论文标题
移动健康中的活动识别的转移学习
Transfer Learning for Activity Recognition in Mobile Health
论文作者
论文摘要
虽然惯性传感器的活动识别具有移动健康的潜力，但传感平台和用户运动模式的差异会导致性能下降。为了应对这些挑战，我们建议转移学习框架，转移，以识别传感器的活动识别。 TransFall的设计包含一个两层数据转换，标签估计层和一个模型生成层，以识别新方案的活动。我们通过分析和经验来验证偏移。
While activity recognition from inertial sensors holds potential for mobile health, differences in sensing platforms and user movement patterns cause performance degradation. Aiming to address these challenges, we propose a transfer learning framework, TransFall, for sensor-based activity recognition. TransFall's design contains a two-tier data transformation, a label estimation layer, and a model generation layer to recognize activities for the new scenario. We validate TransFall analytically and empirically.
