Dynamic forces acting on the lumbar spine during manual handling. Can they be estimated using electromyographic techniques alone?

STUDY DESIGN: Compressive loading of the lumbar spine was analyzed using electromyographic, movement analysis, and force-plate techniques. OBJECTIVES: To evaluate the inertial forces that cannot be detected by electromyographic techniques alone. SUMMARY OF BACKGROUND DATA: Links between back pain and manual labor have stimulated attempts to measure spine compressive loading. However, direct measurements of intradiscal pressure are too invasive, and force plates too cumbersome for use in the workplace. Electromyographic techniques are noninvasive and portable, but ignore certain inertial forces. METHODS: Eight men lifted boxes weighing 6.7 and 15.7 kg from the ground, while joint moments acting about L5-S1 were quantified 1) by using a linked-segment model to analyze data from Kistler force plates and a Vicon movement-analysis system, and 2) by measuring the electromyographic activity of the erector spinae muscles, correcting it for contraction speed and comparing it to moment generation during static contractions. The linked-segment model was used to calculate the "axial thrust," defined as the component of the L5-S1 reaction force that acts along the axis of the spine and that is unrelated to trunk muscle activity or static body weight. RESULTS: Peak extensor moments predicted by the two techniques were similar and equivalent to spinal compressive forces of 2.9-4.8 kN. The axial thrust "hidden" from the electromyographic technique was negligible during slow lifts, and remained below 4% of peak spinal compression even during fast heavy lifts. Peak axial thrust was proportional to the peak vertical ground reaction (R2 = 0.74). CONCLUSIONS: Electromyographic techniques can measure dynamic spinal loading, but additional force-plate data would improve accuracy slightly during lifts requiring a vigorous upward thrust from the legs

Exploiting Prior Knowledge in Compressed Sensing Wireless ECG Systems

Recent results in telecardiology show that compressed sensing (CS) is a promising tool to lower energy consumption in wireless body area networks for electrocardiogram (ECG) monitoring. However, the performance of current CS-based algorithms, in terms of compression rate and reconstruction quality of the ECG, still falls short of the performance attained by state-of-the-art wavelet based algorithms. In this paper, we propose to exploit the structure of the wavelet representation of the ECG signal to boost the performance of CS-based methods for compression and reconstruction of ECG signals. More precisely, we incorporate prior information about the wavelet dependencies across scales into the reconstruction algorithms and exploit the high fraction of common support of the wavelet coefficients of consecutive ECG segments. Experimental results utilizing the MIT-BIH Arrhythmia Database show that significant performance gains, in terms of compression rate and reconstruction quality, can be obtained by the proposed algorithms compared to current CS-based methods.Comment: Accepted for publication at IEEE Journal of Biomedical and Health Informatic

Deep Learning for Processing Electromyographic Signals: a Taxonomy-based Survey

Deep Learning (DL) has been recently employed to build smart systems that perform incredibly well in a wide range of tasks, such as image recognition, machine translation, and self-driving cars. In several fields the considerable improvement in the computing hardware and the increasing need for big data analytics has boosted DL work. In recent years physiological signal processing has strongly benefited from deep learning. In general, there is an exponential increase in the number of studies concerning the processing of electromyographic (EMG) signals using DL methods. This phenomenon is mostly explained by the current limitation of myoelectric controlled prostheses as well as the recent release of large EMG recording datasets, e.g. Ninapro. Such a growing trend has inspired us to seek and review recent papers focusing on processing EMG signals using DL methods. Referring to the Scopus database, a systematic literature search of papers published between January 2014 and March 2019 was carried out, and sixty-five papers were chosen for review after a full text analysis. The bibliometric research revealed that the reviewed papers can be grouped in four main categories according to the final application of the EMG signal analysis: Hand Gesture Classification, Speech and Emotion Classification, Sleep Stage Classification and Other Applications. The review process also confirmed the increasing trend in terms of published papers, the number of papers published in 2018 is indeed four times the amount of papers published the year before. As expected, most of the analyzed papers (≈60 %) concern the identification of hand gestures, thus supporting our hypothesis. Finally, it is worth reporting that the convolutional neural network (CNN) is the most used topology among the several involved DL architectures, in fact, the sixty percent approximately of the reviewed articles consider a CNN