Remote monitoring in lifecare presents a vision of human behavior interactions in complex situations that were previously overlooked. In this research, we designed lifecare algorithms that recognize the activities from processed video sequence images. This groundbreaking technology enables remote monitoring systems to collect reliable data that can be applied across fields such as healthcare, sports, and security. We integrate an embedded hidden Markov model (E-HMM) with visual-sensor-based data input to enhance the efficiency of human behavior interaction systems. The proposed method begins by performing a hue saturation value color transformation to improve the clarity of video frames. The silhouette is extracted using hybrid techniques with sensor data, and signal features are extracted using texton maps, a local intensity order pattern, and oriented features from accelerated segment test and rotated binary robust independent elementary features. Fuzzy optimization is then carried out to choose the most discriminative signal features. An E-HMM is trained to identify actions correctly according to the given functions. Furthermore, since the suggested approach monitors the order of actions, it uses time-related data, which results in improved detection results, even in the presence of occlusions or when actions are performed at various speeds and scales. The sensor data used in the experiment, combined with the recognition algorithms, achieved the following results: Shakefive2: 0.97%, HMDB51: 0.90%, and Okutama Action: 0.68, 0.94, and 0.82%.