Objective:　Deep　learning-based　CNN　networks　have　recently　been　investigated　to　solve　the　problem　of　body　posture　recognition　based　on　surface　electromyographic　signals　(sEMG).　Influenced　by　these　studies,　to　develop　a　combined　approach　of　sEMG　and　CNNs　in　the　study　of　human-product　interactions　and　the　impact　of　body　comfort,　and　to　compare　the　advantages　and　disadvantages　of　various　CNNs　networks.Methods:　In　this　study,　sEMG　measurements　were　carried　out　by　building　a　prototype　usability　experiment,　and　the　data　were　divided　into　four　categories,　with　two　types　of　datasets:　training　and　testing.　Four　CNNs,　LeNet-5,　VGGNet-11,　InceptionNet　V4,　and　DenseNet,　were　used　for　the　recognition　of　sEMG　images.Results:　DenseNet　is　another　type　of　convolutional　neural　network　with　deep　layers,　which　has　a　unique　advantage　over　other　algorithms.　unique　advantages　over　other　algorithms.　DenseNet　has　fewer　layers　and　better　accuracy　than　InceptionNet　V4,　but　not　only　does　it　bypass　enhanced　feature　reuse,　but　its　network　is　easier　to　train　and　has　some　regularization　effects,　while　also　mitigating　the　problems　of　gradient　disappearance　and　model　degradation.Conclusion:　These　findings　could　lead　to　a　more　appropriate　CNN　model　and　a　useful　tool　for　developing　comfort　judgments　of　surface　EMG　signals,　furthering　the　development　of　products　that　come　into　contact　with　the　human　body　without　the　need　for　routine　retraining.