Due　to　the　lack　of　unified　human　activity　model　and　related　specifications,　the　existing　wearable　human　activity　recognition　technology　uses　different　types,　numbers　and　deployment　locations　of　sensors,　and　affects　its　promotion　and　application.　Based　on　the　analysis　of　human　activity　skeleton　characteristics　and　human　activity　mechanics,　a　human　activity　model　based　on　Cartesian　coordinates　is　established　and　the　normalization　method　of　activity　sensor　deployment　location　and　activity　data　in　the　model　is　standardized;　Secondly,　a　sliding　window　technique　is　introduced　to　establish　a　mapping　method　to　convert　human　activity　data　into　ROB　bitmap,　and　a　Convolutional　Neural　Network　is　designed　for　Human　Activity　Recognition　(HAR-CNN);　Finally,　a　HAR-CNN　instance　is　created　and　experimentally　tested　based　on　the　public　human　activity　dataset　Opportunity.　The　experimental　results　show　that　HAR-CNN　achieves　the　F1　values　of　90%　and　92%　for　periodic　repetitive　activity　and　discrete　human　activity　recognition,　respectively,　while　the　algorithm　has　good　operational　efficiency.