The objective of this study was to assess the effectiveness and practicability of an activity index combining acceleration and location data for automated estrus detection in dairy cows. By using a wearable neck tag, measurements of acceleration and location were gathered from 22 multiparous cows monitored incessantly for 6 days to derive activity records of each cow. The maximum-minimum distance clustering (MMDC) method was used to divide hourly activity data into low, medium, high, and intensity level groups. The weighted sum of the proportions of the low, medium, high, and intensity activities in an hour constituted the activity level. The activity index was defined as the ratio of the variation in hourly activity level compared to the same time period during the previous three days. Furthermore, whether the cow was in estrus was judged above a set threshold. The study showed that the power consumption and communication effects of the neck tags were acceptable for indoor-housing conditions. For the two consecutive time periods, the activity-index-based detection algorithm achieved 90.91% for accuracy, 100% for precision, 100% for specificity, 83.33% for recall, 90.91% for F1 score, and 0.82 for Kappa coefficient. On the basis of these results, it can be concluded that the combination of acceleration and location in the activity index can promote estrus detection in dairy cows.