用实时动作识别预警老人跌倒、踉跄和胸痛，提升照护安全。
A Real-Time Human Action Recognition Model for Assisted Living
- 基于TimeSformer模型融合多帧视频特征，实现高精度实时动作识别。
- 在NTU RGB+D 60数据集上达95.33%宏F1分数，推理吞吐量领先。
- 适用于养老机构智能监控系统，助力慢性病人群体安全照护。
在辅助生活环境中保障老年人及弱势群体的安全与健康是重要课题。计算机视觉通过视频监控提供预测健康风险的新途径，其中人体动作识别（HAR）技术尤为关键。然而，实现实时、高性能且高效的动作预测仍具挑战。本研究提出一种结合深度学习模型与实时视频预警系统的实时人体动作识别模型，用于预测居住者跌倒、踉跄和胸痛等异常行为。从NTU RGB+D 60数据集中选取六千个RGB视频样本，构建包含四类的动作数据集：跌倒、踉跄、胸痛和正常（涵盖40种日常活动）。采用迁移学习在GPU服务器上训练四种先进HAR模型：UniFormerV2、TimeSformer、I3D与SlowFast。本文基于类别级与宏平均性能指标、推理效率、模型复杂度及计算成本对比分析四类模型表现。结果显示，TimeSformer在宏F1分数（95.33%）、召回率（95.49%）与精确率（95.19%）方面最优，并具备显著更高的推理吞吐量。研究成果为提升老年人及慢性病患者在辅助生活环境中的安全与健康水平提供了支持，推动可持续照护、智慧社区与产业创新。
原文摘要 · Abstract (English)
Ensuring the safety and well-being of elderly and vulnerable populations in assisted living environments is a critical concern. Computer vision presents an innovative and powerful approach to predicting health risks through video monitoring, employing human action recognition (HAR) technology. However, real-time prediction of human actions with high performance and efficiency is a challenge. This research proposes a real-time human action recognition model that combines a deep learning model and a live video prediction and alert system, in order to predict falls, staggering and chest pain for residents in assisted living. Six thousand RGB video samples from the NTU RGB+D 60 dataset were selected to create a dataset with four classes: Falling, Staggering, Chest Pain, and Normal, with the Normal class comprising 40 daily activities. Transfer learning technique was applied to train four state-of-the-art HAR models on a GPU server, namely, UniFormerV2, TimeSformer, I3D, and SlowFast. Results of the four models are presented in this paper based on class-wise and macro performance metrics, inference efficiency, model complexity and computational costs. TimeSformer is proposed for developing the real-time human action recognition model, leveraging its leading macro F1 score (95.33%), recall (95.49%), and precision (95.19%) along with significantly higher inference throughput compared to the others. This research provides insights to enhance safety and health of the elderly and people with chronic illnesses in assisted living environments, fostering sustainable care, smarter communities and industry innovation.
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