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We propose a deep compressed spiking neural network (DCSNN) to address the challenges. The DCSNN can significantly reduce the inference power consumption and memory usage while improving recognition accuracy. In addition, we designed a linear method of action detection called leaky integrate-and-fire for transient-state action detection (TAD-LIF), which can improve the robustness of recognition systems effectively. To evaluate our method, we developed two lightweight sEMG wristbands respectively for two interaction modes, and collected two datasets from about 40 subjects. The experiment results show that DCSNN had a higher recognition accuracy than existing methods with values of 88.55% and 95.76% on the two datasets. In addition, its inference latency, power consumption, and memory usage are only about 0.4%, 0.05%, and 2% of those of popular convolutional neural network (CNN) methods. Our method enables precise, high-speed, and low-power micro-gesture recognition on a plethora of resource-constrained consumer-level intelligent wearable devices.<\/jats:p>","DOI":"10.1145\/3729494","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T21:21:56Z","timestamp":1750281716000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["DCSNN: An Efficient and High-speed sEMG-based Transient-state Micro-gesture Recognition Method on Wearable Devices"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9450-8498","authenticated-orcid":false,"given":"Youfang","family":"Han","sequence":"first","affiliation":[{"name":"Goertek Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5368-9900","authenticated-orcid":false,"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Goertek Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8715-1254","authenticated-orcid":false,"given":"Ge","family":"Gao","sequence":"additional","affiliation":[{"name":"Goertek Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8003-1975","authenticated-orcid":false,"given":"Xiangjin","family":"Chen","sequence":"additional","affiliation":[{"name":"Goertek Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0176-9532","authenticated-orcid":false,"given":"Jiliang","family":"Yin","sequence":"additional","affiliation":[{"name":"Goertek Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6859-143X","authenticated-orcid":false,"given":"Lin","family":"Wang","sequence":"additional","affiliation":[{"name":"Goertek Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4902-3344","authenticated-orcid":false,"given":"Xin","family":"Meng","sequence":"additional","affiliation":[{"name":"Goertek Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9248-6928","authenticated-orcid":false,"given":"Yang","family":"Yu","sequence":"additional","affiliation":[{"name":"Goertek Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0949-2801","authenticated-orcid":false,"given":"Tengxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Goertek Inc., Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_2_1_1","DOI":"10.1109\/IROS.2016.7759384"},{"key":"e_1_2_2_2_1","first-page":"4730","article-title":"Designing wearable joystick and performance comparison of EMG classification methods for thumb finger gestures of joystick control","volume":"28","author":"Altin Cemil","year":"2017","unstructured":"Cemil Altin and O Er. 2017. 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