Human　Activity　Recognition　(HAR)　is　the　process　of　identifying　human　activity　states　based　on　collected　data,　with　applications　in　health　monitoring,　rehabilitation　therapy,　and　smart　homes.　However,　accurate　recognition　often　necessitates　the　use　of　multiple　sensors,　which　can　cause　discomfort　and　significantly　reduce　practicality　and　convenience.　Currently,　smartphones　with　embedded　sensors　have　broadened　the　potential　for　HAR,　enabling　healthcare　monitoring,　life　logging,　fitness　tracking,　and　more.　This　paper　conducts　experiments　on　the　WISDM　public　dataset　and　a　self-collected　dataset,　demonstrating　that　a　one-dimensional　convolutional　neural　network　structure　does　not　necessarily　underperform　a　two-dimensional　one,　using　only　accelerometer　data.　Moreover,　the　experimental　results　on　the　self-collected　dataset　suggest　that　utilizing　the　barometer　built　into　smartphones　can　enhance　activity　recognition　rates.　Specifically,　we　first　built　the　corresponding　convolutional　neural　network　model　and　conducted　experiments　on　the　WISDM　dataset,　and　the　accuracy　reached　95.42%.　Next,　we　collected　data　from　the　smartphone＇s　embedded　barometer　and　accelerometer　to　construct　a　dataset.　Experimental　results　on　our　self-collected　dataset　show　that　the　inclusion　of　barometer　data　can　improve　the　accuracy　of　activity　recognition,　especially　stair-related　activities.　The　F1-score　for　identifying　walking,　ascending　stairs,　and　descending　stairs　increased　by　6.7%,　7.39%,　and　2.99%　respectively.　©　2023　ACM.