Elderly care is a significant livelihood project in the increasingly serious aging society. Nowadays, the wider application of the Internet of Things (IoT) technology on assistant means has making an importance contribution to elderly care in institutions and at home. On the broader data foundation collected by IoT devices, Human Activity Recognition (HAR) with its high demand in various elderly care applications also has grabbed considerable research attentions. However, the elderly’s sensitive data transmitted over the wireless communication channel need to address many security concerns, and the accuracy of activity recognition is susceptible to many influencing factors, especially, the employed feature selection method and classifier. In this paper, to ensure the confidentiality of the elderly’s sensitive data, a secure and efficient group-based key establishment and authentication framework is first proposed. Subsequently, activity recognition is investigated within the security data sensitive framework, where a feature reorganization based feature selection method is proposed with the demonstrated relationship between the recognition accuracy and strongly correlative features, and three classifiers are investigated based on classical ones. By contrast, an improved convolutional neural network (CNN) enabled classifier is singled out to conduct the activity recognition with the feature reorganization method. Finally, security proofs show that the proposed security framework can ensure data confidentiality and be resilient to possible attacks, and experimental analyses show investigations in activity recognition can achieve more cost-effective results.

在日益严重的老龄化社会中，老年护理是一项重要的民生项目。如今，物联网（IoT）技术在辅助手段上的广泛应用为机构和家庭中的老年人护理做出了重要贡献。在物联网设备收集的更广泛的数据基础上，对各种老年人护理应用有很高要求的人类活动识别（HAR）也吸引了相当多的研究关注。然而，通过无线通信信道传输的老年人的敏感数据需要解决许多安全问题，并且活动识别的准确性容易受到许多影响因素的影响，特别是所采用的特征选择方法和分类器。为了确保老年人敏感数据的机密性，首先提出了一种安全高效的基于组的密钥建立和认证框架。随后，在安全数据敏感框架内研究了活动识别，提出了一种基于特征重组的特征选择方法，并证明了识别精度与强相关特征之间的关系，并在经典分类器的基础上研究了三个分类器。相比之下，改进的卷积神经网络（CNN）支持的分类器被选出，以利用特征重组方法进行活动识别。最后，安全性证明表明，所提出的安全性框架可以确保数据的机密性，并能够抵抗可能的攻击，而实验分析表明，对活动识别的研究可以取得更具成本效益的结果。