Browsing by Author "Chi, Hong-Chuan"

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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.
Smart Shopping Carts Based on Mobile Computing and Deep Learning Cloud Services
(2020-01-01)
Sarwart, Muhammad Atif
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Daraghmi, Yousef-Awwad
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Liu, Kuan-Wen
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Chi, Hong-Chuan
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Ik, Tsi-Ui
Self-checkout systems enable retailers to reduce costs and customers to process their purchases quickly without waiting in queues. However, existing self-checkout systems suffer from design problems as they require large hardware consisting of a camera, sensors, RFID and other IoT technologies which increases the cost of such systems. Therefore, we propose a smart shopping cart with self-checkout, called iCart, to improve customer's experience at retail stores by enabling just walk out checkout and overcome the aforementioned problems. iCart is based on mobile cloud computing and deep learning cloud services. In iCart, a checkout event video is captured and sent to the cloud server for classification and segmentation where an item is identified and added to the shopping list. The Linux based cloud server contained the yolov2 deep learning network. iCart is a lightweight system of low cost solution which is suitable for the small-scale retail stores. The system is evaluated using real-world checkout video, and the accuracy of the shopping event detection and item recognition is about 97%.