SoHAM: A Sound-Based Human Activity Monitoring Framework for Home Service Robots
SoHAM: A Sound-Based Human Activity Monitoring Framework for Home Service Robots
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DOI：
10.1109/tase.2021.3081406
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发表时间：
2021-05-28
影响因子：
5.6
通讯作者：
Sheng, Weihua
中科院分区：
文献类型：
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作者：
Do, Ha Manh;Welch, Karla Conn;Sheng, Weihua
监控日常活动对于家庭服务机器人照顾独居老人来说至关重要。在本文中，我们通过识别家庭环境中的声音事件提出了一种基于声音的人类活动监测（SoHAM）框架。首先，开发了上下文感知声音事件识别（CoSER）方法，该方法使用上下文信息来消除声音事件的歧义。通过融合家中部署的分布式被动红外 (PIR) 传感器的数据来估计声音事件的位置背景。两级动态贝叶斯网络（DBN）用于对上下文和声音事件之间的时内和时间约束进行建模。其次，开发了基于动态滑动时间窗口的人类动作识别（DTW-HaR）来估计活动声音事件片段及其标签和持续时间，然后推断动作及其持续时间。最后，提出了条件随机场（CRF）模型来根据识别的动作、位置和时间来预测人类活动。我们在机器人集成智能家居（RiSH）测试台中进行了实验，以评估所提出的框架。获得的结果显示了 CoSER、动作识别和人类活动监控的有效性和准确性。
Monitoring daily activities is essential for home service robots to take care of the older adults who live alone in their homes. In this article, we proposed a sound-based human activity monitoring (SoHAM) framework by recognizing sound events in a home environment. First, the method of context-aware sound event recognition (CoSER) is developed, which uses contextual information to disambiguate sound events. The locational context of sound events is estimated by fusing the data from the distributed passive infrared (PIR) sensors deployed in the home. A two-level dynamic Bayesian network (DBN) is used to model the intratemporal and intertemporal constraints between the context and the sound events. Second, dynamic sliding time window-based human action recognition (DTW-HaR) is developed to estimate active sound event segments with their labels and durations, then infer actions and their durations. Finally, a conditional random field (CRF) model is proposed to predict human activities based on the recognized action, location, and time. We conducted experiments in our robot-integrated smart home (RiSH) testbed to evaluate the proposed framework. The obtained results show the effectiveness and accuracy of CoSER, action recognition, and human activity monitoring.