인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
This paper presents a personalized force-pattern input interface that enables multiple commands from a single touch location. Instead of mapping force to discrete levels or a continuous value, we capture the full force trajectory during a touch and treat it as a time-series “force pattern.” From each pattern we compute 13 statistical features (e.g., mean, standard deviation) and train machine-learning classifiers. Data were collected with an FSR sensor: four participants performed four self-defined patterns repeatedly, producing 640 samples. In a train–test split over the full dataset, the method achieved 0.9654 average accuracy. To account for distribution changes across collection dates, we additionally performed five-fold cross-validation within each date; Random Forest showed the most stable performance. We also found that feature scaling materially affects accuracy: Standard, MinMax, MaxAbs, and Robust scaling generally improved results, whereas vector normalization performed worse. When reducing the number of training samples per pattern, accuracy decreased overall, but some models remained robust; notably, Logistic Regression and SVM exceeded 0.8 accuracy even in low-data settings. Finally, under a broader condition that jointly classifies user and action, certain model–scaler combinations reached roughly 0.9 accuracy, suggesting applicability beyond strictly personalized use. These findings support practical multi-command touch interaction with modest calibration.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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