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Visual Computing for Industry, Biomedicine, and Art
Fig. 6 | Visual Computing for Industry, Biomedicine, and Art

Fig. 6

From: Automated analysis of pectoralis major thickness in pec-fly exercises: evolving from manual measurement to deep learning techniques

Fig. 6

PMaj thickness change for two different load training intensities at the same biofeedback condition (with or without RUSI biofeedback) (n = 25). The curves represent the average values of the PMaj thickness change, while the envelope areas represent the standard deviation of the PMaj thickness change. (a) represents PMaj thickness change without RUSI biofeedback at different load training intensities; (b) represents PMaj thickness change with RUSI biofeedback at different load training intensities. It is shown in (a) and (b) that differences in the PMaj thickness change distribution between them

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