Activities of Daily Living (ADLs) are a set of fundamental, routine self-care tasks that individuals perform daily to maintain their well-being and independence. These typically include basic personal care like bathing, dressing, eating, toileting, and transferring (moving from bed to chair). In the realm of assistive technology and AI, ADLs are often monitored and predicted to provide timely and appropriate support. This involves using multimodal data (e.g., sensors, vision) to infer a user's current activity and predict their next intended action. The provided research highlights the challenge of making these predictions reliable and safe, especially for assistive devices. Accurate and reliable prediction of ADLs is crucial for developing intelligent assistive devices, smart homes, and robotic aids that can enhance the autonomy and safety of individuals, particularly the elderly or those with disabilities. It enables proactive support, fall prevention, and personalized care. Researchers in human-computer interaction, robotics, pervasive computing, and healthcare technology, as well as companies developing smart home systems, wearable health monitors, and assistive robots, widely use this concept.
Activities of Daily Living (ADLs) are everyday self-care tasks. In AI, predicting these actions helps assistive devices support users safely. New methods focus on making these predictions highly reliable, so devices only act when they are very sure, preventing errors and ensuring user safety.
ADLs, Basic ADLs, Functional Activities
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