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In-Vivo Measurement of Muscle Tension: Dynamic Properties of the MC Sensor during Isometric Muscle Contraction

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      Skeletal muscle is the largest tissue structure in our body and plays an essential role for producing motion through integrated action with bones, tendons, ligaments and joints, for stabilizing body position, for generation of heat through cell respiration and for blood glucose disposal. A key function of skeletal muscle is force generation. Non-invasive and selective measurement of muscle contraction force in the field and in clinical settings has always been challenging. The aim of our work has been to develop a sensor that can overcome these difficulties and therefore enable measurement of muscle force during different contraction conditions. In this study, we tested the mechanical properties of a “Muscle Contraction” (MC) sensor during isometric muscle contraction in different length/tension conditions. The MC sensor is attached so that it indents the skin overlying a muscle group and detects varying degrees of tension during muscular contraction. We compared MC sensor readings over the biceps brachii (BB) muscle to dynamometric measurements of force of elbow flexion, together with recordings of surface EMG signal of BB during isometric contractions at 15° and 90° of elbow flexion. Statistical correlation between MC signal and force was very high at 15° (r = 0.976) and 90° (r = 0.966) across the complete time domain. Normalized SD or σ N = σ/max( F MC) was used as a measure of linearity of MC signal and elbow flexion force in dynamic conditions. The average was 8.24% for an elbow angle of 90° and 10.01% for an elbow of angle 15°, which indicates high linearity and good dynamic properties of MC sensor signal when compared to elbow flexion force. The next step of testing MC sensor potential will be to measure tension of muscle-tendon complex in conditions when length and tension change simultaneously during human motion.

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      Increased rate of force development and neural drive of human skeletal muscle following resistance training.

      The maximal rate of rise in muscle force [rate of force development (RFD)] has important functional consequences as it determines the force that can be generated in the early phase of muscle contraction (0-200 ms). The present study examined the effect of resistance training on contractile RFD and efferent motor outflow ("neural drive") during maximal muscle contraction. Contractile RFD (slope of force-time curve), impulse (time-integrated force), electromyography (EMG) signal amplitude (mean average voltage), and rate of EMG rise (slope of EMG-time curve) were determined (1-kHz sampling rate) during maximal isometric muscle contraction (quadriceps femoris) in 15 male subjects before and after 14 wk of heavy-resistance strength training (38 sessions). Maximal isometric muscle strength [maximal voluntary contraction (MVC)] increased from 291.1 +/- 9.8 to 339.0 +/- 10.2 N. m after training. Contractile RFD determined within time intervals of 30, 50, 100, and 200 ms relative to onset of contraction increased from 1,601 +/- 117 to 2,020 +/- 119 (P < 0.05), 1,802 +/- 121 to 2,201 +/- 106 (P < 0.01), 1,543 +/- 83 to 1,806 +/- 69 (P < 0.01), and 1,141 +/- 45 to 1,363 +/- 44 N. m. s(-1) (P < 0.01), respectively. Corresponding increases were observed in contractile impulse (P < 0.01-0.05). When normalized relative to MVC, contractile RFD increased 15% after training (at zero to one-sixth MVC; P < 0.05). Furthermore, muscle EMG increased (P < 0.01-0.05) 22-143% (mean average voltage) and 41-106% (rate of EMG rise) in the early contraction phase (0-200 ms). In conclusion, increases in explosive muscle strength (contractile RFD and impulse) were observed after heavy-resistance strength training. These findings could be explained by an enhanced neural drive, as evidenced by marked increases in EMG signal amplitude and rate of EMG rise in the early phase of muscle contraction.
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        Changes in single motor unit behaviour contribute to the increase in contraction speed after dynamic training in humans.

        1. The adaptations of the ankle dorsiflexor muscles and the behaviour of single motor units in the tibialis anterior in response to 12 weeks of dynamic training were studied in five human subjects. In each training session ten series of ten fast dorsiflexions were performed 5 days a week, against a load of 30-40% of the maximal muscle strength. 2. Training led to an enhancement of maximal voluntary muscle contraction (MVC) and the speed of voluntary ballistic contraction. This last enhancement was mainly related to neural adaptations since the time course of the muscle twitch induced by electrical stimulation remained unaffected. 3. The motor unit torque, recorded by the spike-triggered averaging method, increased without any change in its time to peak. The orderly motor unit recruitment (size principle) was preserved during slow ramp contraction after training but the units were activated earlier and had a greater maximal firing frequency during voluntary ballistic contractions. In addition, the high frequency firing rate observed at the onset of the contractions was maintained during the subsequent spikes after training. 4. Dynamic training induced brief (2-5 ms) motor unit interspike intervals, or 'doublets'. These doublets appeared to be different from the closely spaced (+/-10 ms) discharges usually observed at the onset of the ballistic contractions. Motor units with different recruitment thresholds showed doublet discharges and the percentage of the sample of units firing doublets was increased by training from 5.2 to 32.7%. The presence of these discharges was observed not only at the onset of the series of spikes but also later in the electromyographic (EMG) burst. 5. It is likely that earlier motor unit activation, extra doublets and enhanced maximal firing rate contribute to the increase in the speed of voluntary muscle contraction after dynamic training.
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          Filtering the surface EMG signal: Movement artifact and baseline noise contamination.

          The surface electromyographic (sEMG) signal that originates in the muscle is inevitably contaminated by various noise signals or artifacts that originate at the skin-electrode interface, in the electronics that amplifies the signals, and in external sources. Modern technology is substantially immune to some of these noises, but not to the baseline noise and the movement artifact noise. These noise sources have frequency spectra that contaminate the low-frequency part of the sEMG frequency spectrum. There are many factors which must be taken into consideration when determining the appropriate filter specifications to remove these artifacts; they include the muscle tested and type of contraction, the sensor configuration, and specific noise source. The band-pass determination is always a compromise between (a) reducing noise and artifact contamination, and (b) preserving the desired information from the sEMG signal. This study was designed to investigate the effects of mechanical perturbations and noise that are typically encountered during sEMG recordings in clinical and related applications. The analysis established the relationship between the attenuation rates of the movement artifact and the sEMG signal as a function of the filter band pass. When this relationship is combined with other considerations related to the informational content of the signal, the signal distortion of filters, and the kinds of artifacts evaluated in this study, a Butterworth filter with a corner frequency of 20 Hz and a slope of 12 dB/oct is recommended for general use. The results of this study are relevant to biomechanical and clinical applications where the measurements of body dynamics and kinematics may include artifact sources. Copyright 2010 Elsevier Ltd. All rights reserved.

            Author and article information

            [1 ] TMG-BMC Ltd., Splitska 5, Ljubljana 1000, Slovenia; E-Mail:
            [2 ] Institute for Kinesiology Research, Science and Research Centre of the University of Primorska, Garibaldijeva 1, Koper 6000, Slovenia; E-Mail: rado.pisot@
            [3 ] Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, Ljubljana 1000, Slovenia; E-Mail: saso.tomazic@
            [4 ] University of Nottingham, School of Graduate Entry Medicine and Health, Derby Royal Hospital, Uttoxeter Road, Derby DE22 3DT, UK; E-Mail: marco.narici@
            Author notes
            [* ] Author to whom correspondence should be addressed; E-Mail: srdjand@ ; Tel.: +38-641-672-601; Fax: +38-613-007-777.
            Sensors (Basel)
            Sensors (Basel)
            Sensors (Basel, Switzerland)
            September 2014
            25 September 2014
            : 14
            : 9
            : 17848-17863
            25256114 4208254 10.3390/s140917848 sensors-14-17848
            © 2014 by the authors; licensee MDPI, Basel, Switzerland.

            This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (


            Biomedical engineering

            measurement, muscle force, muscle tension, noninvasive, selective, in vivo


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