In the last few decades, the number of elderly people living alone surge worldwide due to an increase in human life expectancy. Researchers suggest different types of methods to monitor elderly residents living alone to prevent them from being unable to get help on time after some incidents such as accidental falling or heart attack. High-resolution data generating methods in monitoring elderly people with RGB cameras or wearable devices are inconvenient for elderly residents. While the former raises a privacy concern, the latter is not practical to wear on and off or charge its battery frequently. One of the solutions that solve both aforementioned problems is ultra-low resolution infrared (IR) sensor arrays. We offer a dataset for Activities of Daily Living (ADL) collected with 8×8 IR sensor arrays from 74 volunteers. For ADL recognition, four types of deep learning models, Convolutional Neural Network (CNN), two types of Recurrent Neural Networks (RNN), and Transformer models are employed. Among them, CNN and Transformer models showed promising results. We believe the dataset is a good contribution to versatile data sources for researchers to accelerate their work on the development of privacy-preserved ADL recognition systems.