摘要
为使个人运动管理系统更好地感知用户行为,给予科学的运动指导,提出一种基于通用模型迁移的自适应行为识别方法。该方法无需对个体数据进行标定,通过将群体行为的共性知识迁移到个体行为,使通用识别模型可以随着个体行为样本的增多,自适应地调整共性知识,从而形成针对特定个体的个性化行为识别模型。实验结果表明,个性化模型的平均识别精度可以从67.31%提高到83.54%。
To help personal sports management system accurately detect users＇ activity and give them scientific sports instruction,a common model transferred adaptive activity recognition method is presented.With this method,the common knowledge of many persons can be transferred to a new one and with the new one＇s data increasing,the model can be automatically adapted to fit the new user.The method proposed is evaluated on real-world dataset to demonstrate its performance.
出处
《计算机工程》
CAS
CSCD
2013年第1期213-216,224,共5页
Computer Engineering
基金
国家自然科学基金资助项目(41171341
61070110)
北京市自然科学基金资助项目(4112056)
关键词
运动管理
普适计算
行为识别
迁移学习
机器学习
决策树
exercise management
pervasive computing
activity recognition
transfer learning
machine learning
Decision Tree（DT）
