Activity Segmentation, dividing a continuous sensor stream into a set of activity segments, is a crucial pre-process in Human Activity Recognition (HAR) and it is required to be done in real-time for real-world smart services. Existing single-user activity segmentation schemes fail to correctly detect transition points due to concurrent and overlapping events from multiple users in case of Multi-user Collaborative Activity Recognition (MCAR). In this paper, we propose a novel scheme for activity segmentation for MCAR that expresses complex events and the correlations between them. For this, the proposed scheme first creates an event stream from a sensor stream and defines event sets in terms of time windows. For each time window, two types of correlations for every event pair are calculated: duration correlation and history correlation. After calculating event correlation, the change score of a time window is measured by comparing the calculated correlation values with those of the preceding windows. Then, the proposed scheme elects as an activity transition point a time window whose change score exceeds the transition threshold. We evaluate the proposed method on two multi-user collaborative activity datasets and experiment results show that the proposed scheme achieves better segmentation performance than existing approaches.

A smart temperature sensor in 65-nm CMOS, utilizing CMOS ring oscillators, consumes 1.09 nJ at a conversion rate of 366 kS/s. This is achieved by the direct temperature-to-digital conversion method implemented in the frequency-to-digital converter. The algorithm utilized in the fine code generator makes it possible to increase the resolution of the sensor efficiently. Compared to previous work, this brief shows lower VDD operation. After one point calibration, the chip-to-chip spread is +2.7 ~ -2.9°C over the temperature range of -40°C to 110°C.