Proper　handwashing,　having　a　crucial　effect　on　reducing　bacteria,　serves　as　the　cornerstone　of　hand　hygiene.　For　elders　with　dementia,　they　suffer　from　a　gradual　loss　of　memory　and　difficulty　coordinating　handwashing　steps.　Proper　assistance　should　be　provided　to　them　to　ensure　their　hand　hygiene　adherence.　Toward　this　end,　we　propose　AWash,　leveraging　inertial　measurement　unit　(IMU)　readily　available　in　most　wrist-worn　devices　(e.g.,　smartwatches)　to　characterize　handwashing　actions　and　provide　assistance.　To　monitor　handwashing　scenarios　round-the-clock　while　achieving　energy　efficiency,　we　design　methods　that　distinguish　handwashing　from　other　daily　activities　and　dynamically　adjust　the　sampling　duty　cycle.　Upon　detecting　handwashing　actions,　we　design　several　novel　techniques　to　segment　different　handwashing　actions　and　extract　sensor-body　inclination　angles　that　handle　particular　interference　of　senile　dementia　patients.　Moreover,　a　user-independent　network　model　is　built　to　recognize　the　handwashing　actions　of　senile　dementia　patients　without　requiring　their　training　data.　Furthermore,　we　propose　a　transfer　learning　method　that　improves　system　performance.　To　meet　users’　diverse　needs,　we　use　a　state　machine　to　make　prompt　decisions,　supporting　customized　assistance.　Extensive　experiments　on　a　prototype　with　eight　older　participants　demonstrate　that　AWash　can　increase　the　user’s　independence　in　the　execution　of　handwashing.　IEEE