This paper describes a activity recognition method for Sussex-Huawei Locomotion (SHL) Challenge 2020 by team TDU_BSA. The use of ensemble learning, which combines the outputs of multiple classifiers to produce a single estimation result, improved the accuracy of activity recognition. The ensemble model consists of CNN models and a gradient-boosting model. The objective of SHL Challenge 2020 is that the users of SHL test-set are two different from SHL training-set, and the phone location of SHL test-set is not known to the SHL's participants. Therefore, estimating phone location and the user improved accuracy. SHL test-set's phone location was estimated to be Hips. The user can be estimated from SHL validation-set. The ensemble model was made with all SHL training-set (Only Hips) and 70% of SHL validation-set (Only Hips). In the submission phase, the best F-measure obtained for last 30% SHL validation-set was 84.8%. CCS CONCEPTS • Computing methodologies → Activity recognition and understanding.
This paper describes an activity recognition method for Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge by team TDU_BSA_BCI. The classification accuracy has been improved by switching the estimation model, depending on whether the location is available. Data, including location were classified by Deep Neural Network including LSTM layer. Data that exclude location were classified by the Gradient Boosting Decision Tree. The 2 outputs have been combined. They were optimized by applying a median filter. In the submission phase, the best F-measure obtained for the SHL validation-set was 65%. CCS CONCEPTS• Computing methodologies → Activity recognition and understanding.
In this paper, we propose an education support system that offers two functions: attendance management and advance notification. Attendance management is paramount in the field of education as the attendance of a large number of students needs to be accurately checked in a short time. The proposed system uses Wi-Fi indoor positioning to quickly and accurately determine a student’s indoor location. This system can manage attendance as well as late entries and early departures. Advance notification function prompts students to move from their current location to their destination. The proposed system uses a user’s physical activity and indoor position. This system measures the travel time between classrooms using the user’s smartphone. Based on the collected travel time and the current position of the student, a notification of the start of travel is made at an optimal advance notification time. This prevents students from being late for lectures.
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