EdgeVLM as a Privacy Filter: Toward Privacy-Aware Activity Recognition From Wearable Camera Using Image Captions

Egocentric video captured by wearable cameras offers rich contextual information for recognizing human activities in daily life. However, such video often includes sensitive personal details that must be protected from external threats. This creates a fundamental trade-off between preserving data utility and ensuring privacy, particularly in scenarios where continuous activity monitoring is required. In this study, we explore the concept of using EdgeVLM—a vision-language model designed to run entirely on edge devices—as a privacy filter for wearable camera data. We investigate the impact of caption granularity and demonstrate that our method locally transforms egocentric video into semantically rich textual image captions, enabling activity detection without transmitting raw visual content to the cloud. This edge-based processing preserves contextual cues while minimizing privacy risks through data minimization. To evaluate this approach, we conducted a quantitative user study (N=88) that found EdgeVLM-generated captions notably decreased participants’ privacy concerns compared to raw, blurred, or cartoonized images. Critically, from a bystander’s perspective, the proposed method demonstrated privacy protection levels statistically comparable to canny edge detection. Additionally, when combined with accelerometer data, the caption-based method achieved a 77.2% accuracy in recognizing desk activities such as typing, mousing, swiping, drinking, and writing—effectively replacing pixel-level visual information with text while maintaining performance comparable to models using unfiltered visuals. These findings indicate that EdgeVLM-based image captioning is a promising privacy-conscious solution for wearable camera applications, facilitating continuous activity recognition while protecting user privacy at the edge.