Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor Muscles.

Current methods for evaluating fatigue separately assess intramuscular changes in individual muscles from corresponding alterations in movement output. The purpose of this study is to investigate if a system-based monitoring paradigm, which quantifies how the dynamic relationship between the activity from multiple muscles and force changes over time, produces a viable metric for assessing fatigue. Improvements made to the paradigm to facilitate online fatigue assessment are also discussed. Eight participants performed a static elbow extension task until exhaustion, while surface electromyography (sEMG) and force data were recorded. A dynamic time-series model mapped instantaneous features extracted from sEMG signals of multiple synergistic muscles to extension force. A metric, called the Freshness Similarity Index (FSI), was calculated using statistical analysis of modeling errors to reveal time-dependent changes in the dynamic model indicative of performance degradation. The FSI revealed strong, significant within-individual associations with two well-accepted measures of fatigue, maximum voluntary contraction (MVC) force (rrm=-0.86) and ratings of perceived exertion (RPE) (rrm=0.87), substantiating the viability of a system-based monitoring paradigm for assessing fatigue. These findings provide the first direct and quantitative link between a system-based performance degradation metric and traditional measures of fatigue.

Long-Term Modeling and Monitoring of Neuromusculoskeletal System Performance Using Tattoo-Like EMG Sensors.

This paper introduces stretchable, long-term wearable, tattoo-like dry surface electrodes for highly repeatable electromyography (EMG). The tattoo-like sensors are hair thin, skin compliant and can be laminated on human skin just like a temporary transfer tattoo, which enables multi-day noninvasive but intimate contact with the skin even under severe skin deformation. The new electrodes were used to facilitate a system-based approach to tracking of long-term fatiguing and recovery processes in a human neuromusculoskeletal (NMS) system, which was based on establishing an autoregressive moving average model with exogenous inputs (ARMAX model) relating signatures extracted from the surface electromyogram (sEMG) signals collected using the tattoo-like sensors, and the corresponding hand grip force (HGF) serving as the model output. Performance degradation of the relevant NMS system was evaluated by tracking the evolution of the errors of the ARMAX model established using the data corresponding to the rested (fresh) state of any given subject. Results from several exercise sessions clearly showed repeated patterns of fatiguing and resting, with a notable point that these patterns could now be quantified via dynamic models relating the relevant muscle signatures and NMS outputs.

Topology preservation and cooperative learning in identification of multiple model systems.

The self-organizing network (SON)-based multiple model system is a recently proposed method for identifying the dynamics of a general nonlinear system. It has been observed by researchers that cooperative learning among neighboring regions is sometimes important for the success of identification of a nonlinear system under the multiple model system framework. In this paper, we intend to formally evaluate the effects of cooperative learning and topology preservation in identification of multiple model system based on SON. The results of the mathematical analysis supports the heuristic that a good learning strategy for identifying the local model parameters of a SON-based multiple model system is to choose a neighborhood function whose effective region is initially wider and is reduced gradually during learning. An example of nonlinear function approximation is also provided at the end of this paper to demonstrate the results of the mathematical analysis.

Inaudible temporomandibular joint vibrations.

The aim was to test the hypothesis that inaudible vibrations with significant amounts of energy increasing during jaw movements can be recorded in the temporomandibular joint (TMJ) area. Twenty one subjects, who could perform wide opening movements without feeling discomfort, 12 with and 9 without TMJ sounds audible at conventional auscultation with a stethoscope, were included. Recordings were made during opening-closing, 2/s without tooth contact, and during mandibular rest, using accelerometers with a flat frequency response between the filter cutoff frequencies 0.1 Hz and 1000 Hz. The signals were digitized using a 24 bits card and sampled with the rate 96000 Hz. Power spectral analyses, and independent and paired samples t-tests were used in the analysis of the vibration power observed in frequency bands corresponding to audible and inaudible frequencies. An alpha-level of 5% was chosen for accepting a difference as being significant. In the group with audible sounds, about 47% of the total vibration energy was in the inaudible area below 20 Hz during opening-closing and about 76% during mandibular rest. In the group without audible sounds, the corresponding proportions were significantly different, 85% vs. 69%. The energy content of the vibrations, both those below and those above 20 Hz, increased significantly during jaw movement in both groups. Furthermore, percentage of signal energy above 20 Hz showed a noticeable increase in the group of subjects with audible sounds. This can physically be explained by decreased damping properties of damaged tissues surrounding the TMJ. Vibrations in the TMJ area can be observed with significant portions in the inaudible area below 20 Hz both during mandibular rest and during jaw movements whether or not the subjects have audible joint sounds. Further studies are needed to identify sources and evaluate possible diagnostic value.