Speakers
Description
Home-based resistance training has grown rapidly as a setting for unsupervised exercise, motivating wearable systems that provide automated feedback such as form correction, repetition counting, and load adjustment. Delivering that feedback first requires detecting which lower-limb movement the user is performing, and surface electromyography (sEMG) is well suited to this because each exercise produces a distinguishable muscle co-activation pattern. However, conventional sEMG depends on precise, expert electrode placement and on wet Ag/AgCl gel that dehydrates and irritates the skin; even in controlled laboratory settings its validity is degraded by placement sensitivity, electrode shift during dynamic contraction, crosstalk, and impedance drift, and these problems are magnified in unsupervised home use. Dry electrodes are therefore the realistic option, and graphene has become the most intensively studied dry-electrode material because its combination of high conductivity, mechanical flexibility, and durability under sweat and repeated bending directly targets the failure modes of gel and conventional dry electrodes. This makes a focused appraisal of graphene EMG timely: the graphene-electrode literature has grown quickly but remains scattered across materials and body sites and has never been assessed for the dynamic lower-limb exercise application, so it is not yet known whether graphene can meet the demands of the home gym. Accordingly, this study reviews conventional wet and dry sEMG for lower-limb movement classification and its limitations and asks whether such classification has been demonstrated in gym or home settings; introduces graphene-based EMG and compares graphene material variants quantitatively for durable, reusable dry electrodes; and examines whether graphene-based EMG sensors have been validated in real movement, analysing their measured performance against conventional electrodes. The central finding is that, although graphene dry electrodes match or exceed conventional electrodes under static conditions, no graphene-based EMG sensor has been benchmarked against conventional dry electrodes during dynamic, multi-joint lower-limb resistance exercise.
