Browsing by Author "Lin, Juyi"

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Design and Implement a Mobile Badminton Stroke Classification System
(2017-01-01)
Lin, Juyi
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Chang, Chia-Wei
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Wang, Chih-Hao
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Chi, Hong-Chuan
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Yi, Chih-Wei
The use of the badminton stroke strategy in the evenly matched game is often the key to victory. In this work, a smart racket based on wearable seusors is proposed to collect the data of swing of badminton. A cell phone APP with machine learning techniques is implemented to record stroke types automatically. In each stroke hit event, this prototype system uses Bluetooth earphone to collect the sound for detecting the accuracy time. It uses the data of IMU in each stroke for determining stroke type. Compared to IMU only solution, the system will reduce the false count of stroke hit. Using cloud techniques could record the training and game record in a long period. Overall the accuracy of stroke hit event is almost 100% by using voice print. The data of IMU is classified by Random Forest or SMO. The accuracy for personal model is 95.91 %, and it is 79.32% for general model. We develop a stroke record system which is combined with Wearable sensor, Mobile platform and Cloud service.
Using V2X communications and data fusion to achieve lane-level positioning for road vehicles
(2020-01-01)
Lee, Tsu-Kuang
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Lin, Juyi
;
Chen, Jen-Jee
;
Tseng, Yu-Chee
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資訊工程學系
Precise positioning is a key issue for road vehicles in navigation, safety, and autonomous driving applications. While global positioning system (GPS) is widely accepted, it is still a challenge to achieve lane-level positioning. In this work, we consider the fusion of multi-sensory data using particle filter (PF), which is flexible in integrating different information in complex outdoor environments. We focus on three types of popular sensors: controller area network (CAN bus), GPS, and roadside camera. We propose a PF model that can adopt these types of sensory inputs for vehicle positioning. We show that in scenarios where vision sensory inputs are available, lane-level precision can be achieved. When there is no vision coverage, seamless localisation with reasonable precision can still be supported by GPS. Field trial results are presented to validate our model.