Artificial-Intelligence-Assisted Activities of Daily Living Recognition for Elderly in Smart Home

Activity Recognition (AR) is a method to identify a certain activity from the set of actions. It is commonly used to recognize a set of Activities of Daily Living (ADLs), which are performed by the elderly in a smart home environment. AR can be beneficial for monitoring the elder's health condition, where the information can be further shared with the family members, caretakers, or doctors. Due to the unpredictable behaviors of an elderly person, performance of ADLs can vary in day-to-day life. Each activity may perform differently, which can affect the sequence of the sensor's raw data. Due to this issue, recognizing ADLs from the sensor's raw data remains a challenge. In this paper, we proposed an Activity Recognition for the prediction of the Activities of Daily Living using Artificial Intelligence approach. Data acquisition techniques and modified Naive Bayes supervised learning algorithm are used to design the prediction model for ADL. Our experiment results establish that the proposed method can achieve high accuracy in comparison to other well-established supervised learning algorithms.

Automated summary

Activity Recognition is a method to identify specific activities from a set of actions, commonly used in smart home environments to monitor the health of elderly individuals. Recognizing daily activities remains challenging due to the unpredictable behaviors of elderly individuals. This study proposes an Artificial Intelligence-based prediction model for Activities of Daily Living, which achieves high accuracy compared to other supervised learning algorithms.