<![CDATA[Providing enough health caregivers due to an aging population has recently been challenging. To alleviate this problem, there's a growing demand for certain household monitoring tasks to be automated especially for elderly persons living independently to reduce the number of scheduled visits by caregivers. Moreover, gathering crucial data using AI technology about functional, cognitive, and social health status, is essential for monitoring daily physical activities at home. This paper proposes a system that determines a room's cleanliness (degree of clutter) to decide whether a caregiver visit is required. A Yolov5-based method is applied to recognize objects in the room including clothes, utensils, clothes, etc. However, due to background noise interference in the rooms and the insufficient feature extraction in YOLOv5, an improvement regime is proposed to improve the detection accuracy. The ECA (Efficient Channel Attention) is added to the network's backbone to focus on feature information, reducing the missed detection rate. The initial anchor box clustering algorithm is improved by replacing K-means with the K-means++ algorithm, enabling more effective adaptation to changing room views. The regression loss function EIoU (Enhanced Intersection over Union) is introduced to optimize the convergence speed and improve the accuracy. The room clutter is determined using set rules by comparing the detection results and prior information from the clean room using IOU. In 31 rooms, 9 subjects' evaluation was used to prove the effectiveness of the proposed system. Compared to the original Yolov5 algorithm, the method proposed in this paper achieved better performance]]>
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