The research on human activity recognition has provided novel solutions to many applications like healthcare, sports, and user profiling. Considering the complex nature of human activities, it is still challenging even after effective and efficient sensors are available. The existing works on human activity recognition using smartphone sensors focus on recognizing basic human activities like sitting, sleeping, standing, stair up and down and running. However, more than these basic activities is needed to analyze human behavioural pattern. The proposed framework recognizes basic human activities using deep learning models. Also, ambient sensors like PIR, pressure sensors, and smartphone-based sensors like accelerometers and gyroscopes are combined to make it hybrid-sensor-based human activity recognition. The hybrid approach helped derive more activities than the basic ones, which also helped derive human activity patterns or user profiling. User profiling provides sufficient information to identify daily living activity patterns and predict whether any anomaly exists. The framework provides the base for applications such as elderly monitoring when they are alone at home. The GRU model's accuracy of 95\% is observed to recognize the basic activities. Finally, Human activity patterns over time are recognized based on the duration and frequency of the activities. It is observed that human activity pattern, like, morning walking duration, varies depending on the day of the week.

翻译：人类活动识别研究为医疗健康、体育及用户画像等应用提供了新型解决方案。考虑到人类活动的复杂特性，即便已具备高效传感器，该领域仍面临挑战。现有基于智能手机传感器的人类活动识别研究主要关注坐、卧、站立、上下楼梯及跑步等基本活动。然而，仅凭这些基础活动不足以分析人类行为模式。本文提出的框架采用深度学习模型识别基本活动，同时将PIR红外传感器、压力传感器等环境传感器与加速度计、陀螺仪等智能手机传感器结合，构建混合传感器的人类活动识别系统。这种混合方法有助于推导出超越基本活动的更多动作类型，进而实现人类活动模式识别与用户画像。用户画像可提供充足信息用于识别日常生活活动模式并预测是否存在异常行为。该框架为独居老人监护等应用奠定基础。经观测，GRU模型识别基本活动的准确率达95%。最终，基于活动持续时长与发生频率，时间维度的人类活动模式得以识别。研究发现，诸如晨间散步时长等人类活动模式会随星期变化而呈现差异。