Objective: The behavior monitoring of older adults in their own home and enabling daily-life activity analysis to healthcare practitioner is a key challenge. Methods and procedures: Our framework replicates the elderly home in digital space which can provide an unobtrusive way to monitor the residentâs daily life activities. The learning challenges posed by different performed activities at home are solved by introducing the deep meta-class sequence model. The notion is to group the set of activities into a single meta-class according to the nature of the activities. It helps the learning process, which is based on long short-term memory (LSTM) to learn feature space abstraction. Each meta-class abstraction is further decomposed to an individual activity performed by the elderly at home. Results: The experiments are carried out over the Center for Advanced Studies in Adaptive Systems dataset and proposed model outperforms as compared to baseline models. Clinical impact: Our findings demonstrate a robust framework to digitally monitor the elderly behavior, which is beneficial for healthcare practitioners to understand the level of support the elderly needed to perform the daily tasks or potential risk of an emergency in their own homes.
|Number of pages||11|
|Journal||IEEE Journal of Translational Engineering in Health and Medicine|
|Publication status||Published - 08 May 2023|
- Digital transformation
- Human behavior
- Embedded sensors
- Healthcare services