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The fuzzy membership function was defined based on the Euclidean distance between the embedding vector and dispersion pattern. Simulated signals generated by the one-dimensional (1D) logistic map were used to test the sensitivity of the proposed method to dynamic changes. Moreover, 29 subjects were recruited for an upper limb muscle fatigue experiment, during which surface electromyography (sEMG) signals of the biceps brachii muscle were recorded. Both simulated signals and sEMG signals were processed using a sliding window approach. Sample entropy (SampEn), DispEn and FFDispEn were separately used to calculate the complexity of each frame. The sensitivity of different algorithms to the muscle fatigue process was analyzed using fitting parameters through linear fitting of the complexity of each frame signal. The results showed that for simulated signals, the larger the fractional order &lt;italic&gt;q&lt;\/italic&gt;, the higher the sensitivity to dynamic changes. Moreover, DispEn performed poorly in the sensitivity to dynamic changes compared with FFDispEn. As for muscle fatigue detection, the FFDispEn value showed a clear declining tendency with a mean slope of \u22121.658 \u00d7 10&lt;sup&gt;\u22123&lt;\/sup&gt; as muscle fatigue progresses; additionally, it was more sensitive to muscle fatigue compared with SampEn (slope: \u22120.4156 \u00d7 10&lt;sup&gt;\u22123&lt;\/sup&gt;) and DispEn (slope: \u22120.1675 \u00d7 10&lt;sup&gt;\u22123&lt;\/sup&gt;). The highest accuracy of 97.5% was achieved with the FFDispEn and support vector machine (SVM). This study provided a new useful nonlinear dynamic indicator for sEMG signal processing and muscle fatigue analysis. The proposed method may be useful for physiological and biomedical signal analysis.&lt;\/p&gt;\n\t\t&lt;\/abstract&gt;<\/jats:p>","DOI":"10.3934\/mbe.2024007","type":"journal-article","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T10:09:08Z","timestamp":1702289348000},"page":"144-169","source":"Crossref","is-referenced-by-count":8,"title":["A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection"],"prefix":"10.3934","volume":"21","author":[{"given":"Baohua","family":"Hu","sequence":"first","affiliation":[{"name":"School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingsong","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China"},{"name":"Department of Rehabilitation Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui Provincial Hospital, Hefei 230036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2321","reference":[{"key":"key-10.3934\/mbe.2024007-1","doi-asserted-by":"publisher","unstructured":"V. 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