Background: Only 2% of falls in older adults result in serious injuries (i.e., hip fracture). Therefore, it is important to differentiate injurious versus non-injurious falls, which is critical to develop effective interventions for injury prevention. Objects: The purpose of this study was to a. extract the best features of surface electromyography (sEMG) for classification of injurious falls, and b. find a best model provided by data mining techniques using the extracted features. Methods: Twenty young adults self-initiated falls and landed sideways. Falling trials were consisted of three initial fall directions (forward, sideways, or backward) and three knee positions at the time of hip impact (the impacting-side knee contacted the other knee (“knee together”) or the mat (“knee on mat”), or neither the other knee nor the mat was contacted by the impacting-side knee (“free knee”). Falls involved “backward initial fall direction” or “free knee” were defined as “injurious falls” as suggested from previous studies. Nine features were extracted from sEMG signals of four hip muscles during a fall, including integral of absolute value (IAV), Wilson amplitude (WAMP), zero crossing (ZC), number of turns (NT), mean of amplitude (MA), root mean square (RMS), average amplitude change (AAC), difference absolute standard deviation value (DASDV). The decision tree and support vector machine (SVM) were used to classify the injurious falls. Results: For the initial fall direction, accuracy of the best model (SVM with a DASDV) was 48%. For the knee position, accuracy of the best model (SVM with an AAC) was 49%. Furthermore, there was no model that has sensitivity and specificity of 80% or greater. Conclusion: Our results suggest that the classification model built upon the sEMG features of the four hip muscles are not effective to classify injurious falls. Future studies should consider other data mining techniques with different muscles.

EMG is used in rehabilitation research to provide a method to infer muscle function. This paper will present an introduction to interpretation of electromyography (EMG) data for physical therapists. It is important for the physical therapist to have an understanding of the collection and reduction of raw electrical data from the muscle to allow the physical therapist to interpret findings in a research report, and improve planning of clinical research projects with respect to data collection. We will discuss factors that affect the type of EMG collected and the ways in which various common methods of data reduction will impact the findings from a study that uses EMG.

This study tested whether repeated measurement of median frequency (MDF)-related variables could express the muscle power changes during a 12-week DeLome strengthening program, by using consecutive overlapping FFT (Fast Fourier transformation) and integrated EMG (IEMG) from surface EMG data for isometric and isotonic exercise. To evaluate the effect of training, the following were recorded every 3 weeks for the elbow flexors and knee extensors of 5 healthy male volunteers: MVC, lRM, limb circumference, and surface EMG during isometric MVC or isotonic contraction at 10RM load. From the EMG data, IEMG and variables from a regression analysis between MDF and time were obtained. MVC, lRM, IEMG, and initial MDF increased linearly over the training period. The fatigue index and slope of the regression line increased temporarily until the 6th week and decreased thereafter. From these results, there appeared to be enhanced neural recruitment of fast twitch fibers in the first 6 weeks and continued enhancement in the recruitment and hypertrophy of fast twitch fibers, which led to increased fatigue resistance, over the last 6 weeks. Accordingly, the MDF and IEMG analysis technique could demonstrate the effect of the program detected significant changes in both isometric and isotonic contractions. EMG analysis methods can be used to estimate the electrophysiological and histological changes in skeletal muscles during a strengthening program.