基于非穿戴式传感器的多用户室内活动识别研究
发布时间：2018-12-21 20:06
【摘要】：随着物联网时代的到来,基于传感器的活动识别研究成为热点,其中利用可穿戴式传感器的活动识别在移动计算领域的研究较多,而基于非穿戴式传感器的活动识别研究更适合于智能环境应用中,这也是进一步完善智能家居设计乃至实现的关键点。而且,考虑到使用方便性和设备的无打扰性,基于非穿戴式传感器的活动识别也具有其自身的优势,到目前为止,基于非穿戴式传感器的活动识别仍然具有很大的挑战性。因此,本文研究基于非穿戴式传感器的多人室内活动识别,其中,非穿戴式传感器包括环境传感器和物体传感器,室内活动主要包括一些常见的日常活动,这些活动可能会对环境或是相应物体产生影响,包括:在工位学习、打印、烧水、煮咖啡、谈话、读书、写字、用电脑、喝水。故本文的研究根据活动产生影响的不同可分为两个部分,基于环境传感器的活动识别研究和基于物体传感器的活动识别研究。在基于环境传感器的活动识别研究,本文利用环境传感器,包括电表、温、湿度传感器、红外人体感应传感器、避碍传感器、光强传感器和录音笔搭建模拟实验平台采集活动数据,利用采集到的数据,本文针对数据形态的不同及多人并发活动的特点,给出两种算法和模型进行活动识别,分别为基于动态时间扭曲(DTW)的K近邻(KNN)模型和多标签算法模型的基于支持向量机(SVM)的多分类器模型,最后证明,多标签算法模型的基于SVM的多分类器模型表现更好,最终得到了89%的识别准确率。在基于物体传感器的活动识别研究中,本文主要应用的传感器设备为射频识别(RFID)标签和RFID读写器,其中标签有两种,贴附于各个物体表面,RFID读写器用来读取各个标签的接收信号强度(RSS)数据。本文设计模拟平台采集RSS数据。利用采集到的活动数据,在此部分,考虑到RSS数据的特点,本文提出数据映射方法,再利用CNN-LSTM模型对数据进行自动的数据空间特征和时间特征提取,并最终在活动识别上达到了95%的识别准确率。在本文的最后,分别对基于环境传感器和物体传感器的活动识别方法做了完整的测试和对比分析,从模拟环境搭建策略到数据采集过程,再到活动识别算法模型,对基于非穿戴式传感器的活动识别给出总体的识别和设计方案。综上所述,本文给出基于非穿戴式传感器的多人室内活动识别方法,方法产生了可接受并优于以往相似研究的识别结果,并具有很好的可扩展性和可移植性。
[Abstract]:With the advent of the Internet of things era, the research of sensor-based activity recognition has become a hot topic, in which wearable sensors are widely used in the field of mobile computing. The research of activity recognition based on non-wearable sensors is more suitable for intelligent environment application, which is also the key point to further improve the design and implementation of smart home. Moreover, considering the convenience of use and the non-disturbance of the device, the activity recognition based on the non-wearable sensor has its own advantages. So far, the activity recognition based on the non-wearable sensor is still very challenging. Therefore, this paper studies the identification of multi-person indoor activities based on non-wearable sensors. Among them, non-wearable sensors include environmental sensors and object sensors, and indoor activities mainly include some common daily activities. These activities may have an impact on the environment or related objects, including: studying, printing, boiling water, making coffee, talking, reading, writing, using computers, drinking water. Therefore, the research of this paper can be divided into two parts according to the different influence of activity, namely, the research of activity recognition based on environmental sensor and the study of activity recognition based on object sensor. In the research of activity recognition based on environmental sensors, this paper uses environmental sensors, including ammeter, temperature, humidity sensor, infrared sensor to avoid obstacles. Light intensity sensor and recording pen are used to build a simulation experiment platform to collect activity data. According to the different data forms and the characteristics of multiple concurrent activities, two algorithms and models are presented to identify the activity. It is a K-nearest neighbor (KNN) model based on dynamic time-distorted (DTW) and a multi-classifier model based on support vector machine (SVM) algorithm model. Finally, it is proved that, The multi-classifier model based on SVM is better than the multi-label algorithm model, and the recognition accuracy is 89%. In the research of object sensor based activity identification, the main sensor devices used in this paper are RFID (RFID) tag and RFID reader. There are two kinds of tags attached to the surface of each object. The RFID reader is used to read the received signal strength (RSS) data for each tag. This paper designs a simulation platform to collect RSS data. In this part, considering the characteristics of RSS data, this paper proposes a method of data mapping, and then uses the CNN-LSTM model to extract automatically the spatial and temporal features of the data. Finally, 95% recognition accuracy is achieved in activity recognition. At the end of this paper, the methods of activity recognition based on environment sensor and object sensor are tested and compared, from the strategy of simulation environment to the process of data acquisition, and then to the algorithm model of activity recognition. The overall recognition and design scheme of the motion recognition based on non-wearable sensor is given. To sum up, this paper presents a multi-person indoor activity recognition method based on non-wearable sensors, which produces acceptable and better recognition results than previous similar studies, and has good scalability and portability.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP212.9
[Abstract]:With the advent of the Internet of things era, the research of sensor-based activity recognition has become a hot topic, in which wearable sensors are widely used in the field of mobile computing. The research of activity recognition based on non-wearable sensors is more suitable for intelligent environment application, which is also the key point to further improve the design and implementation of smart home. Moreover, considering the convenience of use and the non-disturbance of the device, the activity recognition based on the non-wearable sensor has its own advantages. So far, the activity recognition based on the non-wearable sensor is still very challenging. Therefore, this paper studies the identification of multi-person indoor activities based on non-wearable sensors. Among them, non-wearable sensors include environmental sensors and object sensors, and indoor activities mainly include some common daily activities. These activities may have an impact on the environment or related objects, including: studying, printing, boiling water, making coffee, talking, reading, writing, using computers, drinking water. Therefore, the research of this paper can be divided into two parts according to the different influence of activity, namely, the research of activity recognition based on environmental sensor and the study of activity recognition based on object sensor. In the research of activity recognition based on environmental sensors, this paper uses environmental sensors, including ammeter, temperature, humidity sensor, infrared sensor to avoid obstacles. Light intensity sensor and recording pen are used to build a simulation experiment platform to collect activity data. According to the different data forms and the characteristics of multiple concurrent activities, two algorithms and models are presented to identify the activity. It is a K-nearest neighbor (KNN) model based on dynamic time-distorted (DTW) and a multi-classifier model based on support vector machine (SVM) algorithm model. Finally, it is proved that, The multi-classifier model based on SVM is better than the multi-label algorithm model, and the recognition accuracy is 89%. In the research of object sensor based activity identification, the main sensor devices used in this paper are RFID (RFID) tag and RFID reader. There are two kinds of tags attached to the surface of each object. The RFID reader is used to read the received signal strength (RSS) data for each tag. This paper designs a simulation platform to collect RSS data. In this part, considering the characteristics of RSS data, this paper proposes a method of data mapping, and then uses the CNN-LSTM model to extract automatically the spatial and temporal features of the data. Finally, 95% recognition accuracy is achieved in activity recognition. At the end of this paper, the methods of activity recognition based on environment sensor and object sensor are tested and compared, from the strategy of simulation environment to the process of data acquisition, and then to the algorithm model of activity recognition. The overall recognition and design scheme of the motion recognition based on non-wearable sensor is given. To sum up, this paper presents a multi-person indoor activity recognition method based on non-wearable sensors, which produces acceptable and better recognition results than previous similar studies, and has good scalability and portability.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP212.9
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