Human activities with videos, inertial units and ambient sensors

Worldwide demographic projections point to a progressively older population. This fact has fostered research on Ambient Assisted Living, which includes developments on smart homes and social robots. To endow such environments with truly autonomous behaviours, algorithms must extract semantically meaningful information from whichever sensor data is available. Human activity recognition is one of the most active fields of research within this context. Proposed approaches vary according to the input modality and the environments considered. Different from others, this paper addresses the problem of recognising heterogeneous activities of daily living centred in home environments considering simultaneously data from videos, wearable IMUs and ambient sensors. For this, two contributions are presented. The first is the creation of the Heriot-Watt University/University of Sao Paulo (HWU-USP) activities dataset, which was recorded at the Robotic Assisted Living Testbed at Heriot-Watt University...全球人口预测显示，人口老龄化趋势日益加剧。这一趋势推动了环境辅助生活（Ambient Assisted Living, AAL）领域的研究，该领域涵盖智能家居与社交机器人的相关研发。为使此类环境具备真正的自主行为能力，算法需从各类可用传感器数据中提取具备语义价值的信息。在此背景下，人类活动识别（Human Activity Recognition, HAR）是当前最活跃的研究领域之一。现有研究方案根据输入模态与所考虑的应用环境的不同而有所差异。与其他研究不同，本文聚焦于以家庭环境为中心的异构日常生活活动识别问题，同时融合了视频数据、可穿戴惯性测量单元（Inertial Measurement Unit, IMU）数据与环境传感器数据。为此，本文提出两项研究贡献。第一项贡献为构建赫瑞-瓦特大学/圣保罗大学（Heriot-Watt University/University of Sao Paulo, HWU-USP）活动数据集，该数据集采集自赫瑞-瓦特大学的机器人辅助生活测试平台……