Long-distance relationships (LDRs) struggle to sustain intimacy without physical touch. Existing mediated social touch systems rely on designer-authored haptic patterns, which limit opportunities for personalization and shared meaning-making. We present Onni, a haptic interface that lets couples collaboratively define and experience a shared library of haptic interactions. In Study 1, we conducted co-creation workshops (n = 20) to examine how couples negotiate and align meanings in haptic interactions. In Study 2, we deployed Onni in everyday routines (n = 6) to explore how these interactions are adopted, adapted, and ritualized. Our findings illustrate that couples co-create and personalize haptic interactions through continuous exploration, negotiation, and situational adaptation. By integrating a dyadic co-design approach, an end-user authoring interface for a shared action–feedback haptic repertoire, and a longitudinal view of how meanings evolve in everyday LDR routines, this work advances the understanding of haptic meaning-making as a collaboratively constructed and ritualized process. It offers concrete design implications for building personalized, evolving haptic systems that support intimacy in LDRs.

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The positioning of multiple magnetic sources with different depths, distributions and magnetic moments is extremely difficult. The improved tilt angle for target area recognition can be well adapted to the self-adaptive fuzzy c-means (SAFCM) clustering algorithm for grid measurement of magnetic gradient tensors (MGT), which provides a feasible solution to solve the complex problem of multi-target inversion. First, we improve the tilt angle with the normalized source strengths and the tensor contraction, which can accurately identify the two-dimensional area boundary of objects with different buried depths regardless of the magnetization direction. The grid measurements of magnetic gradient tensor systems (MGTS) enable this process. Then, with the tensor spacial invariant relations, we calculate the source initial position coordinates corresponding to each node in the recognition area, thereby forming a dense point cloud in the space near the target real position. Finally, we introduce a penalty function into the clustering validity function to eliminate the monotonous decreasing trend of the cluster number optimization process, thus the SAFCM algorithm can accurately and automatically detect the number and position of centroids. The simulation shows that with the Gaussian noise of 0 mean and 5 nT/m variance, the targets number recognition accuracy of 20 magnetic dipoles is 97.6%, and the horizontal position and buried depth estimation accuracy is greater than 91.7% and 85.6% respectively. In the real survey areas of 2.1m×2.1m and 1.2m×1.2m, the coordinates’ estimation deviation of nine small magnets is less than 0.091m.