The　widespread　usage　of　wearables　to　provide　healthcare　services　prompts　the　need　for　secure　group　communication　among　multiple　devices　using　group　keys.　Gait-based　group　key　establishment　schemes　are　either　vulnerable　to　video　attacks,　or　fail　to　offer　a　secure　group　key　update　mechanism　when　group　device　changes.　In　this　paper,　we　present　an　electrocardiogram　(ECG)　signals　based　group　device　pairing　protocol,　which　can　strengthen　the　security　and　reduce　the　overhead　of　wearables.　Specifically,　we　first　design　a　robust　and　lightweight　fuzzy　extractor　that　supports　secure　and　efficient　group　device　association　between　wearables.　Meanwhile,　we　propose　Improved　Martingale　Randomness　Extraction　(IMRE)　algorithm,　which　utilizes　the　trend　of　InterPulse　Interval　(IPI)　from　ECG　signal　to　extract　high-entropy　keys.　Then　we　present　a　membership　management　mechanism　that　enables　group　key　dynamic　update　when　group　device　changes.　Finally,　we　simulate　our　protocol　and　evaluate　the　accuracy　and　efficiency　by　various　experiments.　The　experimental　results　demonstrate　that　the　proposed　work　is　robust　and　efficient,　and　the　threat　model-based　security　analysis　shows　that　the　proposed　protocol　can　prevent　both　active　and　passive　attacks.