test procedures. However, since these parameters are usually estimated from built-in sensor data, the quality and specificity of an exoskeleton to assess certain motor performance parameters must be examined before an exoskeleton can be used, for example, in a research or clinical setting.

A review of recent advances in metal ion hydrogels: mechanism, properties and their biological applications

The mechanisms, common properties and biological applications of different types of metal ion hydrogels are summarized.

Stretchable, Self-Healing, and Skin-Mounted Active Sensor for Multipoint Muscle Function Assessment

Assessment of muscle function is an essential indicator for estimating elderly health, evaluating motor function, and instructing rehabilitation training, which also sets urgent requirements for mechanical sensors with superior quantification, accuracy, and reliability. To overcome the rigidity and vulnerability of traditional metallic electrodes, we synthesize an ionic hydrogel with large deformation tolerance and fast self-healing ability. And we propose a stretchable, self-healing, and skin-mounted (Triple S) active sensor (TSAS) based on the principles of electrostatic induction and electrostatic coupling. The skin modulus-matched TSAS provides outstanding sensing properties: maximum output voltage of 78.44 V, minimal detection limit of 0.2 mN, fast response time of 1.03 ms, high signal-to-noise ratio and excellent long-term service stability. In training of arm muscle, the functional signals of biceps and triceps brachii muscles as well as the joint dexterity of bending angle can be acquired simultaneously through TSAS. The signal can also be sent wirelessly to a terminal for analysis. With the characteristics of high sensitivity, reliability, convenience, and low-cost, TSAS shows its potential to be the next-generation procedure for real-time assessment of muscle function and rehabilitation training.

A novel adaptable isometric strength analysis and exercise development system design

In this study, a low-cost and adaptable isometric strength measurement and exercise development system are described. The implemented system consists of mechanical structure, force measurement sensor, electronic circuit, and computer software. Isometric-isotonic (via spring resistance) strength analysis and various exercise programs can be applied with the system. The developed system has a lower cost compared to its counterparts in the literature and has a structure that can be adapted to different machines and measuring methods. The operability and reliability of the isometric strength measurement and exercise development system have been proven by calibration tests.

Overuse-Related Injuries of the Musculoskeletal System: Systematic Review and Quantitative Synthesis of Injuries, Locations, Risk Factors and Assessment Techniques

Overuse-related musculoskeletal injuries mostly affect athletes, especially if involved in preseason conditioning, and military populations; they may also occur, however, when pathological or biological conditions render the musculoskeletal system inadequate to cope with a mechanical load, even if moderate. Within the MOVIDA (Motor function and Vitamin D: toolkit for risk Assessment and prediction) Project, funded by the Italian Ministry of Defence, a systematic review of the literature was conducted to support the development of a transportable toolkit (instrumentation, protocols and reference/risk thresholds) to help characterize the risk of overuse-related musculoskeletal injury. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach was used to analyze Review papers indexed in PubMed and published in the period 2010 to 2020. The search focused on stress (overuse) fracture or injuries, and muscle fatigue in the lower limbs in association with functional (biomechanical) or biological biomarkers. A total of 225 Review papers were retrieved: 115 were found eligible for full text analysis and led to another 141 research papers derived from a second-level search. A total of 183 papers were finally chosen for analysis: 74 were classified as introductory to the topics, 109 were analyzed in depth. Qualitative and, wherever possible, quantitative syntheses were carried out with respect to the literature review process and quality, injury epidemiology (type and location of injuries, and investigated populations), risk factors, assessment techniques and assessment protocols.

Robot-Aided Systems for Improving the Assessment of Upper Limb Spasticity: A Systematic Review

Spasticity is a motor disorder that causes stiffness or tightness of the muscles and can interfere with normal movement, speech, and gait. Traditionally, the spasticity assessment is carried out by clinicians using standardized procedures for objective evaluation. However, these procedures are manually performed and, thereby, they could be influenced by the clinician's subjectivity or expertise. The automation of such traditional methods for spasticity evaluation is an interesting and emerging field in neurorehabilitation. One of the most promising approaches is the use of robot-aided systems. In this paper, a systematic review of systems focused on the assessment of upper limb (UL) spasticity using robotic technology is presented. A systematic search and review of related articles in the literature were conducted. The chosen works were analyzed according to the morphology of devices, the data acquisition systems, the outcome generation method, and the focus of intervention (assessment and/or training). Finally, a series of guidelines and challenges that must be considered when designing and implementing fully-automated robot-aided systems for the assessment of UL spasticity are summarized.