K-mer-Based Human Gesture Recognition (KHGR) Using Curved Piezoelectric Sensor

Sathishkumar Subburaj, Chih Ho Yeh, Brijesh Patel, Tsung Han Huang, Wei Song Hung, Ching Yuan Chang, Yu Wei Wu, Po Ting Lin

研究成果: 雜誌貢獻文章同行評審


Recently, human activity recognition (HAR) techniques have made remarkable developments in the field of machine learning. In this paper, we classify human gestures using data collected from a curved piezoelectric sensor, including elbow movement, wrist turning, wrist bending, coughing, and neck bending. The classification process relies on data collected from a sensor. Machine learning algorithms enabled with K-mer are developed and optimized to perform human gesture recognition (HGR) from the acquired data to achieve the best results. Three machine learning algorithms, namely support vector machine (SVM), random forest (RF), and k-nearest neighbor (k-NN), are performed and analyzed with K-mer. The input parameters such as subsequence length (K), number of cuts, penalty parameter (C), number of trees (n_estimators), maximum depth of the tree (max_depth), and nearest neighbors (k) for the three machine learning algorithms are modified and analyzed for classification accuracy. The proposed model was evaluated using its accuracy percentage, recall score, precision score, and F-score value. We achieve promising results with accuracy of 94.11 ± 0.3%, 97.18 ± 0.4%, and 96.90 ± 0.5% for SVM, RF, and k-NN, respectively. The execution time to run the program with optimal parameters is 19.395 ± 1 s, 5.941 ± 1 s, and 3.832 ± 1 s for SVM, RF, and k-NN, respectively.
期刊Electronics (Switzerland)
出版狀態已發佈 - 1月 2023

ASJC Scopus subject areas

  • 控制與系統工程
  • 訊號處理
  • 硬體和架構
  • 電腦網路與通信
  • 電氣與電子工程


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