With an increasing number of elders living alone, care-giving from a distance becomes a compelling need, particularly for safety. Real-time monitoring and action recognition are essential to raise an alert timely when abnormal behaviors or unusual activities occur. While wearable sensors are widely recognized as a promising solution, highly depending on user's ability and willingness makes them inefficient. In contrast, video streams collected through non-contact optical cameras provide richer information and release the burden on elders. In this paper, leveraging the Independently-Recurrent neural Network (IndRNN) we propose a novel Real-time Elderly Monitoring for senior Safety (REMS) based on lightweight human action recognition (HAR) technology. Using captured skeleton images, the REMS scheme is able to recognize abnormal behaviors or actions and preserve the user's privacy. To achieve high accuracy, the HAR module is trained and fine-tuned using multiple databases. An extensive experimental study verified that REMS system performs action recognition accurately and timely. REMS meets the design goals as a privacy-preserving elderly safety monitoring system and possesses the potential to be adopted in various smart monitoring systems.
翻译:随着越来越多的老年人独自生活,从远处护理成为迫切需要,特别是安全。实时监测和行动识别对于在出现异常行为或异常活动时及时提高警觉至关重要。尽管磨损传感器被广泛视为有希望的解决方案,但高度取决于用户的能力和意愿,使其效率低下。相反,通过非接触光学照相机收集的视频流提供了更丰富的信息,并释放了老年人的负担。在本文件中,利用独立实时神经网络(IndRNN),我们基于轻量级人类行动识别(HAR)技术,提出了一个新的高级安全实时老年人监测(REMS ) 。使用捕获的骨骼图像,REMS 计划能够识别异常行为或行动并保护用户隐私。为了实现高精度,利用多个数据库对HAR模块进行了培训和微调。一项广泛的实验研究证实,REMS 系统准确和及时地进行行动识别。REMS 符合设计目标,作为保护隐私的老年人安全监测系统,并拥有各种智能监测系统采用的潜力。