###### Abstract  
Recommendation systems are crucial in the provision of services to the elderly with Alzheimer's disease in IoT-based smart home environments. In this work, a Reminder Care System (RCS) is presented to help Alzheimer patients live in and operate their homes safely and independently. A contextual bandit approach is utilized in the formulation of the proposed recommendation system to tackle dynamicity in human activities and to construct accurate recommendations that meet user needs without their feedback. The system was evaluated based on three public datasets using a cumulative reward as a metric. Our experimental results demonstrate the feasibility and effectiveness of the proposed Reminder Care System for real-world IoT-based smart home applications. contextual bandit; IoT; recommender system +
Footnote †: journal:  
from the mild stage of AD where patients begin losing short-term memory; however, they still have the ability to use such a system [1; 5]. A reminder care system is designed to exploit sensory data from various sources, such as the environmental sensors, wearable sensors, and appliance sensors for effective reminder recommendations. A feedback system is not necessarily required to improve the quality of recommendations. The scenario presented in Figure 1 reveals the importance of a reminder care system for Alzheimer patients. In the scenario, the 77-year-old woman, [PERSON], lives independently and she is diagnosed with the mild AD. The deployed reminder care system can monitor and recognize [PERSON] activity patterns and then use this pattern to automatically provide recommendations on what [PERSON] may need based on her current activity. For example, the system is designed to promptly recommend switching off of appliances if she forgets to do so after usage. The system is subsequently improved based on her acceptance of every recommendation without needing [PERSON] explicit feedback. Here, the system considers all contexts about the user and items. For example, if [PERSON] starts to prepare a cup of coffee at midnight, no item will be recommended by the system but instead it will remind her to go back to sleep because time is considered as a key context in conducting the recommendations. Moreover, the system has the ability to learn all new patterns and ignore old patterns. For illustration, when [PERSON] no longer adds milk to her coffee, the system will not recommend milk during this activity. This scenario could be extended to become not only a smart home application but also for m-health applications. Again, [PERSON] at home can be better monitored by the hospital by utilizing the data from sensors that are installed in her house to remotely monitor the development of her condition. The hospital can also provide medical advice recommendations, such as recommending a specific time for resting, recommending an exercise to be done, or recommending certain kinds of foods. Our proposed system is suitable only for the first stage of Alzheimer as, in other stages, patients face more changes in their behaviour where they require intensive care. A number of studies have focused on reminder recommender systems aimed at providing assistance to elderly people who suffer from AD. [PERSON] et al. [6] designed a recommendations system for monitoring the daily indoor activities of seniors with mild cognitive impairment while [PERSON] et al. [7] proposed a smart biomedical assisted system to help patients with Alzheimer's. In some studies, smartphone applications were developed for the provision of care services to AD patients [8; 9; 10; 11]. However, the dynamicity in the complexities of human activities is yet to be adequately addressed, thus, delivering low-quality recommendations. Another notable issue is the increased focus on monitoring, which gives a reminder to patients while the system has to wait for patient feedback to update itself.