Dataset Information

Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG.

ABSTRACT: Muscle activity and fatigue performance parameters were obtained and compared between both a smart compression garment and the gold-standard, a surface electromyography (EMG) system during high-speed cycling in seven participants. The smart compression garment, based on force myography (FMG), comprised of integrated pressure sensors that were sandwiched between skin and garment, located on five thigh muscles. The muscle activity was assessed by means of crank cycle diagrams (polar plots) that displayed the muscle activity relative to the crank cycle. The fatigue was assessed by means of the median frequency of the power spectrum of the EMG signal; the fractal dimension (FD) of the EMG signal; and the FD of the pressure signal. The smart compression garment returned performance parameters (muscle activity and fatigue) comparable to the surface EMG. The major differences were that the EMG measured the electrical activity, whereas the pressure sensor measured the mechanical activity. As such, there was a phase shift between electrical and mechanical signals, with the electrical signals preceding the mechanical counterparts in most cases. This is specifically pronounced in high-speed cycling. The fatigue trend over the duration of the cycling exercise was clearly reflected in the fatigue parameters (FDs and median frequency) obtained from pressure and EMG signals. The fatigue parameter of the pressure signal (FD) showed a higher time dependency (R2 = 0.84) compared to the EMG signal. This reflects that the pressure signal puts more emphasis on the fatigue as a function of time rather than on the origin of fatigue (e.g., peripheral or central fatigue). In light of the high-speed activity results, caution should be exerted when using data obtained from EMG for biomechanical models. In contrast to EMG data, activity data obtained from FMG are considered more appropriate and accurate as an input for biomechanical modeling as they truly reflect the mechanical muscle activity. In summary, the smart compression garment based on FMG is a valid alternative to EMG-garments and provides more accurate results at high-speed activity (avoiding the electro-mechanical delay), as well as clearly measures the progress of muscle fatigue over time.

SUBMITTER: Belbasis A

PROVIDER: S-EPMC5917022 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG.

Belbasis Aaron A Fuss Franz Konstantin FK

Frontiers in physiology 20180419

Muscle activity and fatigue performance parameters were obtained and compared between both a smart compression garment and the gold-standard, a surface electromyography (EMG) system during high-speed cycling in seven participants. The smart compression garment, based on force myography (FMG), comprised of integrated pressure sensors that were sandwiched between skin and garment, located on five thigh muscles. The muscle activity was assessed by means of crank cycle diagrams (polar plots) that di ...[more]

PMID: 29725306

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Project description:BackgroundPartial hand amputation forms more than 90% of all upper limb amputations. This amputation has a notable effect on the amputee's life. To improve the quality of life for partial hand amputees different prosthesis options, including externally-powered prosthesis, have been investigated. The focus of this work is to explore force myography (FMG) as a technique for regressing grasping movement accompanied by wrist position variations. This study can lay the groundwork for a future investigation of FMG as a technique for controlling externally-powered prostheses continuously.MethodsTen able-bodied participants performed three hand movements while their wrist was fixed in one of six predefined positions. The angle between Thumb and Index finger ([Formula: see text]), and Thumb and Middle finger ([Formula: see text]) were calculated as measures of grasping movements. Two approaches were examined for estimating each angle: (i) one regression model, trained on data from all wrist positions and hand movements; (ii) a classifier that identified the wrist position followed by a separate regression model for each wrist position. The possibility of training the system using a limited number of wrist positions and testing it on all positions was also investigated.ResultsThe first approach had a correlation of determination ([Formula: see text]) of 0.871 for [Formula: see text] and [Formula: see text]. Using the second approach [Formula: see text] and [Formula: see text] were obtained. The first approach is over two times faster than the second approach while having similar performance; thus the first approach was selected to investigate the effect of the wrist position variations. Training with 6 or 5 wrist positions yielded results which were not statistically significant. A statistically significant decrease in performance resulted when less than five wrist positions were used for training.ConclusionsThe results indicate the potential of FMG to regress grasping movement, accompanied by wrist position variations, with a regression model for each angle. Also, it is necessary to include more than one wrist position in the training phase.

Project description:ObjectivesTo reduce pain and discomfort associated with breast compression in mammography, a pressure-controlled compression paddle was recently introduced. The objective was to evaluate the pressure-controlled paddle by comparing it to the standard force-controlled paddle.MethodsDifferences of compressed breast thickness (CBT), compression force, compression pressure, and average glandular dose (AGD) between annual follow-up full-field digital mammography exams of 3188 patients were retrospectively examined. Two groups were compared: (1) force-force group (FF-group), both examinations were performed with the force-controlled paddle, and (2) force-pressure group (FP-group), only the follow-up examination was performed with the pressure-controlled paddle. In an additional group of patients, pain scores on a scale of 0 (no pain at all) to 10 (worst pain imaginable) were evaluated prospectively (n?=?343) who were randomly assigned to either paddle.ResultsMedian differences between follow-up exams in CBT, compression force, compression pressure, and AGD were for the FF- and FP-group respectively -?1.0 vs 0.0 mm (p?<?0.001); 0.0 vs?-?1.0 daN (p?=?0.002); -?1.0 vs -?0.5 kPa (p?=?0.005); and 0.05 vs -?0.02 mGy (p?<?0.001). These differences were, although statistically significant, clinically non-relevant (defined as ?CBT >?±?2 mm; ?force >?±?2 daN; ?pressure >?±?1 kPa and ?AGD >?±?0.1 mGy). The subanalysis dividing CBT into five categories revealed similar results. The median [interquartile range] pain scores were 6 [3, 7] and 5 [3, 7] for the force-controlled and pressure-controlled paddle, respectively, which was not significantly different (p?=?0.113).ConclusionsWe observed no clinically relevant differences in CBT, compression force, compression pressure, AGD, or pain score between the force- and pressure-controlled paddle. As such, we found no basis for preferring one paddle over the other.Key points• The pressure-controlled paddle did not show any clinically relevant changes in breast compression parameters compared to the force-controlled paddle. • The pressure-controlled paddle did not lead to significant reduction in pain scores indicated by the patients compared to the force-controlled paddle. • A large variation in compression force and compression pressure was observed in mammography exams for the both the force- and pressure-controlled compression paddle.

Dataset Information

Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG.

Publications

Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG.

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