Leveraging Wearables for Assisting the Elderly With Dementia in Handwashing

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.