Biomechanical characteristics of peripheral diabetic neuropathy: A systematic review and meta-analysis of findings from the gait cycle, muscle activity and dynamic barefoot plantar pressure

Diabetic peripheral neuropathy (DPN) is a major sequela of diabetes mellitus and may have a detrimental effect on the gait of people with this complication. DPN causes a disruption in the body’s sensorimotor system and is believed to affect up to 50% of patients with diabetes mellitus, dependent on the duration of diabetes. It has a major effect on morbidity and mortality. The peripheral nervous system controls the complex series of events in gait through somatic and autonomic functions, careful balancing of eccentric and concentric muscle contractions and a reliance on the sensory information received from the plantar surface. In this literature review focussing on kinetics, kinematics and posture during gait in DPN patients, we have identified an intimate link between DPN and abnormalities in gait and demonstrated an increased risk in falls for older patients with diabetes. As such, we have identified a need for further research on the role of gait abnormalities in the development of diabetic foot ulceration and subsequent amputations.

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Diabetic Neuropathy and Gait: A Review

Surface electromyography: Why, when and how to use it

 


La electromiografia de superficie: ?que es, que se busca con ella y como usarla?




 


 

 



Resumen
Through the use of electromyography, insights have been gained into the understanding of intentional and reactive motor behaviors. This technique posits the detection and analysis of the electromyogram (EMG); the electrical potential produced during muscle contractions. EMGs can be detected either directly, by inserting electrodes in the muscle tissue, or indirectly, with surface electrodes positioned on skin regions immediately above the muscle tissue. Because of its non-invasiveness, surface electrodes are more popular among sport scientists. Surface EMGs often convey information regarding muscle activation as, for example, the intensity of muscle contraction, the myoelectric manifestation of muscle fatigue and the recruitment of motor units. Anatomical and further physiological indications might be obtained when multiple electrodes are used to detect high-density surface electromyograms (HD-sEMG) from individual muscles. The recording of HD-sEMG allows for the identification of neuromuscular compartments, for the decomposition of EMGs into the action potentials of single motor units and for the robust estimation of the length of muscle fibers, the position of innervation zones, the conduction velocity of motor unit action potentials, the territory of motor units and the contribution of synergistic muscles to the force exerted over body joints. Besides providing an extensive repertoire of information, the use of high-density systems for the detection of surface EMGs revealed that interpretation of the conventional bipolar EMGs urges care. Thus, the aim of this review is to synthesize chief aspects concerning the detection and application of surface EMG and to predict possible directions for its use.


 
Resumen
La electromiografia permite comprender los comportamientos motores intencionales y automaticos. Esta tecnica se define como la deteccion y analisis del electromiograma (EMG), es decir, del potencial electrico producido durante las contracciones musculares. Los EMG pueden detectarse directamente, mediante la insercion de electrodos en el tejido muscular, o indirectamente, con electrodos de superficie colocados en zonas de la piel localizadas justo encima del tejido muscular. Por el hecho de ser un metodo no invasivo, los electrodos de superficie son muy populares entre los cientificos del deporte. Los EMG de superficie con frecuencia transmiten informacion sobre la activacion muscular como, por ejemplo, la intensidad de la contraccion muscular, la manifestacion mioelectrica de la fatiga muscular y el reclutamiento de unidades motoras. Con el objetivo de detectar electromiogramas de alta densidad de superficie (HDs-EMG) de los musculos individuales, pueden ser utilizados varios electrodos. Esto permite obtener informaciones anatomicas y fisiologicas complementarias. Mediante la descomposicion de los potenciales de accion del registro de HDs-EMG de las unidades motoras individuales, es posible la identificacion de los compartimentos neuromusculares, una estimacion precisa de la longitud de las fibras musculares, la posicion de las zonas de inervacion, la velocidad de conduccion del potencial de accion de la unidad motora, el territorio de las unidades motoras y la contribucion de los musculos sinergistas que actuan sobre las articulaciones del cuerpo. Ademas de proporcionar un amplio repertorio de informacion, el uso del EMG de superficie de alta densidad revelo que la interpretacion de los EMG bipolares convencionales necesita ser realizado con cuidado. De este modo, el objetivo de esta revision es discutir los principales aspectos sobre la deteccion y la aplicacion de EMG de superficie, asi como predecir posibles orientaciones para su uso.







Keywords
Electromiograma de superficie, Unidad motora, Musculo esqueletico, Electromiograma de superficie de alta densidad (HDs-EMG)


Palabras clave
Surface electromyogram, Motor unit, Skeletal muscle, High-density surface electromyogram (HD-sEMG)

https://ws072.juntadeandalucia.es/ojs/index.php/ramd/article/download/344/520

Surface electromyography: Why, when and how to use it

This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

Gait analysis is a systematic study of human locomotion, which can be utilized in variousapplications, such as rehabilitation, clinical diagnostics and sports activities. The various limitationssuch as cost, non-portability, long setup time, post-processing time etc., of the current gait analysistechniques have made them unfeasible for individual use. This led to an increase in research interestin developing smart insoles where wearable sensors can be employed to detect vertical groundreaction forces (vGRF) and other gait variables. Smart insoles are flexible, portable and comfortablefor gait analysis, and can monitor plantar pressure frequently through embedded sensors thatconvert the applied pressure to an electrical signal that can be displayed and analyzed further.Several research teams are still working to improve the insoles' features such as size, sensitivity ofinsoles sensors, durability, and the intelligence of insoles to monitor and control subjects' gait bydetecting various complications providing recommendation to enhance walking performance. Eventhough systematic sensor calibration approaches have been followed by different teams to calibrateinsoles' sensor, expensive calibration devices were used for calibration such as universal testingmachines or infrared motion capture cameras equipped in motion analysis labs. This paper providesa systematic design and characterization procedure for three different pressure sensors: forcesensitiveresistors (FSRs), ceramic piezoelectric sensors, and flexible piezoelectric sensors that canbe used for detecting vGRF using a smart insole. A simple calibration method based on a load cellis presented as an alternative to the expensive calibration techniques. In addition, to evaluate theperformance of the different sensors as a component for the smart insole, the acquired vGRF fromdifferent insoles were used to compare them. The results showed that the FSR is the most effectivesensor among the three sensors for smart insole applications, whereas the piezoelectric sensors canbe utilized in detecting the start and end of the gait cycle. This study will be useful for any researchgroup in replicating the design of a customized smart insole for gait analysis.

https://www.mdpi.com/1424-8220/20/4/957/pdf

A Systematic Approach to the Design and Characterization of a Smart Insole for Detecting Vertical Ground Reaction Force (vGRF) in Gait Analysis

Introduction: Changes in gait cadence caused by challenging situations in daily life might induce higher demand for strength and propulsion in diabetic neuropathic (DN) sub- jects. Methods: Forty-six subjects (healthy and DN) walked at two cadences (self-selected and 25% higher). Kinematic and electromyographic data were obtained from lower limbs and compared across the gait cycle. Results: DN subjects showed a delayed peak in plantarflexor activity along the whole cycle (irre- spective of cadence) compared with healthy subjects. However, during the imposed cadence, DN individuals showed reduced ankle range of motion along the entire cycle compared with the self-selected condition and healthy individuals walking at both cadences (P ¼ 0.002). Conclusions: These findings suggest that when diabetic individuals face a new challenging situation that induces a higher demand for muscle strength and propul- sion, the necessary range of motion and neuromuscular control around distal joints are insufficient. Muscle Nerve 44: 258-268, 2011 The biomechanical pattern of gait is highly repeti- tive, cyclic, and has low variation across steps as well as across days. 1 This feature allows us to use gait analysis as an experimental model to investi- gate the influence of the environment, such as real-life challenges and perturbations, and biology, such as neuromuscular diseases. These two factors might impair both the coordinated harmonic action of the lower limbs and the neuromuscular strategies adopted for the control of locomotion. Diabetic neuropathy (DN) impairs both the soma- tosensory and motor control systems, 2,3 and affects the amount and quality of the sensory information that is essential for the complexity involved in gait generation and control. 4 The regularity of normal gait allows for the establishment of basic biome- chanical criteria to distinguish between normal and pathologic patterns of movement, such as gait patterns in DN individuals.

Mitie E.I. Otuzi

Papers

Electromyography and kinematic changes of gait cycle at different cadences in diabetic neuropathic individuals

A method for better positioning bipolar electrodes for lower limb EMG recordings during dynamic contractions