An　exoskeleton　is　a　kind　of　intelligent　wearable　device　with　bioelectronics　and　biomechanics.　To　realize　its　effective　assistance　to　the　human　body,　an　exoskeleton　needs　to　recognize　the　real　time　movement　pattern　of　the　human　body　in　order　to　make　corresponding　movements　at　the　right　time.　However,　it　is　of　great　difficulty　for　an　exoskeleton　to　fully　identify　human　motion　patterns,　which　are　mainly　manifested　as　incomplete　acquisition　of　lower　limb　motion　information,　poor　feature　extraction　ability,　and　complicated　steps.　Aiming　at　the　above　consideration,　the　motion　mechanisms　of　human　lower　limbs　have　been　analyzed　in　this　paper,　and　a　set　of　wearable　bioelectronics　devices　are　introduced　based　on　an　electromyography　(EMG)　sensor　and　inertial　measurement　unit　(IMU),　which　help　to　obtain　biological　and　kinematic　information　of　the　lower　limb.　Then,　the　Dual　Stream　convolutional　neural　network　(CNN)-ReliefF　was　presented　to　extract　features　from　the　fusion　sensors’　data,　which　were　input　into　four　different　classifiers　to　obtain　the　recognition　accuracy　of　human　motion　patterns.　Compared　with　a　single　sensor　(EMG　or　IMU)　and　single　stream　CNN　or　manual　designed　feature　extraction　methods,　the　feature　extraction　based　on　Dual　Stream　CNN-ReliefF　shows　better　performance　in　terms　of　visualization　performance　and　recognition　accuracy.　This　method　was　used　to　extract　features　from　EMG　and　IMU　data　of　six　subjects　and　input　these　features　into　four　different　classifiers.　The　motion　pattern　recognition　accuracy　of　each　subject　under　the　four　classifiers　is　above　97%,　with　the　highest　average　recognition　accuracy　reaching　99.12%.　It　can　be　concluded　that　the　wearable　bioelectronics　device　and　Dual　Stream　CNN-ReliefF　feature　extraction　method　proposed　in　this　paper　enhanced　an　exoskeleton’s　ability　to　capture　human　movement　patterns,　thus　providing　optimal　assistance　to　the　human　body　at　the　appropriate　time.　Therefore,　it　can　provide　a　novel　approach　for　improving　the　human-machine　interaction　of　exoskeletons.　©　2022　by　the　authors.