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Prediction of Toothbrushing Position Based on Gyro Sensor Data and its Validation Using Unsupervised Learning-based Clustering
DoYoon Kim, MinWook Kwon, SeungJu Baek, HyeRin Yoon, DaeYeon Lim, Eunah Jo, Seungjae Ryu, Young Wook Kim, Jin Hyun Kim
http://doi.org/10.5626/JOK.2023.50.12.1143
Oral health is an important health indicator that is directly related to longevity. For this reason, oral health has become a key component of public health, from infants to the elderly. The foundation of good oral health is good brushing habits. However, the recommended correct brushing method is not easy to adopt, and this harms oral health. This paper proposes a method to distinguish brushing zones using low-cost IMU sensors to track the correct brushing method. We evaluated the accuracy of the brushing zone estimation method using clustering algorithms in machine learning. In this paper, we propose a method for determining the brushing area based on toothbrush posture alone using the gyro sensor of an IMU sensor. In this paper, we propose a method for determining the brushing area using only the gyro sensor of an IMU sensor based on toothbrush posture. We showed that relatively inexpensive 6-axis IMU gyro sensor data could be used to estimate the user’s brushing area with an accuracy of 80.6%. In addition, we applied a clustering algorithm to these data and trained a logistic regression model using the clustered data to estimate the brushing area. The result was obtained with an accuracy of 86.7%, showing that clustering was effective and that the toothbrush posture-based brushing area estimation proposed in this paper was effective. In conclusion, it is expected that the brushing zone estimation algorithm can be implemented as a function of a relatively low-cost toothbrush and that it can help to maintain oral health by analyzing and improving personal brushing habits.
Analysis of Limits in Applying AP-QoS-based Wi-Fi Slicing for Real-Time Systems
Jin Hyun Kim, Hyonyoung Choi, Gangjin Kim, Yundo Choi, Tae-Won Ban, Se-Hoon Kim
http://doi.org/10.5626/JOK.2021.48.6.723
Network slicing is a new network technology that guarantees the quality of network services according to application services or user’s types. Wi-Fi, IEEE 802.11-based LAN, is the mostly popularly used short-range wireless network and has been continually attracting more and more from users. Recently, the use of Wi-Fi by safety critical IoT devices, such as medical devices, has been drastically increasing. Moreover, enterprises require network slicing of Wi-Fi to introduce the provision of prioritized QoS of Wi-Fi depending on the service type of customer. This paper presents the analysis of the limits and difficulties in applying AP-QoS-based network slicing for hard real-time systems that demand temporal deterministic streaming services. In this paper, we have defined a formal framework to analyze QoS-providing IEEE 802.11e Enhanced Distributed Coordination Access and provide the worst-case streaming scenarios and thereby demonstrated why the temporal determinism of network streaming is broken. In addition, simulation results of AP-QoS-based network slicing using NS-3 are presented to show the limits and difficulties of the network slicing. Moreover, we present Wi-Fi network slicing techniques based on EDCA of AP-QoS for real-time systems through our technical report referenced in this paper.
ILP-based Schedule Synthesis of Time-Sensitive Networking
Jin Hyun Kim, Hyonyoung Choi, Kyong Hoon Kim, Insup Lee, Se-Hoon Kim
http://doi.org/10.5626/JOK.2021.48.6.595
IEEE 802.1Qbv Time Sensitive Network (TSN), the latest real-time Ethernet standard, is a network designed to guarantee the temporal accuracy of streams. TSN is an Ethernet-based network system that is actively being developed for the factory automation and automobile network systems. TSN controls the flow of data streams based on schedules generated statically off-line to satisfy end-to-end delay or jitter requirements. However, the generation of TSN schedules is an NP-hard problem; because of this, constraint solving techniques, such as SMT (Satisfiability Modulo Theory) and ILP (Integer Linear Programming), have mainly been proposed as solutions to this problem. This paper presents a new approach using a heuristic greedy and incremental algorithm working with ILP to decrease the complexity of computing schedules and improve the schedule generation performance in computing TSN schedules. Finally, we compare our proposed method with the existing SMT solver approach to show the performance of our approach.
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